Mtech_Evaluation of Landuse Scenarios_Srinivas Ganji_TSE_IITBWM

95
i Evaluation of Land Use Scenarios using a Travel Demand Model for Mumbai Metropolitan Region M.Tech Dissertation Submitted in partial fulfillment of the requirements for the degree of Master of Technology Submitted by Srinivas G (Roll No. 09304020) Under the supervision of Prof. K. V. Krishna Rao Transportation Systems Engineering Department of Civil Engineering Indian Institute of Technology Bombay May, 2011 of Land Use Scenarios using a Travel Demand Model for Mumbai Metropolitan Region, Srinivas G, II

Transcript of Mtech_Evaluation of Landuse Scenarios_Srinivas Ganji_TSE_IITBWM

i

Evaluation of Land Use Scenarios using a Travel Demand

Model for Mumbai Metropolitan Region

M.Tech Dissertation

Submitted in partial fulfillment of the requirements for the degree of

Master of Technology

Submitted by

Srinivas G

(Roll No. 09304020)

Under the supervision of

Prof. K. V. Krishna Rao

Transportation Systems Engineering

Department of Civil Engineering

Indian Institute of Technology Bombay

May, 2011

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y

iii

DECLARATION

I declare that this written submission represents my ideas in my own words and where others

ideas or words have been included; I have adequately cited and referenced the original

sources. I also declare that I have adhered to all principles of academic honesty and integrity

and have not misrepresented or fabricated or falsified any idea/data/fact/source in my

submission. I understand that any violation of the above will be cause for disciplinary action

by the Institute and can also evoke penal action from the sources which have thus not been

properly cited or from whom proper permission has not been taken when needed.

_________________________________

(Signature)

Srinivas G

________________________________

(Name of the student)

09304020

_________________________________

(Roll No.)

Date: __________

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y

iv

INDIAN INSTITUTE OF TECHNOLOGY-BOMBAY, INDIA

CERTIFICATE OF COURSE WORK

This is to certify that Srinivas G (Roll No: 09304020) was admitted to the candidacy of the

M.Tech Degree on 25th

May, 2011, after successfully completing all the courses required for

the M.Tech Degree Programme. The details of the course done are given below:

Sl. No. Course No. Course Name

Credits

1. CE-605 Applied Statistics 6.0

2. IE-601 Optimization Techniques 8.0

3. CE-740 Traffic Engineering 8.0

4. CE-751 Urban Transportation Systems Planning 8.0

5. CE-753 Traffic Design and Studio 4.0

6. CE-694 Credit Seminar 4.0

7. CE-742 Pavement Systems Engineering 8.0

8. CE-780 Behavioral Travel Modelling 6.0

9. CE-754 Economic Evaluation and Analysis of

Transportation Projects 6.0

10. HS-699 Communication and Presentation Skills 4.0

11. HS-618 Introduction to Indian Astronomy 6.0

12. CE-609 Transportation Infrastructures Systems 6.0

I.I.T. Bombay

Dated: Dy. Registrar (Academic)

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y

v

Abstract

Mumbai has been experiencing continuous growth and change. The total number of trips is

drastically increasing due to heavy growth in population and employment. The current bus

transit system and sub-urban railway network in Mumbai Metropolitan Region (MMR) is

already overloaded in Mumbai. In congested localities the average speed of buses are as low

as 6 KMPH. With increase in private vehicle (PV) ownership (both cars and two wheelers)

the situation is going to be much worse unless the existing public transportation network is

augmented by modern transit facilities like the Metro Rail, Mono Rail and BRTS, etc. Hence

the MMRDA proposed the different public transport systems which will be completed by the

horizon year 2031. In the developing countries like India the land use planning cannot be

done by integrating it with transportation systems accessibility. Hence it is not practicable to

evaluate the proposed transportation system w.r.t. different land use scenarios using integrated

land use transport models. Hence the sixteen possible land use scenarios were developed by

MMRDA exogenously, which are evaluated w.r.t. proposed transportation system

performance perspective.

The aim of the present study is to evaluate land use scenarios with respect to proposed

transportation system for the horizon year 2031 using a travel demand model. To achieve the

aim, the travel demand model is to be developed for entire MMR by considering the all the

transportation systems which are proposed. Towards the travel demand modeling the region

has been divided into 1037 total number of zones. Then highway network has been developed

using ArcGIS, TransCAD and CUBE Voyager. The public transportation system routes are

coded in GIS based CUBE Voyager software (Script based travel demand modeling software)

for all the transport service options. Then the four steps of travel demand modeling are

implemented using the in the CUBE Voyager software. The set of possible and quantifiable

indicators are selected for the evaluation of urban transportation system performance and they

are assigned with relative scores through a rating survey. The working model is used to test

the different land use scenarios with respect to the selected indicators in a Multi Criteria

Decision Making (MCDM) technique to come up with the best scenario.

Key Words: MMR, travel demand model, CUBE Voyager, land use scenarios, evaluation,

transportation system, MCDM

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y

vi

Table of Contents

DISSERTATION APPROVAL SHEET ............................................................................ ii

DECLARATION ............................................................................................................... iii

Abstract ............................................................................................................................... v

Table of Contents ............................................................................................................... vi

List of Figures .................................................................................................................... ix

List of Tables ...................................................................................................................... xi

Chapter 1 ............................................................................................................................. 1

Introduction ........................................................................................................................ 1

1.1 General ...........................................................................................................................................1

1.2 Problem statement ..........................................................................................................................1

1.3 Objectives and Scope of the Study .................................................................................................2

1.4 Organization of the Report .............................................................................................................3

Chapter 2 ............................................................................................................................. 5

Literature Review ............................................................................................................... 5

2.1 General ...........................................................................................................................................5

2.2 Metropolitan Regional Travel Demand Modeling .........................................................................5

2.2.1 Baltimore Regional Travel Demand Model ............................................................................5

2.2.2 San Francisco Metropolitan area Travel Demand Model ........................................................7

2.3 GIS in Travel Demand Modeling ...................................................................................................8

2.4 Land use and Transport Interaction ................................................................................................9

2.4.1 Structure of Land use Transportation Interaction Models .....................................................11

2.4.2 Uncertainty in Integrated Land use Transport Models ..........................................................13

2.5 Evaluation indicators for the land use scenarios ..........................................................................15

2.5.1 Guide lines for selecting the indicators .................................................................................16

2.5.2 Multiple Criteria Decision Making in Transportation Planning ............................................21

2.5.3 Inferences from the literature ................................................................................................22

2.6 Summary .....................................................................................................................................22

Chapter 3 ........................................................................................................................... 23

Study Area and Planning Variables ................................................................................. 23

3.1 General .........................................................................................................................................23

3.2 Study Area ....................................................................................................................................23

3.3 Zoning System .............................................................................................................................24

3.4 Planning Variables .......................................................................................................................25

3.4.1 Road Network and Transport System ...................................................................................28

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y

vii

3.4.2 Alternate Growth Scenarios ..................................................................................................28

3.5 Summary ......................................................................................................................................29

Chapter 4 ........................................................................................................................... 30

Methodology ...................................................................................................................... 30

4.1 General .........................................................................................................................................30

4.2 Development of highway and public transit network ..................................................................30

4.2.1 Highway network development.............................................................................................30

4.2.2 Public Transport Network Development ...............................................................................31

4.3 Updating Base year Travel pattern from the previous study ........................................................31

4.4 Horizon year Travel Demand Forecasts .......................................................................................33

4.5 Development of indices for the evaluation of different growth scenarios ...................................33

4.6 Evaluation of Land use Scenarios using Travel Demand Model .................................................34

4.6.1 Formulation of MCDM approach for evaluation ..................................................................35

4.7 Summary ......................................................................................................................................37

Chapter 5 ........................................................................................................................... 39

Travel Demand Model Development................................................................................ 39

5.1 General .........................................................................................................................................39

5.2 Updating Base year Travel pattern from the previous study ........................................................39

5.3 Network Development .................................................................................................................39

5.3.1 Highway Network Development ...........................................................................................39

5.3.2 Available Data Set .................................................................................................................40

5.3.3 Creation of GIS Database ......................................................................................................41

5.3.4 Building the Highway Network ............................................................................................42

5.4 Public Transport Network Development ......................................................................................44

5.4.1 Bus Network ..........................................................................................................................45

5.4.2 Sub urban Rail Network ........................................................................................................46

5.4.3 Metro Rail Network ..............................................................................................................47

5.4.4 Mono Rail Network ...............................................................................................................48

5.4.5 BRT (Bus Rapid Transit) Network .......................................................................................48

5.4.6 Fare Tables and Wait curves .................................................................................................48

5.4.7 Creation of Access/Egress and Transfer links .......................................................................49

5.5 Generation of Initial Highway and Public Transport Skims ........................................................50

5.6 Trip Generation ............................................................................................................................51

5.7 Trip Distribution Models ..............................................................................................................54

5.8 Modal Split Models ......................................................................................................................55

5.9 Highway and Public Transport Assignment .................................................................................57

5.9.1 Public Transport Assignment ................................................................................................57

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y

viii

5.9.2 Highway Assignment ............................................................................................................58

5.10 Salient features of the present model .........................................................................................59

5.11 Summary ....................................................................................................................................60

Chapter 6 ........................................................................................................................... 61

Evaluation of Urban Transportation System’s Performance using MCDM approach . 61

6.1 General .........................................................................................................................................61

6.2 Selected Scenarios ........................................................................................................................61

6.3 Calculation of Selected indicators from the Travel Demand Model ............................................62

6.3.1 Accessibility to the public transport stops .............................................................................62

6.3.2 Total Public transport user cost in generalized time units .....................................................63

6.3.3 Traffic Congestion .................................................................................................................64

6.3.4 Transportation safety .............................................................................................................65

6.3. 5 Mode share of the public transport .......................................................................................65

6.3.6 Average trip length and vehicle kilometers ...........................................................................69

6.3.7 Cost of the proposed transportation infrastructure for the Horizon year 2031 ......................70

6.4 Analysis of the Rating survey ......................................................................................................70

6.4.1 Inferences from survey ..........................................................................................................71

6.4.2 Calculation of Relative Transportation performance Index ..................................................72

Chapter 7 ........................................................................................................................... 75

Summary and Conclusions ............................................................................................... 75

7.1 Summary of work .........................................................................................................................75

7.2 Conclusions ..................................................................................................................................76

7.3 Limitations ...................................................................................................................................77

7.4 Future Scope of the work .............................................................................................................78

References ......................................................................................................................... 79

Acknowledgments ............................................................................................................. 81

Appendix ........................................................................................................................... 82

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y

ix

List of Figures

Figure No. Description Page No.

Figure 2.1 The Summary of Baltimore Regional Travel demand Model 6

Figure 2.2 Summary of San Francisco Travel demand model 8

Figure 2.3 Architecture of GIS based decision supportive system 9

Figure 2.4 Land use transportation feedback cycle 10

Figure 2.5 The general structure of integrated land use and transport model 12

Figure 2.6 Typical impacts over time of uncertainty in population and employment

(exogenous production) forecasts on model outputs 14

Figure 2.7 Typical impacts over time of uncertainty in commercial trip generation

rates on model outputs 15

Figure 2.8 The role of indicators in a transportation planning process 20

Figure 2.9 sustainability indicator prism 20

Figure 3.1 Sub Regions of MMR 24

Figure 3.2 Forecasted Population of MMR from 1971 to 2031 26

Figure 4.1 Methodology for updating base year travel pattern 32

Figure 4.2 Formulation of procedure for Evaluation of land use scenarios w.r.t.

transportation system performance 35

Figure 4.3 Methodology for Evaluation of Land use scenarios for the horizon years

using Travel Demand Model 38

Figure 5.1 Local and arterials links 41

Figure 5.2 freeway links 41

Figure 5.3 suburban rail 41

Figure 5.4 The geo referenced shape file of total MMR network developed in

TransCAD 42

Figure 5.5 Process for the development of network for MMR on CUBE Voyager

Platform 43

Figure 5.6 Developed highway network for the horizon year 2031 44

Figure 5.7 Public Transport network development in cube voyager 45

Figure 5.8 The PT network coded with all the routes 46

Figure 5.9 Mumbai Local Train information pocket guide 47

Figure 5.10 Metro Rail network for 2031 year 48

Figure 5.11 Attributes of a typical Public transport route in CUBE 49

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y

x

Figure 5.12 Generation of initial highway and PT skims in CUBE voyager platform 50

Figure 5.13 Implementation of Trip Generation, Distribution and Modal split step

in Voyager 53

Figure 5.14 Summary of Mode Choice Model Structures: Without Walk 55

Figure 5.15 The complete flow structure of the Travel demand model for MMR

in Voyager 59

Figure 6.1 Overview of Evaluation of Alternative Development Options 62

Figure 6.2 Total Public transport user cost for three land use scenarios 64

Figure 6.3 Percentage of highway network with V/C>1.2 for three land use scenarios 65

Figure 6.4 Peak hour PT Modal share for the scenario P3E3 without IPT mode 67

Figure 6.5 Peak hour Average trip length of PT modes in Km for the scenario P4E3 67

Figure 6.6 Peak hour PT Modal share for the scenario P2E2 without IPT mode 68

Figure 6.7 Peak hour Average trip length of PT modes in Km for the scenario P2E2 68

Figure 6.8 Peak hour PT Modal share for the scenario P3E4 without IPT mode 69

Figure 6.9 Peak hour Average trip length of PT modes in Km for the scenario P3E4 70

Figure 6.10 The summary of average ratings of the three groups of interest for all the

indicators 72

Figure 6.11 Summary of RTPI for all the scenarios 73

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y

xi

List of Tables

Table No. Description Page No.

Table 2.1 Effects of Land use policies on transportation 11

Table 2.2 Percentage change in outputs for model year 2020 caused by the

percent error modeled in exogenous production 13

Table 2.3 Percentage change in outputs for model year 2020 caused by the

percent error modeled in commercial trip generation rates 14

Table 2.4 Comparison of three major approaches for measure transportation 17

Table 2.5 Evaluation Framework of Urban Transportation Efficiency 18

Table 3.1 Zoning scheme for the MMR 24

Table 3.2 Work Force Participation Rates in Various Cities of the World 27

Table 3.3 Forecasted Employment of MMR 27

Table 3.4 Forecasted Vehicle Ownership in MMR 27

Table 3.5 Overall Forecasts of Total Travel Demands, Modal, Split and Average

Trip Lengths 28

Table 3.6 Range of Population and employment levels in MMR 29

Table 4.1 A typical data sheet for the Rating and Ranking survey 36

Table 4.2 Sample data sheet for Calculation of Aggregate rating score for

each indicator 36

Table 5.1 Different types of road links with their link characteristics 40

Table 5.2 Summary of bus route network available in MMR 46

Table 5.3 Trip Production Model (excluding walk) for various purposes during

morning peak 51

Table 5.4 Trip Attraction Model (excluding walk) for various purposes during

morning peak 52

Table 5.5 Gravity model parameters used for trip distribution 54

Table 5.6 Proposed Mode Choice Models for GMR (Morning Peak without Walk) 55

Table 6.1 Total Public transport user cost for three land use scenarios 63

Table 6.2 Percentage of highway network with V/C>1.2 for three land use scenarios 64

Table 6.3 Expected fatality rate for different vehicle ownership level for developing

countries 65

Table 6.4 Expected fatalities for MMR in case of all the scenarios 65

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y

xii

Table 6. 5 Peak hour loadings by all public transport modes for P3E3 Scenario 66

Table 6. 6 Peak hour loadings by all public transport modes for P2E2 Scenario 67

Table 6. 7 Peak hour loadings by all public transport modes for P3E4 Scenario 68

Table 6. 8 Vehicle kilometers and Average trip lengths by PV and PT for all the

Scenarios 70

Table 6.9 Cost of proposed transport infrastructure for the horizon year 2031 70

Table 6.10 The summary of average ratings of the three groups of interest for

all the indicators 71

Table 6.11 The calculation sheet for computation of RTPI for all the scenarios 74

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y

xiii

List of Abbreviations

BEST Bombay Electric Supply and Transport Company

BRT Bus Rapid Transit

GIS Geographic Information System

GM Greater Mumbai

GT Generalized Time

IPT Intermediate Public Transport

KDMT Kalyan Dombivali Municipal Transport

MBMT Mira Bhayander Municipal Transport

MCDM Multiple Criteria Decision Making

MMR Mumbai Metropolitan Region

MMRDA Mumbai Metropolitan Regional Development Authority

MNL Multinomial Logit Model

NMMT Navi Mumbai Municipal Transport

PT Public Transport

PV Private Vehicles

RTPI Relative Transportation Performance Index

RW Relative Weighted Score

TAZ Traffic Analysis Zone

TMT Thane Municipal Transport

TRANSFORM Transportation Study for Mumbai

VMT Vehicle Miles Travelled

V/C Volume (interpreted as Demand Volume) to Capacity Ratio

VOT Value of Time

VOC Vehicle Operating Cost

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y

1

Chapter 1

Introduction

1.1 General

The transport of human beings represents the people’s desire to participate in various

activities like living, working, education, shopping, healthcare and recreational in various

places of any region that we concern. Similarly the transport of goods also is due to the

various activities such as production, distribution and consumption of goods in various places.

