29
CHAPTER 3
STUDY METHODOLOGY
3.1 INTRODUCTION
The mode choice does not rely on a single parameter. It is
associated with many parameters. In this chapter, the methodology for
estimating the mode choice used in this study is explained in detail. The
factors influencing the mode choice are first identified and then quantified.
The parameters which have significant impact are analyzed in detail. The
analysis is done for the parameters like land use activity, station
characteristics, opinion survey and travel pattern.
The methodology of this work is shown in Figure 3.1. Essentially,
the steps involved are data collection, analysis, model building and
forecasting.
3.2 STUDY AREA
Chennai city is situated on the North-East end of Tamil Nadu on
the coast of Bay of Bengal with a total area of 178.20 Sq. Km. It lies between
120 9' and 130 9' of the latitude and 800 12' and 800 19' of the longitude on a
sandy shelving breaker swept beach. It stretches nearly 25.60 Km along the
Bay coast from Thiruvanmiyur in the south to Thiruvottiyur in the north and
runs inland in a rugged semi-circular fashion.
30
Figure 3.1 Methodology Chart
Chennai has three rail corridors such as southwestern corridor
connecting Beach and Tambaram, western corridor from Beach to Avadi and
northern corridor from Beach to Thiruvallur. In addition to these rail transport
system, MTC operates 2990 buses on nearly 640 routes serving the travel
demand of the people in the city. The MRTS rail corridor in Chennai has been
Secondary Data 1. Demography 2. Employment.
Objective
Study Area Selection
Land Use Study of MRTS Corridor
Data Collection
Demand Matrix of MRTS/ BUS
Model Development and validation
Sensitivity Analysis
Formulation of scenarios and analysis
Primary Data 1. Opinion survey 2. OD survey of Mass Transport passenger (bus, MRTS) 3. Frequency, Accessibil ity, Travel time, and Travel Cost of Mass Transport Systems(bus, MRTS).
Study of operational characteristics of the
Mass Transport System
Identifying Tangible and Non Tangible parameters influencing Mode choice
Findings and Conclusion
Artificial Neural Network (ANN)
LOGIT MODEL
Secondary Data 1 Demography 2 Employment.
Objective
Study Area Selection
Land Use Study of MRTS Corridor
Data Collection
Demand Matrix of MRTS/ BUS
Model Development and validation
Sensitivity Analysis
Formulation of scenarios and analysis
Primary Data 1. Opinion survey 2. OD survey of Mass Transport passenger (bus, MRTS) 3. Frequency, Accessibil ity, Travel time, and Travel Cost of Mass Transport Systems (bus, MRTS).
Study of operational characteristics of the
Mass Transport System
Identifying Tangible and Non-Tangible parameters influencing mode choice
Findings and Conclusion
Artificial Neural Network (ANN) Model Logit Model
31
taken for the study purpose. The MRTS influence area (i.e. about 1.5 Km on
either side) has been taken as the study area (Figure 3.2).
Figure 3.2 MRTS Influence Area
32
The MRTS alignment passes through the Cooum and Adyar rivers
and runs parallel to the Buckingham Canal for about 10 Km. There is a good
scope for the passenger patronage to MRTS from the influence area which has
mixed land use activity. 35 percent are residential, 25 percent are institutional
and about 15 percent are open and vacant land, 12 percent are industrial and
13 percent are commercial. The total MRTS system was planned for 41.47
Km in four stages; till date only two stages have been constructed, that is,
Phase I from Park to Thirumailai and Phase II from Thirumailai to Velachery.
The total length of MRTS in operation is only 19.34 Km. The average inter-
station spacing between MRTS stations is about 1 Km, whereas the distance
between Beach and Fort station, Light house and Thirumailai stations is about
1.71 Km. The study focuses only on the constructed portion of MRTS.
The section of the line encompassing the first three stations -
Beach, Fort and Park Town, is at a ground level; after Park Town the line is
elevated. All the stations after Park Town - Chintadripet, Chepauk, Triplicane,
Light House, Mandaveli, Greenways Road, Thirumailai, Kotturpuram,
Kasthuribai Nagar, Indira Nagar, Thiruvanmiyur, Taramani and Perungudi are
elevated and Velachery station is at ground level.
3.3 LAND USE DISPOSITION ALONG THE MRTS CORRIDOR
The patronage to any transportation project is highly dependent on
the land use disposition along the corridor. The patronage of MRTS is also
attributed to the land use development along the corridor and the socio-
economic characteristics of the people living along the corridor. The land use
along the corridor and the socioeconomic characteristics of the people are
discussed in this section.