Hence the travel is the derived demand of various land use patterns of a region. It can be

easily understood that land means the spatial distribution of locations of various activities in a

region such as residential, commercial, industrial and educational etc., and the transport is the

link between them. The land use determines the magnitude, direction, purpose and spatial

distribution of travel which is to be accommodated by the overall transportation system

present in the region.

Economic development as well as industrial and social developments of any country is

very much dependent on transport infrastructure which accommodates the total travel in a

region and again which depends on land use pattern present in the region. Hence it is very

essential to study the complex inter relation between land use and transport so that

metropolitan regions and their associated transportation can be better planned through

scientific methods. The inter relationship between the urban land use and urban transport has

been recognized as the phenomenon of attention in the policy level (Gupta, 2010). Changes in

land use systems can modify the travel demand patterns and induce changes in transportation

systems. Transportation system evolution, on the other hand, creates new accessibility levels

that encourage changes in land use patterns. Hence it is assumed to be a cyclic process. Based

on this assumption many integrated land use-transport models were evolved worldwide as

well as in India to analyze the effects of land use on transport and vice versa. Even though

many integrated models were evolved, they are not best suitable for implementation for the

Indian conditions due to many reasons. Hence there is high need to study on the land use-

transport interaction in the Indian context.

1.2 Problem statement

Now a days, the urbanization is growing at an exponential rate and posses an increased

demand for the transportation facilities for the movement of man and material. The rapid

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y

2

growth of traffic seems to be a major problem at present for transport planner. Along with the

worldwide trend, developing countries like India are also undergoing rapid urbanization. This

shows the importance of proper planning to meet the urban transportation. The present land

use-transport integrated models explains the inter relationship between the urban land use and

transport theoretically which is practically highly impossible in the countries like India due to

many social, political and other influences. A few integrated land use transport models were

already developed in India were also failed in the applicability. Hence the alternative

approach is needed to evaluate the effect of land use on the travel pattern of the metropolitan

region like Mumbai Metropolitan Region (MMR).

As the evaluation process can be performed using a travel demand model it has to be

developed using more user friendly GIS (Geographical Information System) based

transportation planning software package. The best method should be adopted for the network

development for entire MMR without missing any possible link excepting street roads. Also

in the evaluation process, land use policies which results lowest individual motorized vehicle

need not be the best policies. There is no standardized evaluation criteria to evaluate the given

urban transportation system’s performance. Hence there is a need to study the selection

criteria of performance indicators and also to develop the criteria on which the urban

transportation system performance can be evaluated relatively.

1.3 Objectives and Scope of the Study

The main objective of the study is the evaluation of various land use or growth scenarios in

terms of mobility, accessibility, pollution and total transportation cost for the entire MMR by

developing the GIS based transportation planning model using the software package, CUBE

Voyager. With understanding the need of the project, the following sub-objectives were set

for the present study.

1. To study various methodologies for performing travel demand modeling in regional

context. To discuss the basic aspects of urban land use transport interaction and the

uncertainty of integrated land use transportation models for the evaluation of land use

scenarios in Indian conditions. Addressing the issues of selection of indicators and the

evaluation criteria.

2. Developing GIS based transportation network along with the complete data base of the

public transport services.

3. Implementing the working travel demand model for the present study area by using a

state of the art transportation modeling software.

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y

3

4. Selecting the performance indicator set and conducting the rating survey to assign the

relative importance to the selected indicators for the evaluation.

5. Formulation of Multi Criteria Decision Making approach for evaluating the relative

performance of urban transportation system.

6. Finally, the application of the model to evaluate the urban land use scenarios and

ranking them based on considered indices of evaluation by adopting the MCDM

technique.

1.4 Organization of the Report

The report has been divided into seven chapters. The topic has been introduced in the chapter

one highlighting the nature of the problem and its objective. Literature about Regional travel

modeling, land use transport interaction, uncertainty in integrated land use transport model in

Indian conditions and literature on evaluation of transportation system are reviewed in the

second chapter. The various aspects of study area and planning variables are discussed in the

third chapter. Fourth chapter contains the methodology to achieve the objectives for the

present study and formulated procedure for evaluation using a multi criteria decision making

approach respectively. The fifth chapter is having the contribution towards the present study

i.e. development of travel demand model is explained. The evaluation of urban transportation

system using MCDM is carried out in the chapter six. The summary and conclusions from

analysis, limitations of the study and future scope of the work are mentioned clearly in the

final chapter.

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y

5

Chapter 2

Literature Review

2.1 General

In this chapter basic aspects of GIS based regional travel demand modeling are discussed. The

basic interaction between the land use and transport is emphasized here to understand the inter

relation between them. Some of the existing land use transport models are reviewed and their

limitations in Indian conditions are also discussed. The different evaluation indices and

criterion for testing land use scenarios are studied in detail.

2.2 Metropolitan Regional Travel Demand Modeling

The Travel demand models are being developed since many years. Models are essentially

“decision-support tools” to assist transportation planners and policy-makers in analyzing the

effectiveness and efficiency of various transportation alternatives in terms of mobility,

accessibility, environmental and equity impacts. , it can be used for the evaluation of various

land use and transport scenarios of the region effectively. Travel demand forecasting for a

metropolitan region is definitely need to be paid attention as it consists of different sub

regions having different land use densities and different operators for the same mode of

transportation. The various regional travel demand models are summarized here. Each of

these models is having different requirements for the accuracy and usefulness of the model

outputs.

2.2.1 Baltimore Regional Travel Demand Model

(Baber & John, 2004)It is a computerized travel demand model which can simulate the

person travel and vehicle flows on the highway network and regional transit system. The

Baltimore region consists of Baltimore City and six other regions around it. The model is

developed choosing the 2000 year as the base year. The Traffic Analysis Zoning (TAZ) is

done based on the 2000 year census demographics which consists of 1463 internal TAZs and

42 external zones. Too many TAZ node numbers are used while coding the network which

can accommodate the future requirement and at the same time some lines code is inserted to

skip the process for that unused TAZs to reduce the run time for the model. MapInfo GIS and

VIPER were used to develop the highway network and public transport network coding

respectively. Here 14 different link types are used depending functionality of road. The total

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y

6

region has been divided into four different areas namely city centre, urban sub urban and rural

based on land use densities. The capacities and speeds for all the links are taken from HCM

2000 and they were updated on basis of land use density of the area in which they present.

The summary of the model is shown in the figure 2.1 below.

Skim Transit

Income Stratification

Trip Generation

Skim Highways

Trip Distribution

Trip Assignment

Balance Trip Table

Mode Choice

Skim Highways

Skim Transit

Trip Distribution

Income Stratification

Mode Choice

Balance Trip Table

Trip Assignment

Off - Peak Peak

Figure 2.1 The Summary of Baltimore Regional Travel demand Model (Baber & John, 2004)

The traditional sequential travel modeling steps are adopted for the study and

implemented in TP++ transportation planning software. Trip generation process is done for

different trip purposes such as, Home based other, home based work, work based other, other

based other, commercial vehicles trips, medium truck trips and heavy truck trips using

regression analysis. The generation equations are developed for all areas of the region

differently. The gravity model is used to execute trip distribution step by taking the

impedances as the travel time between the zones. The different skims are produced for six

different timings of a day. The model split step was performed for the trips of different

purposes with congested skims as input. The total trips are converted as vehicle trips and are

assigned on to the regional network to produce the link volumes, vehicle miles of travel and

volume to capacity ratios by using the equilibrium assignment model. There are two passes in

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y

7

the model; the AM peak period assignment produced in the first pass is used to produce

assignments in the next five time period in the second pass.

2.2.2 San Francisco Metropolitan area Travel Demand Model

The Metropolitan Transportation Commission (MTC) zonal system is 1099 regional travel

analysis zones internal to the nine-county Bay Area, and 21 external zones. The 1099 regional

travel analysis zones are based on 1990 census geography. The MTC regional transit network

includes 700+ transit lines for 25 transit operators. The modeling system includes the standard

four steps of trip generation, trip distribution, model split and trip assignment , as well as three

extra main models were; workers in household, auto ownership choice and time of the day

choice models.

Trip based versus Activity based travel demand models were discussed in the project.

It has been verbalized that market segmentation is a critical feature of the advanced trip based

travel modeling. Trips are classified as Home based work, Home based shop, home based

social/recreational, Non home based other, and Home based school. Trip generation models

include both trip production and trip attraction models. Production models are based on trips

made by households, workers or students at the home end of home-based trips. Attraction

models are based on trips made at the non home end of home-based trips. Trips as defined in

these trip generation models include non-motorized trips (bicycle, walk) as well as motorized

modes (auto, transit). With the exception of the home-based school trip generation models, all

of the new trip generation models are multiple regression in form. The home-based shop trip

generation model, in particular, is a hybrid of a cross-classification model. The usual gravity

type model is used in trip distribution step. In addition to friction factors, socio economic

adjustment factors (k-factors) are used in calibrating and validating trip distribution models.

Seven mode choice models are included in the model set, in which six are of nested logit

models and home based grade school modal split model is multinomial logit model. Departure

time choice, or time-of-day choice models, are very new to metropolitan transportation

practice. The departure time choice model included in the model system is a simple, binomial

logit choice model with two alternatives.

The utility for the off-peak alternative is defined as 0.0. Therefore, the exponentiated

utility of the off-peak alternative (exp(0)) is 1.0. In application, the probability of a home to-

work auto person trip starting in the peak period is calculated as;

Probability (Peak Start) = exp (Utility (Peak)) / [1 + exp (Utility (Peak))]

All the trips are assigned on the network as usually in trip assignment step. (Purvis, 1997)

The summary of travel demand model is shown in figure 2.2 below.

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y

8

Figure 2.2 Summary of San Francisco Travel demand model (Purvis, 1997)

2.3 GIS in Travel Demand Modeling

(Moorthy et al., 2003)Geographical Information System (GIS) is a preferred platform for the

travel demand modeling, because the data attributes are associated with topological object

(point, line or polygon). In GIS, information is identified according to their actual locations.

The graphical display capabilities allow visualization of different locations of traffic

generators, network and routes. The use of GIS in transportation planning will enhance the

visualization aspect and facilitate the development of decision modules for use by the

transport planners.

(Beard, 1993)Has taken the small suburban area as the study area to demonstrate the

comparative differences between GIS and Non GIS based travel modeling. The land use

characteristics in terms of developable land were forecasted by using both methods and

performed transportation planning by taking identical data set in both the methods. The results

were compared in terms of vehicle miles travelled and traffic volume on the links which have

shown the large differences. Hence we can conclude from this work that the GIS are

definitely needed for the efficient travel demand modeling.

Workers in Household Choice

Workers in Household Choice

Trip Generation

Trip Distribution

Mode Choice

Time-of-Day choice (Peak/Off-Peak)

Trip Assignment

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y

9

The GIS based travel models are very efficient in decision support system for

example, (Arampatzis et al., 2004) has developed the GIS integrated model, to estimate and

reproduce the traffic behavior and traffic volumes for calculating the emissions and energy

consumptions. The GIS network data base was developed in which link has the attributes like

from and to nodes, length, speed, number of lanes and capacity. The public transport service

lines data and frequencies also included GIS network. The vehicular trips are calculated

assigned on to the GIS based network. Vehicle composition and travel speeds on each link

were used calculate the emissions and energy consumption and they can be shown on the

network each link. The GIS based decision supportive system is shown in the figure 2.3.

GEO Data Central Database Transport Database

Figure 2.3 Architecture of GIS based decision supportive system (Arampatzis et al., 2004)

2.4 Land use and Transport Interaction

(Wegener & Furst, 1999)The two-way interaction between urban land use and transport

addresses the locational, mobility and accessibility responses to changes in the urban land use

and transport system at the urban or regional level. The spatial separation of human activities

creates the need for travel and goods transport is the underlying principle of transport analysis

and forecasting. Following this principle, it is easily understood that the suburbanization of

cities is connected with increasing spatial division of employees, and hence with ever

increasing mobility. However, the reverse impact from transport to land use is less well

known. There is the evolution from the medieval cities, where almost all daily mobility was

Spatial Information System

Logical data base system

Geo data update

Interface

Map based

Interface

KB data

Interface

Emission Energy

Models

Traffic

Models

Predefined Queries Knowledge Base

USER

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y

10

on foot, to the vast expansion of modern metropolitan areas where their massive volumes of

intraregional traffic would not have been possible without the development of first the railway

and in particular the private automobile, which has made every corner of the metropolitan

area almost equally suitable as a place to live or work. The recognition that trip and location

decisions co-determine each other and therefore transport and land-use planning needed to be

co-ordinated led to the notion of the 'land-use transport feedback cycle which is shown in the

figure 2.4.

Figure 2.4 Land use transportation feedback cycle (Wegener & Furst, 1999)

i. The distribution of land uses, such as residential, industrial or commercial, over the urban

area determines the locations of human activities such as living, working, shopping,

education and leisure.

ii. The distribution of human activities in space requires spatial interactions or trips in the

transport system to overcome the distance between the locations of activities.

iii. The distribution of infrastructure in the transport system creates opportunities for spatial

interactions and can be measured as accessibility.

iv. The distribution of accessibility in space co-determines location decisions and so results

in changes of the land-use system.

The table 2.1 explains the effects of land use on transportation well. There are other

factors also which can explain the nature of land use changes. As we are considering these

two effects only into consideration only the residential density and employment density are

taken for the review. In the table we can observe that trip frequency will not be effected up on

any land use changes. Hence we can include the trip length and mode choice excepting trip

frequency.

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y

11

Table 2.1 Effects of Land use policies on transportation

(Wegener & Furst, 1999)

Direction

Observed

Factor Impact on impacts

Land use

Transport

Residential

density

Trip length

Numerous studies support the

hypothesis that higher density

combined with mixed land use leads to

shorter trips. However, the impact is

much weaker if travel cost differences

are accounted for.

Trip frequency

Little or no impact observed.

Mode choice

The hypothesis that residential density

is correlated with public transport use

and negatively with car use is widely

confirmed.

Employment

density

Trip length

In several studies the hypothesis was

confirmed that a balance between

workers and jobs results in shorter

work trips, however this could not be

confirmed in other studies.Mono-

functional employment centres and

dormitory suburbs, however, have

clearly longer trips.

Trip frequency

No significant impact was found.

Mode choice

Higher employment density is likely to

induce more public transport use.

In the similar way the trasport policy changes also effect the land use patterns. i.e for

example the residential and employment density in zones along the metro corridor will be

more than that in other zones with respective to the accessibility to the bus or rail terminals.

2.4.1 Structure of Land use Transportation Interaction Models

For the purposes of longer range forecasting, in the 15 to 50 year range, such transportation

planning models need to be tied to a land use plan for the same region.

(Barra, 1989)Figure 2.5 describes a possible common structure for a typical linked

land use and transport model. The calculation sequence starts with a regional model which

consists of two linked sub models viz. a regional employment sub model and a demographic

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y

12

sub model, which together perform the calculation of the total population and employment for

the region.

Regional level

Urban activity

Level

Urban Transport

Level

Figure 2.5 The general structure of integrated land use and transport model (Barra, 1989)

The next stage corresponds to the location of activities within the urban area or in the

region and consists of the location of basic employment, floor space, residential population

and service employment from the totals generated by the regional model. These in turn are

input to the transportation model, consisting of four stages; trip generation trip distribution,

model split, trip assignment and generalized costs or skims.

From the generalized cost calculations, two main feedbacks are recognized. The first

goes to the trip distribution stage as the congestion builds up in certain parts of the network,

the trip distribution step is affected and probabilities choosing the each mode can change. This

feedback is equivalent to equilibrium between supply and demand of the transport. This

equilibrium is assumed to takes place instantaneously i.e. no time lag is required.

The second feedback goes back to the location of activities, affected by the changes in

the generalized cost of the travel between the zones. This second loop is assumed to takes

place more slowly, because activities will take some to adapt to the changes in the changes in

Regional Employment

Model for all periods

Demographic Model for

all periods

Basic employment

location model

Location of floor space, residents

and service employment

Trip generation models

Trip distribution and

model split models

Trip assignment model

Travel time and

generalized costs

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y

13

the accessibility. It is easily understood that, the two key elements that relate land use and

transportation are trip generation and generalized cost.

2.4.2 Uncertainty in Integrated Land use Transport Models

Johnston & Clay (2005) Integrated land use and transportation models are typically given

precise inputs and return precise outputs. The authors have introduced the uncertainty into the

inputs of an integrated land use and travel demand model to determine the effect of uncertain

inputs on the model outputs. In the uncertainty analysis, only selected variables are varied

based on their sources of uncertainty in the model. Sacramento of California is considered as

the study area which is having the population of 1.9 millions in the base year 2000. The

MEPLAN integrated land use transport model is used for the demonstration of uncertainty.

Exogenous production, commercial trip rates and concentration parameter are varied plus or

minus 10, 25, and 50% which indirectly represents the errors in their sources such as forecasts

of population and employment. The results were demonstrated through the results shown in

following table 2.2 and table 2.3.

Table 2.2 Percentage change in outputs for model year 2020 caused by the percent error

modeled in exogenous production (Johnston & Clay, 2005)

Exogenous

Production (%)

VMT

(%)

SOV mode

share (%)

SOV mode share

for work trips (%)

Total number of

SOV trips (%)

+10 1.64 -0.96 -0.51 2.37

-10 -2.87 0.57 0.82 -2.81

+20 4.36 -1.87 -1.56 6.83

-25 -6.13 1.13 1.40 -7.38

+50 8.61 -3.16 -3.44 13.11

-25 -12.60 3.15 4.38 -14.23

Vehicle miles traveled (VMT) and the number of trips are the most vulnerable of the

outputs monitored to uncertain population and employment forecasts. This is expected. The

number of trips per household is a fixed relationship in this model, hence any increase in the

number of households will be accompanied by an increase in the number of trips. The number

of miles traveled will vary with the number of trips and with average speeds.

Single occupant vehicle (SOV) mode shares change very little even with 50% swings

in the forecasts of exogenous demand and they change in the opposite direction to VMT and

total number of trips. As population goes up, so does VMT and the number of trips, which

causes increased congestion and a lower mode share for single occupant vehicles. Figure 2.6

shows the impact across model years of uncertainty in exogenous production on outputs.

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y

14

Figure 2.6 Typical impact over time of uncertainty in population and employment

(exogenous production) forecasts on model outputs (Johnston & Clay, 2005)

Modeling uncertainty in the commercial trip generation rates had the largest effect on

model outputs. One reason for this is that while the exogenous production inputs deal only

with added population and employment (i.e. they do not affect existing firms and households),

the commercial trip generation rates are ‘global’ parameters, affecting both existing and new

employment.

Table 2.3 Percentage change in outputs for model year 2020 caused by the percent error

modeled in commercial trip generation rates (Johnston & Clay, 2005)

Commercial trip

generation rates (%)

VMT

(%)

SOV mode

share (%)

SOV mode share

for work trips (%)

Total number of

SOV trips (%)

+10 4.35 -4.54 -2.66 2.88

-10 -6.32 5.75 3.61 -2.46

+20 9.74 -10.16 -7.22 7.29

-25 -13.91 12.10 6.56 -9.65

+50 16.45 -17.89 -13.31 13.99

-25 -23.15 24.85 9.37 -23.55

The direction of impact in Table 2.3 is interesting. While increasing the commercial

trip generation rate increases VMT and the number of trips (as expected) it lowers the mode

shares of SOV for all trips and work trips. By increasing this input, which increases VMT and

total trips, congestion on the roadways increases, which consequently leads to a weaker SOV

share of trips. This region has light rail transit and so some SOV trips switch to LRT, which is

not slowed down by road congestion. Figure 2.7 presents the impact of uncertainty in

commercial trip generation rates on model outputs over time.

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y

15

Figure 2.7 Typical impacts over time of uncertainty in commercial trip generation rates on

model outputs (Johnston & Clay, 2005)

Here we can easily understand that the amount of error in the VMT outputs may be

greater than the VMT differences produced by alternative policies (or transportation

improvements/investments). If this is the case, the land use plans cannot be evaluated or

ranked correctly.

2.5 Evaluation indicators for the land use scenarios

(Littman, 2011) has mentioned that the sustainable planning decisions depend on the how the

transportation systems performance is measured with some indicators. The indicators are

having many uses in planning and management. This data helps in indentifying the problems,

accessing the alternative options to solve them, setting the performance targets and evaluate a

particular jurisdiction in the study area or whole region. The type of indicators that we chose

will definitely influence on the results analysis because a particular policy may rank high with

one set of indicator set but it rank very low with another set of indicators. Hence it is

suggested to take at most care while selecting the performance indicators.

The author also specified standard definitions as below which have given motivation

to develop the methodology which is used in the present study.

Target: A specified, realistic, measurable objective

Indicator: a variable selected and defined to measure the progress towards the

objective

Indicator type: nature of data used by the indicator (quantitative, qualitative, absolute

or relative)

Indicator set: a group of indicators selected to measure the comprehensive progress

toward the goals

Index: a group of indicators aggregated to form a single value

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y

16

2.5.1 Guide lines for selecting the indicators

Littman, 2011 has suggested a few guidelines or precautions to select the proper indicator set

while evaluating the transportation system performance.

i. All the indicators are selected based on their usefulness in decision making in

transportation planning and also on the ease of collection of data or measuring them.

ii. An indicator which focuses too much on one type of impact may overlook the other

impacts, so that the decisions resulted cannot be optimal. Hence it is very important to

understand the perspectives, assumptions and limitations of each indicator in

representing the particular impact.

iii. The indicators are evaluated for each jurisdiction wise to take the better decisions in

transportation planning, and also the indicators should be comparable with the other

jurisdictions in terms of performance.

iv. Indicators should be easy to understand.

v. The maximum possible indicators are used in the set which can well represent the

impacts or performance as well as which can quantifiable easily the available

resources of information.

(Littman, 2008)There are different perspectives with different measures of transportation

system in a region. There exist three approaches to measuring transportation system

performance.

i. Traffic-based measurements (such as vehicle trips, traffic speed and roadway level of

service) evaluate motor vehicle movement.

ii. Mobility-based measurements (such as person-miles, door-to-door traffic times and

ton miles) evaluate person and freight movement.

iii. Accessibility-based measurements (such as person-trips and generalized travel costs)

evaluate the ability of people and businesses to reach desired goods, services and

activities.

Accessibility is the ultimate goal of most transportation and so is the best approach to use.

There is no single way to measure transportation performance that is both convenient and

comprehensive. Transportation professionals should become familiar with the various

measurement methods and units available, learn about their assumptions and perspectives, and

help decision makers to understand how they are best used to accurately evaluate problems

and solutions.

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y

17

Conventional ways of measuring transportation system performance, such as roadway

Level of Service and traffic speed, tend to favor vehicle travel over other forms of access.

Only by developing better methods of measuring mobility and accessibility, more accessible

land use patterns will be recognized. The following table 2.4 compares the compares the three

major approaches for measuring transportation.

The transportation system performance can also be measured in mode share for different

modes, V/C (Volume to Capacity ratio) ratios for all links of the network and level of service

for the total network.

Table 2.4 Comparison of three major approaches for measure transportation (Littman, 2008)

Traffic Mobility Accessibility

Definition of

Transportation

Vehicle travel. Person and goods

movement.

Ability to obtain

goods, services and

activities.

Unit of measure Vehicle-miles and

vehicle-trips

Person-miles, person-

trips and ton-miles.

Trips.

Modes

considered

Automobile and truck. Automobile, truck and

public transit.

All modes, including

mobility substitutes

such as

telecommuting.

Common

performance

indicators

Vehicle traffic

volumes and speeds,

roadway Level of

Service, costs per

vehicle- mile, parking

convenience.

Person-trip volumes

and

speeds, road and transit

Level of Service, cost

per person- trip, travel

convenience.

Multi-modal Level of

Service, land use

accessibility,

generalized cost to

reach activities.

Assumptions

concerning what

benefits

consumers.

Maximum vehicle

mileage and speed,

convenient parking,

low vehicle costs.

Maximum personal

travel and goods

movement.

Maximum transport

options, convenience,

land use accessibility,

cost efficiency.

Consideration of

land use.

Favors low-density,

urban fringe

development patterns.

Favors some land use

clustering, to

accommodate transit.

Favors land use

clustering, mix and

connectivity.

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y

18

Traffic Mobility Accessibility

Favored

transport

improvement

strategies

Increased road and

parking capacity,

speed and safety.

Increased transport

system capacity,

speeds and safety.

Improved mobility,

mobility substitutes

and land use

accessibility.

(YUAN, 2003) has considered that the urban transportation system performance is the

key factor in determining the capability of urban transportation system and the balance

between the travel demand and supply. The impact factors of urban transportation efficiency

are mainly divided into four aspects such as urban land use pattern, transportation

infrastructure and traffic management system. The hierarchical evaluation framework of

urban transportation efficiency is proposed which is shown in the table 2.5.

Table 2.5 Evaluation Framework of Urban Transportation Efficiency (YUAN, 2002)

Type of

factors Index level _1 Index level _2 Index level _3

Urban layout

and land-use

pattern

A1—population density in downtown areas

A2-- ratio of job units to residential population

A3-- ratio of population density in downtown areas to that in suburbs

A4-- relative radius of transportation within 0.5, 1, 2 hours

Urban

transportation

structure

A5-- share of urban public transportation modes

Urban

transportation

infrastructure

A6--

efficiency of

road

infrastructure

B1-- ratio of Average Travel Speed (ATS) to designed

road speed

B2 -- ratio of V/C

B3 -- ratio of traffic volume in peak hours to AADT

A7 --

efficiency of

parking

infrastructure

B4--ratio of average parking volume in peak hours to

designed capacity

B5-- ratio of average daily occupancy time of each berth

B6-- ratio of average daily parking number of each berth

A8--

efficiency of

urban

transportation

vehicles

B7 --

efficiency of

bus systems

C1 -- average load factor of bus systems

C2 --average area of road occupancy per

passenger of bus systems

C3 – average daily overload duration of bus

systems

B8--

efficiency of

urban rail

systems

C4 -- average load factor of rail systems

C5 -- average area of carriage occupancy

per passenger of rail systems

C6-- average daily overload duration of rail

systems

B9 -- proportion of congested intersections without signal

Urban traffic A9 – status of control during peak hours

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y

19

Type of

factors Index level _1 Index level _2 Index level _3

management traffic

congestion

B10 --proportion of congested intersections controlled by

traffic signal during peak hours

B11--average intersections daily congestion duration of

main intersections

A 10--status of

traffic safety

B12 -- death toll per 10000 PCU

B13 -- death toll per 1 mil. (PCU˙Km)

Energy

reservation A11-- average energy consumption per capita in urban transportation

systems

Environment

protection

A12-- share of air pollution

A13-- share of noises pollution

The author also has clearly expressed the problems encountering in the evaluation of urban

transportation infrastructure efficiency is that there is not a determined and absolute way to be

referred and the uncertainty of evaluation criteria is the most important problem to be solved.

The fuzzy theory is adopted here to reduce the uncertainty and the three cities (Guangzhou,

Shanghai and Beijing) are compared w.r.t. the relative transportation system performance.

The more practical performance indicators suitable for evaluating the proposed transportation

system’s performance are given below by Litman, 2011.

• Awareness – the portion of potential users who are aware of a program or service.

• Participation – the number of people who respond to an outreach effort or request to

participate in a program.

• Utilization – the number of people who use a service or alternative mode.

• Mode split – the portion of travelers who use each transportation mode.

• Mode shift – the number or portion of automobile trips shifted to other modes.

• Average Vehicle Occupancy (AVO): Number of people traveling in private vehicles

divided by the number of private vehicle trips. This excludes transit vehicle users and

walkers.

• Average Vehicle Ridership (AVR): All person trips divided by the number of private

vehicle trips. This includes transit vehicle users and walkers.

• Vehicle Trips or Peak Period Vehicle Trips: The total number of private vehicles

arriving at a destination (often called “trip generation” by engineers).

• Vehicle Trip Reduction – the number or percentage of automobiles removed from

traffic.

• Vehicle Miles of Travel (VTM) Reduced – the number of trips reduced times average

trip length.

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y

20

• Energy and emission reductions – these are calculated by multiplying VMT reductions

times average vehicle energy consumption and emission rates.

• Accessibility (ability to reach desired services and activities), including the travel time

and costs required by various users to reach activities and destinations such as work,

education, public services and recreation

• User Evaluation – Overall user satisfaction with their transportation system.

Planners should identify appropriate indicators that measure progress toward stated goals and

objectives, taking into account the quality of available data and the costs of collecting any

additional data.

(Zegras, 2006) has explained the role of performance indicators, evaluation criteria in a very

nice manner through a flow chart which is shown in the figure 2.8.

Figure 2.8 The role of indicators in a transportation planning process

(Zegras, 2006) also presents the Sustainability Indicator Prism that innovatively represents the

hierarchy of goals, indexes, indicators, and raw data as well as the structure of

multidimensional performance measures (Zegras, 2006). As shown in Figure 2.9, the top of

the pyramid represents the community goals and vision, the second layer represents a number

of composite indexes around the selected themes, third layer represents indicators or

performance measures building from raw data at the bottom of the pyramid.

Figure 2.9 sustainability indicator prism (Zegras, 2006)

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y

21

2.5.2 Multiple Criteria Decision Making in Transportation Planning

The multidimensional nature of sustainability indicates that multi criteria or multi objective

methods would be more appropriate for sustainability assessments than single-

criterion/single-objective methods. This section first reviews multiple criteria decision making

(MCDM) methods in general and identifies a number of MCDM applications to transportation

planning decision making. Multi-criteria decision making (MCDM) is one of the established

branches of Decision Theory, and it is especially useful when making preference-based

decisions over available alternatives that are characterized by multiple, usually conflicting,

attributes

(Hwang and Yoon, 1981; Triantaphyllou, 2000) Unlike single-objective decision-making

techniques, such as benefit-cost or cost-effectiveness analysis, MCDM approaches can take

into account a wide range of differing, yet relevant criteria. Even though these criteria cannot

always be expressed in monetary terms, as is the case with many externalities, comparisons

can still be based on relative priorities. MCDM methods are generally divided into (1) multi-

objective decision making (MODM) that studies decision problems with a continuous

decision space and (2) multi attribute decision making (MADM).

Because the transportation planning process includes many different objectives or attributes

and reflects the interests of a wide range of stakeholders, appropriate techniques need to

incorporate these multiple and conflicting objectives into the assessment process. Moreover,

decision-making in the context of sustainable transportation should involve the evaluation of a

discrete set of alternatives while simultaneously considering conflicting objectives. This

section identifies relevant international studies that apply different MCDM methods to

metropolitan transportation planning and decision making.

The research trends indicate that MCDM methods have been often applied to project-level

studies since the early 1980s. MCDM applications to broader scope analyses, such as the

evaluation of transportation plans or policies, are more recent research trends. One of the most

common methodologies of MCDM is Saaty’s Analytic Hierarchy Process (AHP) developed in

1970s to provide a systematic approach to setting priorities and decision making based on

pairwise comparisons between criteria. Another recent trend includes an embracement of

different types of “fuzzy” multi criteria decision making approaches. These fuzzy-type

MCDM methods attempt to cater for uncertainty, vagueness, or fuzziness commonly inherent

in human decision making due to a lack of information or constraints in human thinking.

Some other initiatives make progress by combining the AHP method with different types of

fuzzy MCDM methods. The following two paragraphs describe some examples of relevant

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y

22

studies that apply the AHP method and fuzzy-type MCDM methods, respectively, in

transportation decision making.

2.5.3 Inferences from the literature

The existing indicator systems reveal that operationally, transportation system performance is

largely being measured by transportation system effectiveness and efficiency as well as the

environmental impacts of the system. The application of a multiple criteria decision making

(MCDM) approach in the sustainability evaluation framework is broadly applicable. Most

analytical models of sustainability are based on the multidimensional themes of economic,

environmental, and social impacts, indicating that a robust method should at the minimum

consider these dimensions as decision making criteria. Thus, multicriteria/multiobjective

methods seem to be better suited to sustainability assessments than single-criterion/single

objective methods. Common multiple criteria decision making (MCDM) methods were first

reviewed in general, and their applications to transportation planning and decision making

identified. The chapter 5 demonstrates a proposed formulation of the multiple criteria decision

making (MCDM) approach for evaluating competing land use scenarios and identifying

superior alternatives.

2.6 Summary

The stated of art in regional travel modeling is reviewed here by taking two regional travel

models as case studies. In both the models the traditional travel modeling technique is used.

Advantages of advanced trip based modeling are pointed out over activity based modeling.

After that the importance of GIS in Travel demand modeling is discussed. Then the concept

of land use transport interaction and outline of land use transport modeling are reviewed. The

uncertainty in integrated land use transport models is highlighted by taking one case study.

Hence it is proposed that the traditional scenario based approach is adopted for the present

study for the evaluation of land use scenarios. The evaluation indices also are briefly outlined

here. The next chapter explains the study area features and planning variables for the present

study.

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y

23

Chapter 3

Study Area and Planning Variables

3.1 General

Mumbai Metropolitan Region (MMR)/Mumbai Metropolitan Area, comprising of a municipal

corporations, councils, and rural areas is the largest urban agglomeration in India. The MMR

is spread over an area of 4355 square kilometers and has a population of approximately 16.73

million according to 2001 census which is accounting for 20% population of total

Maharashtra and about 2% population of India. It is estimated to grow to 23 million by 2011

and 34 million by 2031. Primary among the constituents of MMR is the Greater Mumbai

which is referred as financial capital of India, hence the employment is very much high in

MMR. About 0.7 million people enter the Greater Mumbai daily in the morning peak period.

The economic and transportation perspective in the different regions in the MMR are

functioning as a single entity with people travelling between different municipal areas for

work, education, shopping and personal needs.