The economically weaker section of the society encroached on
vacant land available on the banks of Buckingham Canal. They have built
33
huts and temporary structures for living. The area of settlement by this group
is called slum. Table 3.1 explains clearly the impact of slum population on
MRTS ridership. It is evident that, if the slum population is higher (10 - 20
percent) there is less scope for MRTS ridership. In case of Thirumailai, the
slum population is 13.22 percent but, it has good ridership. It is because of
high density residential population and slum is available at only on one side.
Hence, the slum population has significant impact on reduction of MRTS
ridership. Because of the encroachment, safety and hurdles in commercial
development other people avoid using the MRTS.
Table 3.1 Details of Population and Slum Population
Station Population in numbers
– 2001
Slum Population
in numbers
Percentage of Slum Population
MRTS Patronage
in numbers
Beach 45635 2405 5.27 5469 Fort 45635 2333 5.11 2995 Park Town 34650 727 2.10 3155 Chintadripet 74381 4590 6.17 1191 Chepauk 28046 1020 3.64 3364 Triplicane 33381 5720 17.14 1277 Light House 82140 16555 20.15 2047 Thirumailai 103398 13668 13.22 5365 Mandaveli 73866 7475 10.12 585 Greenways Road 42289 9137 21.60 875 Kotturpuram 82498 6815 8.00 825 Kasthuribai Nagar 54733 6735 12.30 1025 Indira Nagar 65042 8228 12.60 825 Thiruvanmiyur 46567 6577 14.00 3246 Taramani - I 22439 5105 9.00 350 Taramani – II 22439 2105 9.00 250 Velachery 132584 10235 7.00 3656
34
Table 3.2 and Figure 3.3 give the classification of stations by
population density. Table 3.3 gives the land-use breakup of MRTS corridor.
Correlating Table 3.1, 3.2 and 3.3, it is very clear the stations Triplicane and
Light House come under high density zones but have 20 percent of slum
population. Hence, the ridership at these stations is very low. The stations like
Chindaripet, Beach, Chepuk, Thirumailai and Mandaveli come under medium
density zones and hold a moderate ridership. The stations like Fort, Park,
Greenways Road, Kasturbanagar, Indiranagar, Thiruvanmiyur and Velachery
come under low density zones but, due to availability of institutional and
residential area theses stations hold good ridership. Thus stations with high
density with good mix of residential and Institutional area will have scope for
higher ridership.
Table 3.2 Classification of Stations Based on Population Density in
MRTS corridor
Phase High Density
Zone > 400 ppHa Medium Density
Zone 200-400 ppHa Low Density Zone
< 200 ppHa Phase I Triplicane (586) Chintadripet (294) Fort (195)
Light House (517) Beach (287) Park Town (180) Chepauk (236) Thirumailai (370)
Phase II Mandaveli (204) Greenways Road (158) Kotturpuram (127) Kasthuribai Nagar(120) Indira Nagar (154) Thiruvanmiyur (147) Taramani and
Perungudi (77) Velachery (44)
35
From Table 3.3 and Figure 3.3 it is clear that both ends of MRTS
are having institutional area and commercial area. The residential area is
sandwiched between the two ends. Hence there is a good scope for trip
generation and attraction along the MRTS corridor.
Table 3.3 MRTS Station Wise Land Use break up in percentage
Station Name
Land Use break up in percentage
Tota
l in
perc
enta
ge
Res
iden
tial
Com
mer
cial
Inst
itutio
n
Ope
n
Roa
d
Indu
stri
al
Vac
ant
Beach 1.29 57.44 23.45 0.00 17.82 0.00 0.00 100
Fort 5.81 6.97 64.87 0.00 22.35 0.00 0.00 100
Park Town 21.87 12.06 39.93 0.00 26.14 0.00 0.00 100
Chintadripet 34.13 16.51 27.83 0.54 19.86 1.13 0.00 100
Chepauk 25.95 15.67 51.02 0.00 7.36 0.00 0.00 100
Triplicane 41.22 10.64 16.33 2.76 28.38 0.68 0.00 100
Light House 40.03 14.77 15.88 5.32 23.99 0.00 0.00 100
Thirumailai 41.05 8.82 15.78 5.28 29.07 0.00 0.00 100
Mandaveli 44.11 14.44 8.00 3.03 30.42 0.00 0.00 100
Greenways Road 62.83 4.04 5.86 5.44 21.12 0.72 0.00 100
Kotturpuram 47.83 2.11 20.34 11.10 18.62 0.00 0.00 100
Kasthuribai Nagar 43.97 7.98 25.63 4.02 18.67 0.00 0.00 100
Indira Nagar 36.53 5.19 25.52 3.38 29.39 0.00 0.00 100
Thiruvanmiyur 48.07 2.97 27.39 7.89 13.39 0.00 0.00 100
Taramani 4.78 1.28 41.64 10.78 11.11 4.15 26.2 100
Perungudi 62.62 3.93 0.90 25.79 6.76 0.00 0.00 100
Velachery 55.27 3.26 0.56 23.43 0.00 1.87 15.6 100
36
Figure 3.3 Population Density along the MRTS Corridor
3.4 MASS TRANSPORT SYSTEMS OPERATED IN THE STUDY
AREA
MRTS and Bus transport system are the two major mass transport
systems operated in the study area. The operational aspects and ridership of
the systems are described in this section.