3.2 Study Area

The Mumbai Metropolitan Region is the metropolitan area consisting of the metropolis of

Mumbai and its satellite towns. Developing over a period of about 20 years, it consists of

seven municipal corporations and fifteen smaller municipal councils. The entire area is

overseen by the Mumbai Metropolitan Region Development Authority (MMRDA)The study

area consists of 4 districts of Maharashtra in which Mumbai City and Mumbai suburbs are

completely included and parts of Thane and Raigarh. The Mumbai Metropolitan Region

(MMR) is one of the fastest growing metropolises in India. With a population of 17.7 Million

(Census, 2001), it is ranked as the sixth largest metropolitan region in the world. The

composition is given in the figure 3.1 as well as in table 3.1.

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y

24

Figure 3.1 Sub Regions of MMR (TRANSFORM, 2005)

3.3 Zoning System

Any transportation model requires the study area to be divided into the zones i.e. Traffic

Analysis Zones. As per TRANSFORM, 2005 the MMR has been divided into 1037 zones. In

all there are 1030 internal zones are within MMR and there are 7 external zones which are

within Maharashtra but outside of MMR. All these zones are systematically numbered which

have been shown in the table 3.1.

Table 3.1 Zoning scheme for the MMR (TRANSFORM, 2005)

S.No Name of area Number of TAZ Zone Coding

1

MCGM 577

1-577 Island 232

Western Suburb 228

Eastern Suburb 117

2 Mira-Bhayender 26 578 - 603

3 Thane 95 671 - 765

4 Nallasopara 13 631 - 643

5 Navgarh-Manikpur 6 625 - 630

6 Vasai 21 604 - 624

7 Virar 27 644 - 670

8 Navi Mumbai 66 766 - 831

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y

25

S.No Name of area Number of TAZ Zone Coding

9 Panvel 8 842 - 849

10 Uran 2 850 -851

11 Rest of CIDCO 10 832 - 841

12 Kalyan-Dombiviali 54 851 - 905

13 Ulhasnagar 25 906 - 930

14 Ambernath 14 931 - 944

15 Bhiwandi - Nizampur 28 951 - 978

16 Kulgaon - Badlapur 6 945 - 950

17 Alibag 3 979 -981

18 Pen 6 982 - 987

19 Khopoli 5 988 - 992

20 Karjat 4 993 - 996

21 Matheran 1 997

22 Rural areas 33 998 - 1030

23 External zones 7 1031 - 1037

3.4 Planning Variables

Transportation is the movement of people or goods from an origin to destination. This pattern

of movement may vary with time and region. To capture these travel patterns and to

appropriately represent in the model, the characteristics of the origin and destination zones

should be necessarily studied. The number of planning parameters or variables used for the

modeling may vary with the study area, level of details required for the modeling and purpose

of modeling.

Earlier comprehensive transportation studies had been carried out by M/S Wilbur

smith & Associates in 1963, Central Road Research Institute (CRRI) in 1983 and by W.S.

Atkins in 1994. They have proposed various road development plans like developing arterials

and expressways. The planning parameters used in Wilbur smith were total population,

employment, and vehicle ownership and population density. In this study the MMR has been

divided into 139 zones. In the CRRI study total population, resident workers, resident

students, total employment, industrial employment were taken as planning variables. In this

study, MMR was divided into 99 zones.

Besides population, employment and its sub categories no other parameter have been

taken for planning by any of the studies.

The travel demand in an area depends on land use distribution and its intensity. The

variables that describe the travel demand traditionally have been the population, employment

and vehicle ownership. Population, employment and vehicle ownership in greater Mumbai

and their projection into the future has been taken from the Transform study (2005). The

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y

26

zonal planning parameters considered from the Transform study for Travel demand modeling

are,

i. Population (POP)

ii. Resident Worker – Office (RWF)

iii. Resident Worker - Industry category (RWI)

iv. Resident Worker – Other (RWO)

v. Resident Student (RS)

vi. Office Jobs/Employment (OJ)

vii. Industry Jobs/Employment (IJ)

viii. Other Jobs/Employment (OTJ)

The planning variables for the year 2005, 2016, and 2031 and the aggregate total population

and employment are taken from TRANSFORM Study and updated to base year.

Population

The estimation of population for the total MMR is shown the figure 3.2.

Figure 3.2 Forecasted Population of MMR from 1971 to 2031 (TRANSFORM, 2005)

Employment

Examples of the employment participation rates in some of the larger cities of the world are

shown in table 3.2. The relatively low employment participation rate in the MMR is largely a

reflection of the low participation rates by females in the workforce. An increase in the per

capita employment rate (0.37 to 0.45) over the next 25 years is considered reasonable. This

would mean a 2031 employment level of 15.3 million or a doubling of employment over

2005. The female employment rate in the MMR is currently at 0.12 compared to 0.56 of

males. In order to increase the overall participation rate to 0.45 and assuming a 5% increase in

male participation the female rate would have to increase to 0.28. This increase is considered

both achievable and desirable over the next 25 years particularly with greater levels of female

enrollment in schools. (TRANSFORM, 2005)

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y

27

Table 3.2 Work Force Participation Rates in Various Cities of the World (TRANSFORM,

2005)

Name of city Work force participation rate

Bangkok 0.53

Shanghai 0.59

Mexico City 0.40

Bogota 0.41

Seoul 0.48

Sap Paulo 0.41

London 0.53

Frankfurt 0.81

Hong Kong 0.47

Tokyo 0.54

Mumbai 2005

Estimate for MMR (2031)

0.37

0.45

The rest of the MMR i.e. MMR excluding Greater Mumbai had an employment of 6.09 lakhs

in 87,720 establishments in 1980 which has increased to 7.97 lakhs and 1.62 lakhs

respectively in 1990. Bhiwandi urban in 1990 has the highest employment of 1.71 lakhs

followed by 1.54 lakhs in Thane and 1.32 lakhs in Kalyan. The projected employment in

MMR is shown in the table 3.3.

Table 3.3 Forecasted Employment of MMR (TRANSFORM, 2005)

Factor 1971 1981 1991 2001 2011

Employment 1760500 2822300 3228763 4139521 543452

Vehicle ownership

The increase in private vehicle ownership during the period 1996-2005 in GMR is from 52 to

82 where as private vehicle ownership in MMR is increases from 50 to 95. The private

vehicle ownership in the rest of region is more than GMR. The phenomenon may be due to

high accessibility of IPT in GMR. Forecasted growth of vehicle ownership (per 10000

population) presented in the table was taken from transform study and are shown in Table 3.4

Table 3.4 Forecasted Vehicle Ownership in MMR per 1000population (TRANSFORM, 2005)

Year GMR Rest of MMR MMR

2006 95 134 110

2011 112 180 139

2016 132 228 171

2021 153 270 204

2026 175 304 236

2031 197 329 266

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y

28

3.4.1 Road Network and Transport System

The Region is well connected by 4 National Highways (to Pune, Nasik, Goa and Ahmadabad)

and 19 designated State Highways for the inter-regional passenger and goods traffic besides,

600 km. of road length fall under major district roads and other district roads. The overall

travel demand is summarized in the table 3.5.

Table 3.5 Overall Forecasts of Total Travel Demands, Modal, Split and Average Trip Lengths

(TRANSFORM, 2005)

1993 2011

Total Trips (peak period) 2,154,860 3,260,431

-Public Transport 1,893,751 (88%) 2,770,691 (85%)

-Private Vehicles 148,167 (7%) 289,516 (9%)

-Taxi 112,942 (5%) 200,224 (6%)

Average Trip Length (Km)

-PT 15.06 12.36

-Bus 4.67 4.67

-Rail 22.15 17.72

-PV 14.17 12.1

-Taxi 5.77 3.99

Average Road Speed (kmph) 22.2 20

*Average PT trip lengths are estimated, and exclude walking distance

In 1993, approximately 30,116 trucks are observed to enter Greater Mumbai. Of these,

approximately half are destined to the Island City. A third of the trips originate from the north

(Gujarat) and 42% from the north-east (north Maharashtra, Delhi, and Calcutta). Less than

25% come from southern Maharashtra or other south Indian States. Due to the above shifting,

it is observed that the commercial vehicular traffic is slowly declining in the City areas while

the traffic on Express Highways and National Highways is growing. Public stage carriage bus

services are provided by BEST in MCGB area (and 20 km beyond the municipal boundary),

TMT in Thane , NMMT in Navi Mumbai, KDMT in Kalyan area , MBMT in Mira Bhayander

area and MSRTC elsewhere. The main skeleton of the rail network in Mumbai was laid down

over 100 years ago, initially to link Mumbai and adjacent townships. This network grew

rapidly. (TRANSFORM, 2005)

3.4.2 Alternate Growth Scenarios

The different growth scenarios have been developed based on distribution of population and

employment in different regions of MMR by TRANSFORM, 2005. Four alternate growth

scenarios (named as P1, P2, P3 and P4) with population share of Greater Mumbai as 40% to

60% are developed and, four alternate growth scenarios (named as E1, E2, E3 and E4) with

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y

29

employment share of Greater Mumbai as 33% to 75% are developed. The totals of sixteen

combinations of these scenarios are generated. Based on distribution of population and

employment among various areas in MMR, the other planning parameters are deduced and

are used in the model. The same is shown in the table 3.6. The sixteen possible combinations

of these scenarios are formed in which three short listed scenarios (P2E2, P3E3 and P3E4) are

evaluated in the present study.

Table 3.6 Range of Population and employment levels in MMR (TRANSFORM,2005)

CLUSTERS

POPULATION (IN LAKHS) EMPLOYMENT (IN LAKHS)

2005 2031

P1

2031

P2

2031

P3

2031

P4 2005

2031

E1

2031

E2

2031

E3

2031

E4

Island 33.9 54.4 47.8 40.8 37.4 22.6 40.3 36.2 28.4 20.5

Western 56.3 91.8 78.8 71.5 61.3 23.0 48.0 41.5 30.8 19.3

Eastern 38.4 61.2 53.6 47.6 40.8 11.4 21.5 19.3 14.4 11.1

Total Greater Mumbai 128.6 207.4 180.2 159.9 139.5 56.9 109.8 97.0 73.5 51.0

Thane 15.2 16.0 26.2 26.2 26.2 3.9 7.2 9.9 13.3 14.9

Navi Mumbai/CIDCO 15.0 22.8 33.0 33.0 39.8 5.9 10.0 12.1 17.5 22.3

Mira Bhayandar 6.3 13.6 13.6 13.6 13.6 1.5 2.6 2.5 3.9 5.0

Kalyan Dombivali 23.0 29.6 41.5 46.7 46.7 4.8 7.4 9.4 13.5 14.0

Bhiwandi 6.8 13.1 13.1 13.1 13.1 2.1 4.3 4.3 4.5 4.5

Vasai-Virar 7.1 13.1 13.1 14.8 18.2 1.6 2.4 4.1 7.2 9.1

Pen-SEZ 1.2 18.8 13.7 27.2 37.4 0.2 8.5 12.8 18.6 31.2

Rural 4.9 5.6 5.6 5.6 5.6 0.7 0.8 0.9 1.1 1.1

Total 208.2 340.0 340.0 340.0 340.0 77.6 153.0 153.0 153.0 153.0

3.5 Summary

The basic demographic aspects of the study area, outline of previous transport studies and

basic planning variables and their statistics are discussed here. The population and

employment related planning variables are only considered for the study. The different of land

use growth scenarios are also presented here, out of which the three short listed scenarios

adopted directly for the present study. Zoning system and its numbering which is adopted in

the present study is also given this chapter. The same zoning system is followed in our

modeling process also.

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y

30

Chapter 4

Methodology

4.1 General

This chapter describes the methodology adopted for evaluation of land use scenarios with the

development of travel demand model in the study area. Comprehensive Transportation study

for Mumbai Metropolitan (TRANSFORM-2005) is the starting point for this present

modeling exercise. The methodology adopted for evaluation of land use scenarios consists of

the following steps,

- Development of highway network and public transit network

- Updating the base year travel pattern

- Horizon year Travel Demand Model development

- Development of indices for the evaluation of different land use scenarios

- Evaluation of land use land use scenarios using the travel demand model w.r.t.

proposed transportation system performance perspective

The following sections briefly describe these steps.

4.2 Development of highway and public transit network

The network is developed from different shape files for all types of road network which are

available from MMRDA which have been used also for the TRANSFORM Study. The

network information generated was strategic consisting of major roads in the study area. The

road network was properly connected to all the zone centroids by means of centroid

connectors.

4.2.1 Highway network development

The total highway network is developed from the shape files in GIS based software tools. The

collection of shape files consists of local roads, arterials, sub-urban, metro and mono rail links

individually for existing and proposed links. The attributes associated with the shape file are

inadequate for the network development. Hence the required attributes are added to the

corresponding individual shape files using ArcGIS. Then they are converted to geographic

files and then they have been merged using TransCAD to form the single shape file consisting

of all the existing and proposed links. The network is developed from the resultant shape file

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y

31

using CUBE Base software. Now the resultant network file is lacking of centriod connectors

which can be developed through overlapping of network on the shape file zonal layers but

zonal numbers will be different from the same in TRANFORM Study. Hence the centriod

connectors information from the TRANSFORM study is integrated with all the links and

node’s data from the developed network and total full filled highway network is developed

using CUBE –Trips software.

4.2.2 Public Transport Network Development

Once the network is ready, the public transport lines should be coded on to the network. This

is done using the PUBLIC TRANSPORT program in CUBE- Voyager software for all the

public transport services available or present in entire MMR. The latest (2010) data on bus

routes, frequencies and fares etc. operated by BEST, TMT, NMMT, KDMT, MBMT,

MSRDC etc., in the study are collected and coded on to the network as per the Voyager’s

requirement. Similarly, the line wise information of all sub-urban trains like frequencies,

fares, etc. compiled from the latest time tables of western and central railways are coded on to

the network.

4.3 Updating Base year Travel pattern from the previous study

The main objective of this step is to develop validated mode-wise OD matrices for the base

year (2010). This comprises of the following steps.

- Updating the Base Year Network

- Trip Generation

- Trip Distribution

- Modal Split

- Trip Assignment on the Updated Network

The complete model development process is graphically represented in Fig.4.1.

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y

32

No

Yes

Figure 4.1 Methodology for updating base year travel pattern

The developed highway network is updated to the base year by identifying and

deleting some proposed and uncompleted links till now. The planning variables for the year

2005, 2016, 2031and the aggregate total population and employment are taken from

Projection of Planning Variables

{from TRANSFORM (2005)}

Calibrated trip-end equations purpose wise

{from TRANSFORM (2005)}

Gravity trip distribution model for internal

trips {from TRANSFORM (2005)}

Disaggregate mode choice models

{from TRANSFORM (2005)}

Mode wise peak hour OD Matrix

(PV, PT, TC, & CV) for 2010

Trip Assignment

Re-Calibrate the

Gravity Model with

adjusted trips

Proceed with the existing Gravity model

Commercial vehicle O-D

Matrix

External O-D matrix

Internal O-D matrix

Screen line counts

Growth factor methods

Validate the Link

flows with serene

line counts

Base Year (2010) Net Work

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y

33

TRANSFORM Study and updated to base year. Trip end models, distribution and modal split

models are taken from TRANSFORM study, to update the internal OD matrix for the base

year. The internal commercial vehicle trips are estimated from link counts using standard

matrix estimation procedure.

The External OD matrix of TRANSFORM (2005) updated to the base year, by using

the appropriate zonal growth factors. The mode-wise trip matrices obtained will be further

adjusted and assigned on the base year network. This helps in obtaining the link flows. These

modelled link flows will be validated against the observed link flows across the screen lines.

If the deviation exceeds specified limits, Gravity model will be re-calibrated. The thoroughly

validated O-D matrices can be used for model development.

4.4 Horizon year Travel Demand Forecasts

The Updated Travel Demand Model is used to forecast the Horizon Year loadings on each

mode on all the links. Future forecasts would be done for the Horizon year 2031. The

planning variables for the year 2031 and the aggregate total population and employment are

taken from TRANSFORM Study and forecasted for all Horizons. The planning variables of

horizon year form the input to the Travel demand model along with the future network. Trip

ends are estimated and are fed into the existing / re-calibrated gravity model along with base

year highway skims. The distributed PA matrix so obtained is fed into the Mode split model

and mode wise PA matrices are estimated. This forms the internal portion of the PA matrix.

The external passenger PA portion as well as Commercial vehicle trips are estimated by

Furness method and added to the horizon year internal matrices. The combine PA matrix is

converted into an OD matrix and is loaded on to the highway and PT networks. Skims

obtained from this assignment process are updated in the gravity model and redistribution of

trips is done. Mode wise OD matrices are estimated by the updated skims. The final matrices

thus produced are loaded on to the network and the cycle is continued till the skims are stable.

The models are developed for morning peak period because, for public transport morning

peak flows are critical from the transportation network supply point of view.

4.5 Development of indices for the evaluation of different growth scenarios

Indices are developed at the system level by following the standard principles which are

representing the impacts and transportation system’s performance as well and also which are

quantifiable from our model. The impacts such as economical, environmental and safety

impacts are all considered for developing the indices.

The transportation system consists of four major components such as,

1. User

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y

34

2. Vehicle

3. Environment

4. Transport Network

The impacts on or of total transportation system depend mainly on the interaction between the

above mentioned components. Hence the total transportation system performance can be

evaluated by studying those impacts. While identifying the impacts, it is necessary see that the

impacts are considered from all the important aspects like economic, social and

environmental. The selected performance indicators are listed below.

The indicators set is selected by following the guidelines listed in section 2.5.1 and by

ensuring that they represent impacts from all the important aspects like urban transportation

infrastructure performance, travel behaviour of public transport user, environment, safety,

economical…etc. The selected indicators which can be used in the present study are listed

below.

• Accessibility to the Public transit stops

• Total public transport user cost

• Traffic Congestion

• Transportation Safety

• Average trip length and speeds by Public Transport (PT) and Private Vehicles (PV)

• Vehicles Kilometers or Passenger kilometers travelled

• Transportation network length per trip

• Total Cost of Proposed Transportation Infrastructure

4.6 Evaluation of Land use Scenarios using Travel Demand Model

The planning variables for the different land use scenarios are collected and they are given

input to the implemented working travel demand model for the horizon year 2031. The

evaluation indices for the different land use scenarios are quantified using the model for the

horizon year 2031. The scenarios are evaluated in both the perspectives (transportation system

performance and environmental) using the formulated procedure of MCDM. The outline of

evaluation procedure is shown in the figure 4.2.

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y

35

Figure 4.2 Formulation of procedure for Evaluation of land use scenarios w.r.t. transportation

system performance

4.6.1 Formulation of MCDM approach for evaluation

A particular scenario may rank high when evaluated using one indicator but low when ranked

with the other indicator. Hence it is highly necessary to find the total transportation system

performance index relatively which can be the linier function of the selected quantifiable

indicator set. Hence it is decided that the relative weightages should be given for each

indicator from rating survey for all the indicators that are selected.

4.6.1.1 Rating survey on Performance Indicator set

It is proposed to receive the rating for each indicator out of 10 marks. The respondents are

asked to give the marks (out of 10) for each indicator depending on the dependability on that

Identification of Impacts

Identification of Indicators which reflects the

impacts

Selection appropriate and quantifiable indicators

Rating Survey for assigning weightages to the

indicators

Choosing the Best Growth Scenario

Analysis of Different scenarios (by calculation of

RTPI’s) using the computed indicators

Measuring or computation of selected indicators

using the Developed Travel demand Model

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y

36

particular indicator to evaluate the transportation system performance. The samples are taken

from all the interested groups or stake holders such as researchers, transportation industry

professionals and also the common typical Public Transport users. The importance of the

survey and the way to give the rating for each alternative is explained to the respondent prior

to the survey.

A typical data sheet will look like as following table 4.1, however the original data sheet is

attached in the appendix.

Table 4.1 A typical data sheet for the Rating and Ranking survey

Indicator I II III IV V VI VII VIII IX

Rating

Ranking

*All the marks should be given for out of 10

Depending on the cumulative rating score for each indicator the relative weightage can be

calculated.

Rating Score of the indicator i =Wi = Average score obtained for that indicator I as tabulated

in the table 4.2.

Table 4.2 Sample data sheet for Calculation of Aggregate rating score for each indicator

Indicator I II III IV V VI VII VIII IX

Sample1

Sample2

Sample3

Sample4

Sample5

Sample6

avg avg avg avg avg avg avg avg avg

4.6.1.2 Computation of Relative Transportation system Performance Index (RTPI)

The main problem in the evaluation of urban transportation system performance, the absolute

index cannot be given for particular transportation system. Hence the relative transportation

system performance index can be computed and used for the evaluation.

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y

37

Relative score is given for all the scenarios based on measure of the each indicator and

measure is computed from the model.

Relative score of a scenario j with respect to indicator i = 100-(% difference with the best

measure among the scenarios)

Then that relative score is then modified by the average rating score of that indicator as

below.

Let, Relative weighted score for a scenario j w.r.t indicator i = RWji

RWji = Relative score of a scenario j * average rating score of that indicator i

Relative Transportation System Performance Index for Scenario j =RTPIj= ∑ RWji

The highest RTPI can represent the best transportation system performance among all

the obtained RTPI’s. By comparing the obtained RTPI for all the scenarios the best scenario

(with highest RTPI) can be chosen.

4.7 Summary

The brief step by step procedure to achieve the objectives is explained in this chapter with the

help of flow chart to achieve the objectives of the present study. The Development of travel

demand model is explained in the next chapter. .

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y

38

Figure 4.3 Methodology for Evaluation of Land use scenarios for the horizon years using

Travel Demand Model

Projection of Planning Variables

For the selected land use scenario

Apply trip-end equations and obtain future year

trip-ends of internal trips

Apply calibrated gravity model and obtain O-D

matrix for internal trips

Previous cost/time skims

for initial run

Measure the Evaluation Indices for this scenario

Assignment of PT passenger trips on to the

public transport network

Assignment of peak-hour PCU trips on road

network taking peak-hour PT & truck PCU

flows as preloads

Matrix of PT (Bus + Rail+ Taxi) Passenger

trips for AM peak period

AM peak matrices of PV trips in PCU

Apply mode choice model and obtain PT, car

and two-wheeler O-D matrices of passenger

internal trips

Truck matrix and mode-wise

external O-D matrices by

Furness method

Regional peak hour to daily

flow ratios, Passenger - PCU

conversion factors

Link costs stable?

Road network data and PT

network data for the scenario

under consideration

Selection of Land use

Scenarios

Travel forecast is over for

every scenario?

No

• Comparing of different scenarios

• Suggest the Best Scenario for the proposed

Transportation system in the horizon year

Evaluation Criteria

(RTPI)

Yes

Yes

No

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y

39

Chapter 5

Travel Demand Model Development

5.1 General

Transportation is an important infrastructure in shaping the city. The changes in the policies in

land use and other economic activities influence the systems design. Hence it has become

important to periodically asses the travel demand taking into consideration of past

developments and current requirements. Travel demand models are used to determine the

amount of travel on the given network at any point of time. Travel forecasting models are

used to predict the change in travel pattern, magnitude and utilization of transportation system

in response to the changes in the regional development, demographic and transportation

system. The development process is discussed in brief in the subsequent sections in this

chapter.

5.2 Updating Base year Travel pattern from the previous study

As the network development is done directly for the horizon year 2031, the base year travel

pattern is not updated but it is assumed to be updated as we are using the calibrated

parameters from the TRANSFORM study. Hence it is assumed that the base year travel

pattern is also validated using the screen line counts in the base year 2005.

5.3 Network Development

Transportation network development includes the development of both the physical highway

network and coding of public transport lines on to the network. These are explained in brief in

sub sequential steps.

5.3.1 Highway Network Development

The MMR consists of very wide range of transportation network which consisting of the

following,

- Local or Arterial road network (existing and proposed)

- Free ways (existing and proposed)

- Sub-urban rail network (existing and proposed)

- Metro rail (existing and proposed)

- Mono Rail network

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y

40

The road network inventory carried out during the TRANSFORM study identified 16

different types of road links. The link type of 17 is also added now which is associated with

centroid connectors. All these links types were classified by a factor called VDF (Volume

Delay Function). The same is coded as link type in CUBE and all the links in GMR are

extracted from the CTS study which comprises of the whole MMR region network. Table 5.1

shows the different types of road links with their link characteristics.

Table 5.1 Different types of road links with their link characteristics (TRANSFORM, 2005)

Link

Type Link Configuration

Divided/

Undivided Type of flow Capacity

Free flow

speeds(kmph)

Bus PV 1 2/3 lane Undivided One way 1500 25 30

2 2/3 lane Undivided Two way 1250 25 45

3 2 lane (Flyover) Undivided One way 1750 NA 60

4 4 lane(effective 2 lane) divided Two way 950 20 30

5 4 lane Undivided Two way 1150 30 47

6 4 lane divided Two way 1500 30 40

7 6 lane divided Two way 1500 40 64

8 6 lane(Flyover) divided Two way 2000 40 72

9 8 lane divided Two way 1750 40 55

10 10 lane divided Two way 2000 40 50

11 10 lane(ser road) Undivided Two way 2000 40 60

Regional

12 2/3 lane Undivided Two way 1100 40 65

13 4 lane NH divided Two way 1600 60 60

14 4/6 lane Bypass divided Two way 1600 60 88

15 Experss way divided Two way 1600 70 90

16 Long Bridge divided Two way 2000 45 60

17 Centroid connectors - Two way 9999 3

21 Suburban Rail - Two way 9999 30

22 Metro Rail - Two way 9999 33

23 BRTS - Two way 9999 40

24 Rail to highway

connector

- Two way 9999 5

25 BRT to highway

connector - Two way 9999 5

5.3.2 Available Data Set

The highway network is developed basically from raw shape files available from the

MMRDA. The collection of different raw shape files consists of local links, arterial links,

existing freeway and freeway links with proposed, metro links, proposed metro links, and

suburban railway links individually for the year 2031 which have been shown in the figure

5.1, figure 5.2 and figure 5.3. We have observed that both local links and arterial links shape

files are having many links in common. All the shape files are having the attributes of From

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y

41

Node, To Node and most of the links of some shape files are not having Link Type. Also the

links information is available with it’s from node, to node and link type from the

TRANFORM Study (MMRDA). Many of these links are not present in the shape files. We

know that the network development from the shape files will be more accurate and reliable

and hence the network development proceeded with the shape files.

Figure 5.1 Local and arterials links Figure 5.2 freeway links Figure 5.3 suburban rail

5.3.3 Creation of GIS Database

The separate data base with all the required attributes is created for the each individual shape

file. The procedure for the creation of database for each shape file is followed as follows,

i. The attribute data is extracted into Excel from the shape file in GIS based TransCAD

software.

ii. All the links are examined once weather all the links are associated with link types or

not. If not, they were separated out with it’s from node and to node.

iii. Those links are compared with the link data available from TRANSFORM Study

w.r.t. to their form and to nodes, and then the corresponding link types or Volume

Delay Function (VDF) numbers are assigned for those links using a C programme.

iv. These links are again added to the list links extracted from the shape files.

v. The other attributes like capacity, speed are generated for all the links by using the

look up table 6.1.

vi. The prepared link data is added to the shape file as attributes in ArcGIS software.

The above procedure is followed for all the shape files available. Now the every shape file is

ready with all the required attributes.

All these shape files were converted to geographic files (.dbd extension files) in

TransCAD. These geographic files were merged and formed into a single shape file consisting

of all the links except mono rail links because of lack of mono rail link shape file. The

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y

42

resultant shape file is not geo referenced one, hence it is geo referenced by using the rubber

sheeting tool by taking the sub urban railway stations as the reference. The exact latitude and

longitude of the reference points are taken from the Google earth. Hence the resultant shape

file is the geo referenced one which is shown in the figure 5.4. Then it is overlapped with

Google earth and the major existing links are cross checked Google network.

Figure 5.4 The geo referenced shape file of total MMR network developed in TransCAD

5.3.4 Building the Highway Network

The shape file developed from the TransCAD cannot be used for the travel demand modeling

in CUBE software. Hence it has to be converted into the network file. We are having three

options in the CUBE to develop the network file,

i. We can develop the network from the shape files in CUBE base.

ii. We can develop the network from the ASCII files having the node and the link data as

per the CUBE Trips software requirement.

iii. We can develop the network from the geo data base files (.dbf extension files) of link

and nodes in CUBE Base environment.

In the first option the network will be developed very easily by giving the input as our shape

file and specifying A-node and B-node, Distance and Direction fields of the shape file. The

centriod connectors have to be generated by overlapping the network on the zonal layer shape

file but that is not having the zonal numbers as per the CTS Study. Also it will not connect all

the possible or accessible road network with the zone centriod. Hence this option is violated.

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y

43

The link and node data are extracted from the shape file and they are added to the link data of

the centriod connectors taken from the TRANSFORM Study then the network is built through

the ASCII files by using the MVNET programme of CUBE Trips software which is then

converted to link and node geo data base files using the NETWORK programme of CUBE

Voyager. Then the Voyager network of total length of 6918 Km for horizon year 2031 is

developed from the .dbf files of link and node data using the NETWORK Programme. The

process is shown in the figure 5.5.

Figure 5.5 Process for the development of network for MMR on CUBE Voyager Platform

The highway network developed from the above process along with the attributes of a link is

shown in the figure 5.6. Each link in the network is having the attributes like,

1. A-Node (From Node),

2. B- Node (To Node),

3. Distance (length of the link in Km),

4. Link type,

5. Jurisdiction Code,

6. Capacity Index,

7. Time or Speed Flag (S or T) representing free flow travel time or speed on that link

8. Time or Speed in minutes or Kmph

9. Capacity of the link

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y

44

Figure 5.6 Developed highway network for the horizon year 2031

The base year network can be deduced from the developed network by deleting the proposed

links which have been added while developing the shape files.

5.4 Public Transport Network Development

Once the highway network is ready, the public transport lines should be coded on to the

network. This is done using the PUBLIC TRANSPORT program in CUBE-Voyager software

and it is shown in Fig 5.7. The public transport consists of the following modes.

Public Transport in MMR

BEST NMMT TMT KDMT MBMT MSRTC AUTO/TAXI SUB-URBAN METRO MONO

By Road By Rail

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y

45

Figure 5.7 Public Transport network development in cube voyager

The route is the series of road links over which the transport service travels. In this process of

development of public transport, each line is coded exogenously or hard coded along all nodes

on which it is running. The appropriate line attributes are supplied for each line such as Line

Name, Line headway, capacity, crowding curve number etc.

5.4.1 Bus Network

Bus route Network of Mumbai Metropolitan Region consists of several bus services operated

by different Municipal corporations. They are,

i. BEST Bus service operated by Municipal Corporation of Greater Mumbai (MCGM)

ii. NMMT Bus service operated by Navi Mumbai Municipal Corporation (NMMC)

iii. TMT Bus service operated by Thane Municipal Corporation (TMC)

iv. KDMT Bus service operated by the Kalyan-Dombivli Municipal Corporation

(KDMC)

v. MBMT Bus service operated by the Mira- Bhayander Municipal Corporation

(MBMC)

vi. MSRTC Inter City Bus service operated by the Maharashtra State Road Transport

Corporation. It is not coded due to the time constraint.

All the buses are updated to the base year 2010. All the bus routes are taken from different

sources are hard coded by hand, relating them to the developed highway network. All the bus

stops are identified on the network and are coded relevantly. Once the hard coding of the bus

routes is complete, these are converted into a format which is amenable to cube software and

are overlaid on the developed highway network. The details of routes or lines of different bus

service are summarized in the table 5.2. The coded PT network is shown the figure 5.8.

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y

46

Table 5.2 Summary of bus route network available in MMR

Bus Service Total number of

routes or lines

Source of Details

BEST 400 BRTS Office and its website

NMMT 44 NMMC Office and its website

TMT 62 TMC Office

KDMT 46 KDMC Office and website

MBMT 17 MBMT Office

MSRTC NA MSRTC Office and website

Figure 5.8 The PT network coded with all the routes

5.4.2 Sub urban Rail Network

The suburban rail network in Mumbai consists of Central, Western and Harbour lines. All the

three lines are coded on the network. Rail links are coded as a separate link type (Link type

=21) because only trains ply on them. All the links are specified as a part of the link data file

Access or egress leg

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y

47

specified in the network building phase. Now, the lines are specified along with their

frequencies which are calculated from the number of services in peak period. The peak period

is taken as the duration from 18:00 to 20:00 in the evening for down movements from island

city and 09:00 to 11:00 in the morning for up movements to the island city. The frequency

data for all the routes is derived from the Mumbai Local train guide book which is shown in

the figure 5.9. The Public Transport network file showing all the coded suburban lines is

shown in Fig. 5.8.

Figure 5.9 Mumbai Local Train information pocket guide

Lines have been coded separately for Fast and Slow services. Also the lines are classified

based on their capacities (9 car rakes and 12 car rakes) and number of services during peaks.

5.4.3 Metro Rail Network

Though metro service does not exist in the base year, it is coded along with other public

transport lines. While doing the assignment for horizon years, the lines relevant to that

particular horizon are switched ON while all other lines are kept off. Same as the Suburban

network Metro is also coded on a different link type (Link Type = 22). The metro rail network

is shown in Fig. 5.10.

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y

48

Figure 5.10 Metro Rail network for 2031 year

5.4.4 Mono Rail Network

Mono Rail network is coded similar to metro network to be switched on in the relevant

Horizon years. Link Type for Monorail is not coded separately because monorail occupies a

portion of the existing road only. The typical information supplied for a route or line is also

shown in the figure 5.11.

5.4.5 BRT (Bus Rapid Transit) Network

The proposed BRT routes identified will be coded on to the CUBE network, same as BEST

buses are coded.

5.4.6 Fare Tables and Wait curves

Every line in the public transport network should be allocated with its corresponding fare

table and wait curves. Fare tables are taken from the website directly for all the buses and

suburban rails and they are supplied to the CUBE Network as the Voyager’s requirement.

Sample fare table is shown below.

FARESYSTEM NUMBER=1,

LONGNAME="Distance_Based1 Fare for BEST Ordinary Buses",

NAME="DISTFARE_BEST_ORD",

STRUCTURE="DISTANCE” SAME="CUMULATIVE",

IBOARDFARE=0.50,

FARETABLE=0-3,2-3,3-4,5-5,7-6,10-8,15-10,20-12,25 -14,30-15,35-16,

40-18, 45-20

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y

49

Figure 5.11 Attributes of a typical Public transport route in CUBE

The wait curves and crowd curves are defined in the same programme of PUBLIC

TRANSPORT in CUBE Voyager. The sample wait curve definition is as follows,

;DEFINITION OF WAIT CURVES WAITCRVDEF NUMBER=1 LONGNAME="InitialWait" NAME="In itWait" , CURVE=1-0.5,16-8,27-12,48-15, 160-20 WAITCRVDEF NUMBER=2 LONGNAME="TransferWait" NAME="X ferWait" , CURVE=1-0.5,4-2,12-6,20-8, 40-15,60-20 CROWDCRVDEF NUMBER=1 NAME="For Buses", CURVE=0-1.43,54-1.65,95-1.87,100-3.74

5.4.7 Creation of Access/Egress and Transfer links

Access or egress legs are the collection of one or more links from the zone centriod to the

nearer transit stop. Access/Egress and transfer links are created using the scripting language

(similar to syntax of C Language), to facilitate the access to the zones and transfer between

the different public transport services. Limiting radius for generating these Non transit legs is

given for each mode differently.

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y

50

5.5 Generation of Initial Highway and Public Transport Skims

After building both the highway and public transport network, the highway network is utilized

to generate the initial free flow travel time and distance skims to be given as input to the

demand stage i.e. for trip distribution stage. With the generated connectors for a transit line in

the PT network, the route-enumeration process enumerates routes for each origin zone

connecting to the line via a valid access leg with respect to the factors or parameters given

below. The process is implemented by specifying the appropriate script and input files for

each programme and those programmes used for this step in CUBE are shown in the figure

5.12.

Figure 5.12 Generation of initial highway and PT skims in CUBE voyager platform

1. Maximum number of transfers for a route

MAXFERS=3

2. Number of transfers at which the program stops exploration of less direct routes.

EXTRAXFERS1 = 2

3. Maximum number of transfers explored in excess of the number of transfers required

by the minimum-cost route.

EXTRAXFERS2 = 1

4. SPREAD defines an upper cost limit for routes between an O-D pair.

SPREADFUNC=1, SPREADFACT = 1.05

As shown in the figure 5.12, the enumerated routes are used for the route evaluation in which

the cost of the best route is skimmed based on their probability of use. Then the skim matrices

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y

51

are generated which are required for the computation of utility functions in the Modal split

step. The generated skim matrices are listed below.

IVTT_Train - In Vehicle Travel Time by Train

IVTT_Bus - In Vehicle Travel Time by Bus

IVTT_IPT - In Vehicle Travel Time by IPT

IVTT_PV - In Vehicle Travel Time by either Car or TW both of which is same

IVTC_Train - In Vehicle Travel Cost by Train

IVTC_Bus - In Vehicle Travel Cost by Bus

TROVDI - Out of vehicle distance traveled in case of train (access + egress)

BOVDI - Out of vehicle distance traveled in case of bus (access + egress)

5.6 Trip Generation

The trip generation model is the first of the four models of the four step travel demand

modelling process. The trip generation model estimates the number of trips produced and

attracted to each of the TAZ. The trip end models developed during TRANSFORM Study

(2005) for Mumbai Metropolitan Region are adopted here. The trips produced are estimated

from the household socio-economic and trip making characteristics. The trip attractions are

estimated from type of employment categorized in each zone.

Trip end models are classified by six purposes namely,

1. Home Based Work-Office (HBWF)

2. Home Based Work-Industries (HBWI)

3. Home Based Work-Other (HBWO)

4. Home Based Education (HBE)

5. Home Based Other (HBO)

6. Non Home Based (NHB)

Trip End Model excluding walk trips captures the intra study area trips (i.e.) internal to

internal (I-I) made by the residents of the study area for morning peak i.e. 6 a.m to 11 a.m.

Multiple Regression technique was adopted to develop these models. The models adopted for

trip production and trip attraction are shown in table 5.3 and table 5.4.

Table 5.3 Trip Production Model (excluding walk) for various purposes during morning peak

(TRANSFORM, 2005)

Purpose Model R2 t SEE F

HBWF-AM =0.743 RWF 0.90 38.10 4815 1452

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y

52

Purpose Model R2 t SEE F

HBWI-AM =0.420 RWI 0.81 26.66 2857 711

HBWO-AM =0.286 RWO 0.85 30.49 3907 929

HBE-AM =0.153 RS 0.81 26.85 3312 721

HBO-AM =0.014 PoP 0.69 19.57 1574 383

NHB-AM =0.002 EBZ 0.19 6.26 297.8 39

Where,

POP : Population

RWF : Resident Worker – Office category

RWI : Resident Worker – Industry category

RWO : Resident Worker – Other category

RS : Resident Student

EBZ : Employment by Zone

HBWF-AM : Home based work-office trip generation during AM peak period

HBWI-AM : Home based work-industry trip generation during AM peak period

HBWO-AM : Home based work-other trip generation during AM peak period

HBE-AM : Home based education trip generation during AM peak period

HBO-AM : Home based other trip generation during AM peak period

NHB-AM : Non-home based all purpose trip generation during AM peak period

The models are statistically significant except the Non home based trips. This is

because of the fact that, it was not possible to capture the NHB trips through any of the

standard socio-economic variables. Since, the percentage of NHB trips in total trips is

marginal (0.39%) and their estimation may not cause significant errors in the network flows.

Table 5.4 Trip Attraction Model (excluding walk) for various purposes during morning peak

(TRANSFORM, 2005)

Purpose Model R2 t SEE F

HBWF-AM =0.742 OJ 0.94 52.27 4999 2838

HBWI-AM =0.477 IJ 0.90 39.16 2363 1535

HBWO-AM =0.293 OtJ 0.88 35.67 3526 1272.8

HBE-AM =0.212 OtJ 0.71 20.58 4429 423

HBO-AM =0.006 Pop + 0.019 EBZ 0.72 5.30, 7.56 1371 215.5

NHB-AM =0.002TJ 0.23 7.19 287.7 51.7

Where,

OJ : Office Jobs/Employment by zone

IJ : Industry Jobs/Employment by zone

OtJ : Other Jobs/Employment by zone

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y

53

TJ : Total Jobs/Employment by zone

Pop : Population by zone

EBZ : Employment by Zone

HBWF-AM : Home based work-office trip attraction during AM peak period

HBWI-AM : Home base work-industry trip attraction during AM peak period

HBWO-AM : Home base work-other trip attraction during AM peak period

HBE-AM : Home base education trip attraction during AM peak period

HBO-AM : Home base other trip attraction during AM peak period

HBWF-AM : Home base work-office trip attraction during AM peak period

By using these regression models and the data set of required planning variables the trip

productions and attractions are computed using the GENERATION programme of the CUBE

Voyager for all the purposes by coding the appropriate script. The programme structure is

shown in the figure 5.13.

Figure 5.13 Implementation of Trip Generation, Distribution and Modal split step in Voyager

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y

54

5.7 Trip Distribution Models

Once the trip productions and attractions for each zone are computed, the trips are distributed

among the zones using Trip Distribution Models in the form Origin destination matrices. A

doubly constrained gravity trip distribution model is used for distributing passenger trips.

A Gravity Trip Distribution model of the following form is used for distributing the

total internal passenger trips.

ijjjiiij FDBOAT = (5.1)

Where,

∑=

j

ijjj

iFDB

A1

=

i

ijii

jFOA

B1

(5.2)

Fij = the deterrence function

tij= Highway travel time from i to j = Friction factor

ijT = Number of trips between zones i and j.

α = Calibration parameter – power function

β = Calibration parameter – exponential function

These parameters are not calibrated in the present exercise but the calibrated gravity

trip distribution models for all six purposes from the TRANSFORM study (2005) are adopted.

Tanner’s distribution was used in that study for the friction factors and the Gravity model

parameters used for trip distribution are given in table 5.5

Table 5.5 Gravity model parameters used for trip distribution (TRANSFORM, 2005)

Purpose Type of Function Coincidence Parameters

Ratio α β

HWF

0.90 - 1/34.90757

HWI

0.89 - 1/28.318647

HWO

0.90 - 1/26.863928

HBE

0.79 0.001 1/20.484823

HBO

0.73 0.001 1/3.4244816

NHB

0.76 0.001 1/2.9021146

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y

55

The productions and attraction for each zone are supplied as inputs and the script for the

gravity model calculations are coded as required for the DISTRIBUTION programme in

Voyager to implement the trip distribution as shown in the figure 5.12. The output of the

programme i.e PA matrix for each purpose is then supplied to the modal split step for the

corresponding purpose.

5.8 Modal Split Models

For this study a simple Multi-Nomial Logit (MNL) mode choice models for morning peak

period without walk trips are used from TRANSFROM study. Table 6.6 lists all the mode

choice models used for the MMR. The total trips from the distribution step are are divided

into captive riders (70% - PT) and choice riders (30%) first for each purpose. The MNL

applied for the choice riders is shown in the figure 5.14. Then the modal split equations are

applied for those choice riders separately for Island and non-Island city.

MNL Model, Island City, Vehicle Available MNL Model, Non-Island, Vehicle Available

Figure 5.14 Summary of Mode Choice Model Structures: Without Walk (TRANSFORM,

2005)

Then the PT users from the choice riders are accumulated and added to the captive

riders. Hence the whole trips are here being divided into PT and PV trips. The adopted Mode

Choice Models for MMR are shown in the table 5.6.

Table 5.6 Proposed Mode Choice Models for GMR (Morning Peak without Walk)

(TRANSFORM, 2005)

Model Availability

of Vehicle

Locat

ion

Rho

Square Utility Equations

HBW-

Employed

in Office

Vehicle

Available

Island

City

0.33

U (Train) = -0.0553*IVTTTrain-0.0192*IVTCTrain-0.485*TROVDI

U (Bus) = 0.08-0.0553*IVTTBus-0.0192*IVTCBus-0.485*BOVDI

U (IPT) = -4.621-0.0553*IVTTTaxi-Rickshaw-0.0192*IPTCOST

U (PVT) = 3.458-0.0553*IVTTCar-TW-0.0192*PVTCOST

U (Metro) = 0.900-0.0553*IVTTmet-0.0192*metCOST-0.97*MOVDI

Non-

Island

0.41 U (Train) = -0.0252*IVTTTrain-0.0148*IVTCTrain-0.1255*TROVDI

U (Bus) = -1.100-0.0252*IVTTBus-0.0148*IVTCBus-0.1255*BOVDI

U (IPT) = -3.8619-0.0252*IVTTTaxi-Rickshaw-0.0148*IPTCOST

U (PVT) = 3.699-0.0252*IVTTCar-TW-0.0148*PVTCOST

U(Metro) = 0.600-0.0252*IVTTmet-TW-0.0148*metCOST-0.251*MOVDI

HBW-

Employed

Vehicle

Available

Island

City

0.44 U (Train) = -0.0506*IVTTTrain-0.0269*IVTCTrain-0.3521*TROVDI

U (Bus) = -0.51-0.0506*IVTTBus-0.0269*IVTCBus-0.3521*BOVDI

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y

56

Model Availability

of Vehicle

Locat

ion

Rho

Square Utility Equations

in Industry U (IPT) = -40.353-0.0506*IVTTTaxi-Rickshaw-0.0269*IPTCOST

U (PVT) = 4.0775-0.0506*IVTTCar-TW-0.0269*PVTCOST

U (Met) = 1.00-0.0506*IVTTmet-0.0269*metCOST-.7042*MOVDI

Non-

Island

0.37 U (Train) = -0.0208*IVTTTrain-0.0150*IVTCTrain-0.1354*TROVDI

U (Bus) = -0.63-0.0208*IVTTBus-0.0150*IVTCBus-0.1354*BOVDI

U (IPT) = -3.5727-0.0208*IVTTTaxi-Rickshaw-0.0150*IPTCOST

U (PVT) = 3.987-0.0208*IVTTCar-TW-0.0150*PVTCOST

U (Met) = 0.900-0.0208*IVTTmet-0.0150*metCOST-.2708*MOVDI

HBW-

Employed

in Others

Vehicle

Available

Island

City

0.48 U (Train) = -0.0626*IVTTTrain-0.0254*IVTCTrain-0.126*TROVDI

U (Bus) = 1.04-0.0626*IVTTBus-0.0254*IVTCBus-0.126*BOVDI

U (IPT) = 0.0001-0.0626*IVTTTaxi-Rickshaw-0.0254*IPTCOST

U (PVT) = 2.0647-0.0626*IVTTCar-TW-0.0254*PVTCOST

U (Met) = 2.512-0.0626*IVTTmet-0.0254*metCOST-0.252*MOVDI

Non-

Island

0.50 U (Train) = -0.0325*IVTTTrain-0.0169*IVTCTrain-0.1571*TROVDI

U (Bus) = -1.34-0.0325*IVTTBus-0.0169*IVTCBus-0.1571*BOVDI

U (IPT) = 0.0001-0.0325*IVTTTaxi-Rickshaw-0.0169*IPTCOST

U (PVT) = 4.378-0.0325*IVTTCar-TW-0.0169*PVTCOST

U (Met) = 1.178-0.0325*IVTTmet-0.0169*metCOST-0.3142*MOVDI

HBE Vehicle

Available

Island

City

0.38 U (Train) = -0.0733*IVTTTrain-0.0256*IVTCTrain-0.8412*TROVDI

U (Bus) = 1.400-0.0733*IVTTBus-0.0256*IVTCBus-0.8412*BOVDI

U (IPT) = -4.369-0.0733*IVTTTaxi-Rickshaw-0.0256*IPTCOST

U (PVT) = 1.529-0.0733*IVTTCar-TW-0.0256*PVTCOST

U (Met) = 0.029-0.0733*IVTTmet-TW-0.0256*metCOST-

1.6824*MOVDI

Non-

Island

0.14 U (Train) = -0.0349*IVTTTrain-0.0160*IVTCTrain-0.1737*TROVDI

U (Bus) = 0.45-0.0349*IVTTBus-0.0160*IVTCBus-0.1737*BOVDI

U (IPT) = 2.029-0.0349*IVTTTaxi-Rickshaw-0.0160*IPTCOST

U (PVT) = -0.001-0.0349*IVTTCar-TW-0.0160*PVTCOST

U (Met) = -0.516-0.0349*IVTTmet-0.0160*metCOST-.3474*MOVDI

HBO Vehicle

Available

Island

City

0.16 U (Train) = -0.0405*IVTTTrain-0.0170*IVTCTrain-0.272*TROVDI

U (Bus) = 0.24-0.0405*IVTTBus-0.0170*IVTCBus-0.272*BOVDI

U (IPT) = 0.00012-0.0405*IVTTTaxi-Rickshaw--0.0170*IPTCOST

U (PVT) = 2.7592-0.0405*IVTTCar-TW-0.0170*PVTCOST

U (Met) = 1.459-0.0405*IVTTmet-0.0170*metCOST-0.544*MOVDI

Non-

Island

0.28 U (Train) = -0.0221*IVTTTrain-0.0130*IVTCTrain-0.1760*TROVDI

U (Bus) = -0.6200-0.0221*IVTTBus-0.0130*IVTCBus-0.1760*BOVDI

U (IPT) = 0.00012-0.0221*IVTTTaxi-Rickshaw-0.0130*IPTCOST

U (PVT) = 2.8720-0.0221*IVTTCar-TW-0.0130*PVTCOST

U (Met) = 1.000-0.0221*IVTTmet-0.0130*metCOST-0.352*MOVDI

NHB Vehicle

Available

Island

City

0.16 U (Train) = -0.0405*IVTTTrain-0.0170*IVTCTrain-0.272*TROVDI

U (Bus) = 0.24-0.0405*IVTTBus-0.0170*IVTCBus-0.272*BOVDI

U (IPT) = 0.01-0.0405*IVTTTaxi-Rickshaw--0.0170*IPTCOST

U (PVT) = 3.7592-0.0405*IVTTCar-TW-0.0170*PVTCOST

U (Met) = 1.459-0.0405*IVTTmet-0.0170*metCOST-0.544*MOVDI

Non-

Island

0.28 U (Train) = -0.0221*IVTTTrain-0.0130*IVTCTrain-0.1760*TROVDI

U (Bus) = -0.6200-0.0221*IVTTBus-0.0130*IVTCBus-0.1760*BOVDI

U (IPT) = 0.01-0.0221*IVTTTaxi-Rickshaw-0.0130*IPTCOST

U (PVT) = 3.8720-0.0221*IVTTCar-TW-0.0130*PVTCOST

U (Met) = 1.000-0.0221*IVTTmet-0.0130*metCOST-0.352*MOVDI

Where,

IVTTTrain - In Vehicle Travel Time by Train

IVTTBus - In Vehicle Travel Time by Bus

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y

57

IVTTTaxi-Rickshaw - In Vehicle Travel Time by either Taxi or Rickshaw both of

which is same

IVTTCar-TW - In Vehicle Travel Time by either Car or TW both of which is same

IVTCTrain - In Vehicle Travel Cost by Train

IVTCBus - In Vehicle Travel Cost by Bus

IPTCOST - Weighted in vehicle travel cost of Rickshaw and Taxi

PVTCOST - Weighted in vehicle travel cost of Car and TW

TROVDI - Out of vehicle distance traveled in case of train (access + egress)

BOVDI - Out of vehicle distance traveled in case of bus (access + egress)

By using the utility equations specified in table the modal split computation steps are

performed by coding the suitable script for all the purposes using the programme MATRIX in

Voyager. Then these matrices for all the purposes are merged to a single PA matrix of PV and

PT. This matrix is for the morning peak period of 5 hours. That PA matrix for private vehicles

and PT is converted to the OD and then into peak our matrix by converting them to PCU and

passenger trips respectively as shown in the figure 5.12.

5.9 Highway and Public Transport Assignment

5.9.1 Public Transport Assignment

Peak hour public transport passenger matrix, which includes trips made by bus, suburban train

and Intermediate Public Transport (IPT), was assigned on to the public transport network. The

public transport assignment is done based on generalized time (GT) units of each mode. In the

present study, the direct cost or fare has been converted into time units by assuming the

appropriate value of time (VOT). The stochastic user equilibrium algorithm is utilized for the

public transport assignment. For software input preparation, VOT is given for all the transit

modes as same as Rs.27 /hr and Rs.100 /hr for freeways (only western freeway sea link) based

on base year.

The assignment process enumerates all the options and assigns the trips using a logistic choice

function based on GT.

GT = IVTT + WTFAC*WT +TRFAC*NTR + WKTFAc*WKT + FARE / VOT + DF

Where,

IVTT - In-vehicle travel time FARE / VOT - Fare / Value of time

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y

58

WT - Waiting time NTR - Number of Transfers

WKT - Walk time DF - Discomfort

Discomfort is taken care by defining different multiple crowding curves for different PT

modes

The initially prepared public transport network and peak hour passenger trip matrix

which is obtained from the Modal split step are given as the input to the PUBLIC

TRANSPORT programme and necessary script is coded implement the public transport

assignment. Then the matrix manipulations have been performed on the loaded PT network

to make it preloaded and then it is given as input to the highway assignment. The loaded

network from the highway assignment is supplied to PT assignment with congested times.

The same procedure is implemented for 3 iterations to stabilize the skims which can be given

input to the modal split step in next iteration.

5.9.2 Highway Assignment

Highway assignment has been carried out for peak hour by preloading the highway network

with peak hour public transport flows in terms of PCUs. A capacity restraint procedure based

on generalized cost was used in loading these PV matrices. Tolls are also considered on the

western freeway sea link. The link type wise parameters for BPR equation for speed flow

relation are taken from the TRANSFORM, 2005 for the calculation of travel cost. These steps

are performed in the script file. The Highway assignment is done based on generalized Cost

(GC)

GC = VOT * TT + TC

Where,

VOT = Value of travel time

TT = Travel time

TC = Travel cost

Parameters of Generalized cost used in Highway Assignment are as below,

VOT = Rs. 80/hr and VOC = Rs. 5/Km

The assignment of PT and Private Vehicle trips were done iteratively till an overall

equilibrium was reached between PT and highway networks. The complete flow structure of

the model developed in CUBE voyager is shown in the figure 5.15 as well as in appendix in

detailed manner.

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y

59

Figure 5.15 The complete flow structure of the Travel demand model for MMR in Voyager

5.10 Salient features of the present model

The silent feature of the presently developed model is listed as below.

1. The traditional four step travel model of MMR is implemented for the horizon year

2031 in the Script based CUBE Voyager software for the morning peak hour (09:00 to

10:00) only.

2. All the present and proposed public transport service route data was coded for the

model except a few lines.

3. The gravity model and MNL model are used for the trip distribution and modal slit

respectively for all six purposes.

4. Stochastic User Equilibrium is used for the public transport assignment and capacity

restraint algorithm is implemented for high assignment.

5. The total model is iterated for two times to get the skims stabilized with crowding

conditions

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y

60

6. Total running time for the model is about 22 to 24 hours when it is run on a desktop

of 3 GB RAM and with Dual Core processor.

5.11 Summary

This chapter dealt with the development of network as well as the Travel demand model of

horizon year 2031 for evaluation of land use scenarios. Implementation of various steps

involved in the present Travel Demand Model are described in detail and we have adopted the

trip-end, gravity and modal split models of TRANSFORM study for the current study. The

evaluation of urban transportation system performance based on developed model in Voyager

is described in the next chapter.

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y

61

Chapter 6

Evaluation of Urban Transportation System’s Performance using

MCDM approach

6.1 General

The travel demand model is run for all the short listed scenarios P2E2, P3E3 and P3E4 and

the results pertaining to the selected indicators are measured from the model and compared

among all the selected scenarios of land use. Then the rating survey results of indicators and

Multiple Criteria decision making analysis which is proposed for the present study are

analyzed in the subsequent sections to choose the best scenario.

6.2 Selected Scenarios

Totally sixteen combinations of scenarios of population and employment were made initially

and then the short-listing from an original sixteen growth scenarios to six scenarios and then

finally to three options was done which are shown in the figure 6.1.

1. P2E2

2. P3E3

3. P3E4

These scenarios are evaluated against the given proposed transportation system for the

horizon year 2031 only. The transportation network is kept same for all the scenarios. The

proposed transportation network includes,

1. The proposed suburban rail network

2. The Metro and Mono network

3. BRTS network

4. The freeways

5. Improvements of major arterials

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y

62

Figure 6.1 Overview of Evaluation of Alternative Development Options

(TRANSFORM, 2005)

6.3 Calculation of Selected indicators from the Travel Demand Model

6.3.1 Accessibility to the public transport stops

This is the major thing on which we can decide whether to choose a particular stop or not.

Hence this was studied as the average walking travel time spent to reach a bus stop. It tells us

the accessibility of the public transportation network to all the zones of the study area. Hence

it is considered as a good measure for the proposed transport network. It will also be used for

the planning of the feeder services where the walking to the transit stops are very so that the

PT share will be increased. It is used to evaluate the proposed transportation network by

comparing with the base year network. As this is not sensitive to the land use scenarios, the

results are shown below for the given proposed transportation network.

It can also be quantified as the number of Non-Transit legs generated per zone for the

proposed public transport network. It gives the exposure of the zones to the public transport

within the radius of 1.2 (for buses) to 2 Km (for transit only). The results from the model are

summarized as below.

Total number zones = 1037

Average walking time to a transit stop = 17.2min

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y

63

Total number of access/egress legs = 10698

Number of access/egress legs per zone = 10.31

Average walking time on a transfer leg = 11.3min

Total number of access/egress legs (without IPT mode) = 8571

Number of access/egress legs per zone(without IPT mode) = 8.26

Average walking time to a transit stop(without IPT mode) = 19.3min

Average walking time on a transfer leg(without IPT mode) = 14.4min

From the results it can be easily interpreted that even without considering the IPT

mode the PT network is availing the huge number opportunities for the people. Even though it

as availing the more opportunities per zone, about hundred number zones are not accessible as

they are not present within the limiting radius of the coded PT stop if the IPT mode is not

present. They are listed as below.

605-607 610-618 620-623 625 631-634 637 639 645 653 655 686 777 787 790 819 835 841 866-867 886 893 910 912 915 -918 931 934-935 942 949-953 955 958-962 964-966 975 979-982 985 987 990 992 995-999 1001-1002 1004-1007 1009 1012 1015- 1016 1019-1027 1029 1031-1032 1035-1037

Most of them may be connected with the ST buses and proposed suburban route

network those zones. However these zones are connected with the IPT mode in the model

which again will lead to high passenger boardings through IPT. It would be more interesting

if we compare these results with base year network.

6.3.2 Total Public transport user cost in generalized time units

Here the total public transport user cost refers to the combination of all the component costs

such as waiting time, in vehicle travel time, fare, effect due to crowding inside the vehicle,

transfer time in the crowded condition generalized time units by considering the same VOT

for all the modes.

It is quantified as the sum of crowded composite cost of each origin-destination pair

taken from the skim matrix produced in the traffic assignment stage in crowding condition

and are summarized for all scenarios in table 6.1 and in figure 6.2.

Table 6.1 Total Public transport user cost for three land use scenarios

Scenario Number Scenario Total Public Transport user cost in min

I P2E2 426194686

II P3E3 434591402

III P3E4 439701361

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y

64

Figure 6.2 Total Public transport user cost for three land use scenarios

From the above results, there is no much difference between among the scenarios. But

the P2E2 scenario is leading to the better performance of the proposed transport infrastructure

in this aspect.

6.3.3 Traffic Congestion

Here the traffic congestion is assumed as the percentage of the highway network length which

is being exceeded with the V/C (interpreted as demand/capacity) ratio 1.2. These results are

given in the table 6.2 and figure 6.3 along with the average crowded speed on those highway

network links for all the scenarios.

Percentage of highway network with V/C>1.2 = (length of highway network with V/C>1.2 /

Total length of highway network)*100

Table 6.2 Percentage of highway network with V/C>1.2 for three land use scenarios

Scenario

Number

Scenario Percentage of highway

network with V/C>1.2

Average crowded

speed(Km/hr)

I P2E2 8.80 26.7

II P3E3 10.03 27.7

III P3E4 9.4 28.3

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y

65

Figure 6.3 Percentage of highway network with V/C>1.2 for three land use scenarios

6.3.4 Transportation safety

The transportation safety is evaluated with the number of expected fatalities per 10,000 PCU

of private vehicles. Hence the private vehicles PCU’s (peak hour) are taken from the model.

The expected fatality rate is given by Ghee et al, 1997 for developing countries which is

shown in the table 6.3.

Table 6.3 Expected fatality rate for different vehicle ownership level (Ghee et al, 1997) for

developing countries

Vehicle ownership Expected fatality rate

<100 vehicles per 10,000 population 50-100 fatalities per 10,000 PCU

>100 vehicles per 10,000 population 10-50 fatalities per 10,000 PCU

According to the above specifications the expected fatalities are computed for all the

scenarios which are tabulated in the table 6.4. Thirty fatalities per year are assumed, as

vehicle ownership of MMR is more than 100.

Table 6.4 Expected fatalities for MMR in case of all the scenarios

Scenario Number Scenario Private vehicle PCU Expected fatalities

I P2E2 805612 2416

II P3E3 810158 2430

III P3E4 806771 2420

6.3. 5 Mode share of the public transport

The share of the public transport is considered for the peak hour and in that, percentage of the

different public transport modes are also discussed here. The peak hour loadings by all public

transport modes in terms of passenger boardings, passenger distances in Km and passenger

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y

66

hour for each land use scenario (P2E2, P3E3 and P3E4) are given in tables 6.5, 6.6 and 6.7

respectively. The more percentage of the IPT is due to the fact that,

• the passengers are using the IPT mode as feeder service (as the average trip length is

4.5-5 Km)to reach the transit modes like metro, suburban and BRTS etc. and

• As there are no transit lines coded except metro in the rural areas of the study area.

Hence the trips are getting attracted to IPT to reach the nearest Metro station and this

discrepancy can corrected by coding all the remaining public transport bus services

and the suburban routes in the proposed corridor in the rural area.

The proportions of all the Public transport modes (without IPT) are shown in the figures 6.4,

6.6 and 6.8 for scenarios P3E3, P2E2 and P3E4 respectively.

Table 6. 5 Peak hour loadings by all public transport modes for P3E3 Scenario

Mode Passenger

Boardings

Passenger

Kilometers

Average Trip

length

In Km

SUBURBAN 713121.15 13621038.4 19.10

METRO 1959430.8 22208662.3 11.33

MONO 4690.45 19789.82 4.21

BEST 1073777.7 8837929.79 8.23

TMT 310850.8 2375889.84 7.64

NMMT 228270.24 854284.44 3.74

MBMT 98259.43 281918.9 2.86

KDMT 406058.16 1249674.25 3.07

IPT 3368747.4 16392558.4 4.86

BRTS 147620.69 732166.17 4.95

Average PT(without IPT)Trip length= 10.2 Km

The low value of average trip length of PT is also due to the lack of coding of

sufficient public transport service lines. As about 100 zones are not connected by the transit

mode , they are connected with IPT mode which is one more factor to have the high IPT

share in passenger boardings. This limitation can be easily achieved by the coding of

remaining lines of PT in the model by which one can evaluate the scenarios in a more exact

way.

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y

67

Figure 6.4 Peak hour PT Modal share for the scenario P3E3 without IPT mode

When we compare the average trip lengths for PT modes, the reasonable values are

coming for the TPT as only 4.5-5 km. The suburban is having the highest the average trip

length in all the scenarios. The average trip lengths in km for PT modes are given in the

figures 6.5, 6.7 and 6.9 for the scenarios P3E3, P2E2 and P3E4 respectively.

Figure 6.5 Peak hour Average trip length of PT modes in Km for the scenario P4E3

Table 6. 6 Peak hour loadings by all public transport modes for P2E2 Scenario

Mode Passenger Boardings Passenger Kilometers Average Trip length

In Km

SUBURBAN 619043.53 12331270.39 19.92

METRO 1772487.86 19870374.94 11.21

MONO 6941.07 31010.1 4.47

BEST 1322137.62 10253645.72 7.76

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y

68

Mode Passenger Boardings Passenger Kilometers Average Trip length

In Km

TMT 279477.8 2116294.2 7.57

NMMT 196595.42 793193.22 4.03

MBMT 85942.71 265649.02 3.09

KDMT 1110563.75 27625.33 0.02

IPT 3250024.09 14836433.34 4.57

BRTS 203046.4 1026562.29 5.06

Average PT(without IPT)Trip length= 10.22 Km

Figure 6.6 Peak hour PT Modal share for the scenario P2E2 without IPT mode

Figure 6.7 Peak hour Average trip length of PT modes in Km for the scenario P2E2

Table 6. 7 Peak hour loadings by all public transport modes for P3E4 Scenario

Mode Passenger Boardings Passenger Kilometers Average Trip length

In Km

SUBURBAN 660257.28 11374520.07 17.23

METRO 2173322.53 24343336.74 11.20

MONO 3788.9 14996.72 3.96

BEST 1058345.3 9600520.01 9.07

TMT 336032.06 2443472.46 7.27

NMMT 258083.24 945011.42 3.66

MBMT 94575.3 266417.41 2.82

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y

69

Mode Passenger Boardings Passenger Kilometers Average Trip length

In Km

KDMT 463631.2 1440535.52 3.11

IPT 3345553.5 17872938.19 5.34

BRTS 110736.87 529396.09 4.78

Average PT(without IPT)Trip length= 9.8 Km

Figure 6.8 Peak hour PT Modal share for the scenario P3E4 without IPT mode

Figure 6.9 Peak hour Average trip length of PT modes in Km for the scenario P3E4

6.3.6 Average trip length and vehicle kilometers

Average trip length by private vehicles tells us the relative distribution of population or

employment in the study area. The vehicle kilometers by the private vehicles, corresponding

the average trip length and the average speed across scenarios which is varies from 28 to 31

Kmph.is given in the table 6.8.

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y

70

Table 6. 8 Vehicle kilometers and Average trip lengths by PV and PT for all the Scenarios

Mode P2E2 P3E3 P3E4

Vehicle kilometers 12721693 13605542 14184850

Average trip length for PV in Km 15.19 16.8 17.5

Average trip length by PT in Km 10.22 10.2 9.8

Average Speed by PV in Kmph 28.65 29.47 30.28

Average Speed by PT in Kmph 29.47 34.64 35.02

6.3.7 Cost of the proposed transportation infrastructure for the Horizon year 2031

This is parameter is same for all the land use scenarios as there is no change considered in the

transport scenario. The cost of the proposed transportation infrastructure according to the

TRANSFORM, 2005 is Rs. 1,887,07 crore which is given in the table 6.9.

Table 6.9 Cost of proposed transport infrastructure for the horizon year 2031

Sl. No. Transport System Length

(kms)

Estimated Total Cost

(Rs. crores)

@ 2005-06 Prices

Estimated

Total Cost

in % of Total

(%)

I Metro System 514 1,158,28 61.4

II Sub-Urban Railway System 241 320,67 17.0

III Highway System 1974 408,12 21.6

Total 2729 1,887,07 100.0

6.4 Analysis of the Rating survey

As proposed in the methodology, the rating survey is done for assigning the relative

weightages to the indicators measured from the model. Totally a twenty number of samples

are taken in which,

Number of samples taken from researchers = 10

Number of samples taken from Common user = 5

Number of samples taken from industrial professional = 5

The corresponding sample survey data sheet is attached in the appendix. Along with

their ratings and rankings for the indicators, their response is also considered according to

which, ITS component is the main indicator on which the public transport service can be

evaluated. However it is assumed here that, the ITS will be functioning well in the horizon

year 2031 for all the scenarios. The summary of average ratings of the three groups of interest

for all the indicators along with their relative weightages are presented in the table 7.9 and

also shown in the figure 7.2.

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y

71

6.4.1 Inferences from survey

According to the researchers point of view, the accessibility to the transit modes will be

having the high weightage to evaluate a given transportation system along with the average

trip length through PT. Industrial professionals perceived that the generalized cost through PT

should have a very high importance. A common user perceives that the total cost to the

destination should be low and accessibility to transit mode should be high. Transportation

safety and environmental pollution (here it is indicated according to vehicle kilometers

travelled) is highly preferred to evaluate the transportation system performance.

From the response of the samples, it is inferred that some more indicators can also be

considered while evaluating the transportation system absolutely. They are listed below as,

1. Feeder services performance

2. Passenger travel information system

3. Integration between different transit modes

4. Provision of park and ride facility

5. Provision of non-motorized transport facilities like dedicated bike lanes

6. Pollution cost occurred during construction activity of proposed

transportation infrastructure.

Due to the limitations of the present model these are not considered but they can be

accounted easily for evaluating the absolute transportation system performance.

The selected indicator set for the analysis is listed below,

1. Indicator I – Accessibility to the transit stop

2. Indicator II – Total public transport user cost to the destination

3. Indicator III– Traffic Congestion

4. Indicator IV – Transportation safety

5. Indicator V – Average trip length through PT

6. Indicator VI– Average speed through PT

7. Indicator VII – Vehicle kilometers travelled

8. Indicator VIII – Average trip length through PV

9. Indicator IX – Average speed for PV

Table 6.10 The summary of average ratings of the three groups of interest for all the

indicators

Sl. No Sample type Indicators

I II III IV V VI VII VIII IX

1 Researchers 8.77 8.22 9.11 8 7.22 7.16 6.89 6.89 6.85

2 Common user 7.5 9 8 9 7.5 7.4 7.5 5.5 5.3

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y

72

Sl. No Sample type Indicators

I II III IV V VI VII VIII IX

3 Industrial

professional 8 9.5 7 8 6.5 6.35 5.5 4 4

Average rating score 8.46 8.53 8.61 7.84 7.15 7.13 6.76 6.23 6.20

Figure 6.10 The summary of average ratings of the three groups of interest for all the

indicators

6.4.2 Calculation of Relative Transportation performance Index

Relative score is given for all the scenarios based on measure of the each indicator and is

computed as proposed in the methodology.

Relative score of a scenario j with respect to indicator i = 100-(% difference with the best

measure among the scenarios)

Let RWji = Relative weighted score for the scenario j with respect to indicator i

RWji = Relative score of a scenario j * average rating score of that indicator i

Relative Transportation System Performance Index for Scenario j =RTPIj= ∑RWj

The total analysis part is shown in the table 6.11 to arrive at RTPI for each scenario

based on which we can rank them. The same results are also shown in the figure 6.11.

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y

73

Figure 6.11 Summary of RTPI for all the scenarios

It is very clear that, all the scenarios are getting differed very less score, however the

scenario P2E2 scenario is the best according to the obtained. The P2E2 tell that inticification

of Greater mumbai in population and employment. This decision is based on the coded Public

transportation network and the inclusion of more sustainable transportation system

performance indicators. Hence this can be modified by overcoming the a few limitations of

the model.

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y

74

Table 6.11 The calculation sheet for computation of RTPI for all the scenarios

Indicator

Relative

Score for

the indicator

i (out of 10)

Wi

(1)

Relative Rating of each scenario w.r.t. corresponding indicator

Scenario 1 (P2E2) Scenario 2 (P3E3) Scenario 3 (P3E4)

Measure

from model

(2)

Relati

ve

Score

(3)

Relative

(RWij)weighted

score

(4) = (3)x (1)

Measure

from model

(5)

Relati

ve

Score

(6)

Relative

(RWij)weight

ed score

(7) = (6)x (1)

Measure

from model

(8)

Relati

ve

Score

(9)

Relative

(RWij)weighted

score

(10) = (9)x (1)

I(min) 8.46 19.38 min 100 846 19.38min 100 846 19.38min 100 846

II(min) 8.53 426194686 100 853 434591402 98.02 836.11 439701361 96.83 825.95

III(%) 8.61 8.8 100 861 10.03 86.02 740.63 9.4 93.18 802.27

IV 7.84 805612 100 784 810158 99.43 779.53 806771 99.8 782.43

V(Km) 7.15 10.22 100 715 10.2 99.98 714.85 9.8 96.0 686.4

VI (Kmph) 7.13 29.47 81.2 578.95 34.64 98.9 705.15 035 100 713

VII(veh-km) 6.76 12721693 100 676 13605542 93.05 629.01 14184850 88.49 598.19

VIII(Km) 6.23 15.19 84.79 530 16.8 95.83 556.96 17.5 100 623

IX (Kmph) 6.20 28.65 94.31 584.722 29.47 97.25 602.95 30.28 100 620

RTPIj ∑ RW1=6521 ∑RW2=6411 ∑ RW3=6402

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y

75

Chapter 7

Summary and Conclusions

7.1 Summary of work

The travel is the derived demand of various land use patterns of a region. It can be easily

understood that land means the spatial distribution of locations of various activities in a region

such as residential, commercial, industrial and educational etc., and the transport is the link

between them. Hence the land use determines the magnitude, direction, purpose and spatial

distribution of travel which is to be accommodated by the overall transportation system

present in the region. Either the transportation system should be planned according to the

given land use distribution or the land use distribution should be made for the given

transportation system.

The study has been started with the problem statement of uncertainty in the existing

integrated land use-transport models in the prediction of a transportation system performance.

However even there are no standardized methods to follow to evaluate the given

transportation system. Hence the study has been moved forward with the aim of evaluating

the selected land use scenarios for with respect to the transportation system’s performance.

The study area is taken as the MMR. As the part of the aim the sub-objectives were set as

below.

1. Implementing the travel demand model

2. Selection of transportation performance indicators.

3. Formulation of procedure to evaluate the relative transportation system

performance through MCDM approach.

4. Evaluation of transportation performance using the implemented travel demand

model

In process of implementing the travel demand model, the complete highway and

transit network for horizon year 2031 is developed using the raw shape files got from

MMRDA by incorporating the all the attributes required for the modeling. That complete

network has been developed using the CUBE, TransCAD and ArcGIS tools. Then the

complete route databases of all the public transportation services which can be available in the

year 2031 were coded onto CUBE platform. Then the traditional four step modeling process is

adopted for the travel demand modeling. The demand modeling was done for six purposes for

morning peak hour. The planning variables are taken from MMRDA and modeling process

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y

76

was performed using the calibrated models in the TRANSFORM study of MMR conducted in

2005. The trip generation was done based on regression models. The calibrated gravity model

is used for trip distribution. The MNL is utilized in the model split computations. Then the

peak hour matrix of PT and PV are assigned to the network using stochastic user equilibrium

assignment technique and capacity restraint algorithm for public transport and highway

assignment respectively. The whole modeling process was implemented in the CUBE

Voyager software which is a script based transportation planning software.

The literature review has been done to select the indicators which represent the

transportation system performance and some quantifiable indicator set is selected based on the

general guidelines specified in the literature. It has been found from the literature that, there

has been no standard methodology to evaluate the transportation system’s performance.

Hence a procedure is formulated using the MCDM according which a rating survey has been

done to rate the indicators from their own perspectives. The samples are taken from threes

groups of interest such as researchers, industrial professional and common users. After giving

the relative importance to the individual indicators, then those are measured from the travel

demand model for all the selected scenarios P2E2, P3E3 and P3E4 for the given

transportation system for the horizon year 2031. As the part of formulated procedure, the

composite index (RTPI) is computed based on all other indicators. The each scenario is

assigned with the RTPI based on which the best scenario is selected as P2E2.

7.2 Conclusions

There are few conclusions drawn from the review of the literature, implementing the travel

demand model on Voyager software and from the evaluation.

Single level (TAZ level) Traditional four stage travel demand modeling is adopted for

the present study. It is observed from literature review that, the integrated land use

transportation model does not work well for the Indian conditions. Hence the scenario based

approach is adopted for the evaluation of land use scenarios. The various evaluation indices

are reviewed and the indices which represent the environmental, economical and proposed

transportation system based impacts are selected for the present study. As it has been clear

from the literature that, there is no standard criteria for the evaluation an attempt is made to

propose a procedure for the evaluation by considering the response from the researchers,

common user and industrial professionals in a multi criteria decision making approach.

The GIS based network is created for entire MMR covering all the existing and

proposed corridors of all the modes from the shape files along with the preparation of data

base of all the public transport services which is given as input to the travel demand model. It

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y

77

is suggested to use more number GIS tools for network building and database management

when we handle such a huge network as there is no single GIS tool which can perform the

entire requirement that we need. It was the largest time taking task performed in the study.

Then the travel demand model is developed for the horizon year 2031 CUBE Voyager

software which was also consumed much time in learning the command language which is

very similar to the basic C language. It has been found that the software provides a very

flexible platform to implement any kind of algorithm to suite our requirement and also

provides the similar GIS platform as in ArcMAP.

The results from the model are discussed which has shown that there is a discrepancy

in the IPT passenger boardings share due to the limitation model which is discussed in the

next section. Without considering the IPT share, in all the scenarios the share for metro is

high w.r.t. passenger boardings but the average trip length for the suburban train is higher.

The rating survey was done to assign the relative importance to each selected indicator, then

the analysis is carried out as proposed in the methodology which led to the decision as P2E2

as the best land use scenario to control growth of the growth scenario. However this decision

can be modified by overcoming the limitations of the model as well as that of the

methodology.

7.3 Limitations

There are certain limitations which are to be considered and corrected to evaluate the

transportation system in a more exact way. They are,

1. Lack of route data coded for the proposed sub-urban rail system for the year 2031 .ST

bus route data is also not coded. A very few of the bus routes of NMMT, BSET and

MBMT bus routes are remaining.

2. The survey was done for only 20 numbers of samples due to the time constraint which

can be increased to more number.

3. The model is developed only for the horizon year 2031 by using the calibrated

parameters from the TRANSFORM study. The hence to the base year model should

also be developed to recalibrate the gravity model, so that forecasts for the 2031 year

can be more reliable.

4. The selected indicators for evaluation represent a few impacts only.

5. Here the only given transportation system is considered for the evaluation i.e. the

evaluation is not done by varying the transport scenarios.

6. The present methodology for evaluation is for computing the relative transportation

performance index only but not the absolute.

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y

78

7.4 Future Scope of the work

The future commitments to be completed to achieve the goal for the present study are as

below,

1. Overcoming the limitations stated in the previous section.

2. Performing Sensitivity analysis by considering the relative importance’s of indicators

from each group of interest wise.

3. Conducting a large scale survey to arrive at the more possible set of indicators which

can indicate more impacts for the evaluation and assigning the relative importance

among them.

4. Implementing the methodology for evaluation with tools like Fuzzy logic.

5. Modifying the methodology to compute the absolute transportation system

performance index for the developing countries like India.

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y

79

References

Arampatzis, G., Kiranoudis, C., Scaloubacas, P., & Assimacopoulos, D. (2004). “A GIS-

based decision support system for planning urban transportation policies”. European Journal

of Operational Research , Vol. 13(7),pp.465–475.

Baber, C. M., & John, G. (2004). “Baltimore Region Travel Demand Model”. Task Report.

Baltimore Metropolitan Council, Mary land, pp.5-26.

Barra, T. D. (1989). “Integrated land use and transport modeling”. Melbourne, Cambridge

University press, pp.114-120

Beard, D. A. (1993). GIS and Transportation Planning: a case study. Comput. Environ. and

Urban Systems, Vol. 17(3), pp.563-574.

Ghee, C.E, D.T.Silcock, A.Astrop, M.Grove and G.D.Jacobs (1997). “Socio-economic

aspects of road accidents in developing countries”. Transport Research Laboratory.

Crowthorne , pp.25-36.

Gupta, S. (2010). “Urban form-Transport patterns in indian cities and emerging policy

implications”. Sustainable Lifelines: Transportation Planning and Management (pp. 33-39).

Chandigarh: Guru Ramdas School of Planning.

Hwang, C.L. and Yoon, K.P. (1981) “Multiple Attribute Decision Making: Methods and

Applications”, Berlin/Heidelberg/New York: Springer-Verlag, pp. 18-35.

Johnston, R. A., & Clay, M. J. (2005). “Univariate Uncertainty Analysis of an Integrated

Land Use and Transportation Model”. Transportation Planning and Technology,Vol. 28 (5),

pp.149-165.

Littman, T. (2008). “Measuring Transportation Traffic, Mobility and Accessibility”. Victoria

Transport Policy Institute , pp. 1-15.

Littman, T. (2011). “Developing Indicators for comprehensive and Susteainable Transport

Planing”. Victoria Transport Policy Institute , pp. 1-13.

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y

80

Moorthy, R., Dhingra, S., & Rao, K.V.K. (2003). “Travel demand assessment in GIS”. Map

Inda, GIS Develpments , pp. 12-20.

Mumbai Metropolitan Region. (2010). Retrieved 10 14, 2010, from Wikipedia:

http://en.wikipedia.org/wiki/Mumbai_Metropolitan_Region

Purvis, C. L. (1997). “Travel Demand Models for the San Francisco Bay Area (BAYCAST-

90)”. Technical Summary .Metropolitan Transportation Commission.,Oakland,

California.,pp. 16-35.

TRANSFORM. (2005). “Comprehensive Transportation study for Mumbai Metropoliatn

region”. Mumbai Metropolitan Region: Lea Associates., 4th

Chapter, Vol. 1 Main Report,

pp.58-230.

Wegener, M., & Furst, F. (1999). “Land-Use Transport Interaction: State of the Art”. Institute

for Raumplanung (Dotumond University), pp.3-23.

Yuan, H, Huapu , L.U., (2003). “Evaluation and analysis of urban transportation efficiency in

China”. Beijing institute of transportation engineering, Tsinghua university, Beijing, china,

pp.2-9.

Zegras, C,(2006). “Sustainable Transport Indicators and assessment methodologies”.

Conference on clean air initiative for latin American cities. Brazil, pp. 1-6.

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y

81

Acknowledgments

I am heartily thankful to my supervisor, Prof. K.V. Krishna Rao, whose encouragement,

guidance and support from the initial to the present level enabled me to develop an

understanding and application of the subject to achieve the objective of the study. It is a

pleasure to thank Prof. S.L.Dhingra, who has spent his lot of valuable time to share his

suggestions towards the objective. I also obliged to Prof. Tom V. Mathew, Dr. Gopal Patil,

and Dr. P. Vedagiri for their constant encouragement. I am indebted to all of my classmates,

juniors and friends to support me. I would like to be grateful to the MMRDA organization

which has supplied the required data very timely. I was overwhelmed to CITILABS for their

help in understanding the CUBE Voyager software. Last but not the least, I would like to

thank my seniors Mr. Madhav and Mr. Bala Krishna who were always behind me and

encouraged me in every possible aspect.

Srinivas G

Date:

Place:

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y

82

Appendix

Data sheet for Rating and Ranking Survey on Urban Transportation

System Performance Indicators

Name: Income: Age: Date:

Sample Type: Researcher/Industrial/Common User Sample number:

The below indicators are to evaluate the performance of transportation system of a study area.

Please give proper rating and ranking to the indicators based on their dependability to

evaluate the transportation system in your perspective in system level.

If you think an indicator which will be best suited for evaluating the performance, then

you can give rating of maximum 10 and rank 1 then accordingly give the rating for other

indicators.

Indicator

Number

Indicator Rating out 10

marks

Ranking

1 Accessibility to Public Transport(PT) stops from

your home

2 Total Public transport user cost origin to destination

3 Traffic Congestion

4 Transportation safety (number of accidents)

5 Average Trip length through PT

6 Average speed through PT

7 Vehicle kilometers travelled by PV

8 Average Trip length through PV

9 Average speed through PV

Please write if you think there are any other indicators also, to evaluate the urban transportation system performance.

…………………………………….:

…………………………………….:

…………………………………….:

Thank you for your valuable time

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y

4 Stage Travel Demand Model Structure implemented in CUBE Voyager

N

E

T

W

O

R

K

D

E

M

A

N

D

S

U

P

P

L

Y

Loop

Iterations=3

Initial Highway and PT costs

Highway and PT costs

Congested Highway and PT

costs

Generation of Initial PT cost

PT Assignment Highway assignment PT/NPT Routes Development

- Loaded PT Network - Loaded PT and PV Network - Congested PT network

- Loading Reports - Loading Reports

- Congested PT Skim - Congested Highway Skim

Loop

Iter =2

Free flow PT network

Congested PT network

PT network

Trip Generation Trip Distribution (Gravity Model) Model Split (MNL)

- Zone wise Productions PA matrix Peak Hour OD Matrix

and Attractions

Zonal Planning Variables

Network Development

(Highway and Public transport)

Public Transport routes Development

and Non Transit leg generation

Generation of Initial Highway Cost

Evalua

tion o

f Lan

d Use

Sce

nario

s usin

g a Trav

el Dem

and M

odel

for M

umba

i Metr

opoli

tan R

egion

, Srin

ivas G

, IIT B

omba

y