3.4.1 MRTS Characteristics
MRTS operation between Chennai Beach and Chepauk stations
was unveiled to commuters in 1995. The stretch was built with an investment
of 534.6 million Indian Rupees (INR). The demand for MRTS on
implementation was about 600 per day, since MRTS operated for a small
distance of 5.84 Km with six stations. On extending the MRTS up to
Thirumailai in 1997 with an additional cost of 2069.5 million INR the
37
demand gradually picked up from 600 to 9000 over a period of 7 years. In
2004 the MRTS was further extended upto Thiruvanmiyur with an additional
cost of 6840 million INR. Because of the extension the demand increased
from 9000 to 18000 and over three years it reached up to 28000. On
extending the MRTS section up to Velachery in 2008 again there was rise in
the demand and it is up to 36000 per day on an average. On studying the
growth of demand from 1995-2008 (Figure 3.4) it is evident that the demand
increased in relation with the length of operation.
After commissioning of Phase II MRTS showed remarkable
increase in the ridership. Figure 3.5 shows the trend of average MRTS
passenger loading per day from the year 1995 to 2008 (March). It is evident
that Beach and Thirumailai station has average patronage of 5500. Beach is
the only station which has the maximum patronage of 5469 per day and next
to Thirumaili station the Velachery station which has the average patronage of
3656 per day.
0
5000
10000
15000
20000
25000
30000
35000
40000
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
Year
MR
TS P
asse
nger
Dem
and
per D
ay
in N
umbe
rs
Figure 3.4 MRTS Passenger Demand per Day from 1995 to 2008
38
29553156
1191
3346
1277
2047
5365
585878 825 825 1025
3246
350 250
3656
5469
0
1000
2000
3000
4000
5000
6000
Beach Fort
Park Town
Chintad
ripet
Chepau
k
Triplic
ane
Light
House
Thirumail
ai
Manda
veli
Greenways
Roa
d
Kotturp
uram
Kasthu
ribai N
agar
Indira N
agar
Thiruv
anmiyu
r
Taramani
Perung
udi
Velach
ery
MRTS Stations
MR
TS P
asse
nger
Dem
and
per D
ay in
N
umbe
rs
Figure 3.5 Average Passenger Loading at MRTS Stations
The other stations like Fort, Park, Chintadripet, Triplicane, Light
House, Mandaveli, Greenways Road, Kotturpuram, Kasthuribai Nagar, and
Indiranagar have average passenger loading of 1500- 2000 per day. The total
ridership on MRTS ranges from 36000- 37000 per day. Table 3.4 gives the
average loading at each MRTS station as on March 2008.
Table 3.4 Patronage at MRTS Stations as on March 2008
Name of the station
MRTS Demand per day in Numbers
Name of the station
MRTS Demand per day in Numbers
Beach 5469 Greenways Road 878 Fort 2955 Kotturpuram 825 Park Town 3156 Kasthuribai Nagar 825 Chintadripet 1191 Indira Nagar 1025 Chepauk 3346 Thiruvanmiyur 3246 Triplicane 1277 Taramani 350 Light House 2047 Perungudi 250 Thirumailai 5365 Velachery 3656 Mandaveli 585
Source: Railways Department
39
MRTS operates 100 services / day between Beach and Velachery
and the cumulative running time from Beach to Velachery is 42 minutes at an
average speed of 21 Kmph. The morning peak Period is identified between
8.00 AM – 10.00 AM and the evening peak period is identified between 5.00
PM – 7.00 PM. The frequency varies from 2 trips per hour to 5 trips per hour.
A survey of boarding and alighting passengers was carried out for 8
hours covering morning and evening peak hours to understand the current
ridership and extent of line utilization. It showed that at present the system
operates 5 transit trips during peak period. The maximum number of
passenger trips observed during peak period is 2700. MRTS system is
designed to carry 13, 00,000 trips for 15 hours, both ways but the total trips
observed for 15 hours is 36000 trips both ways. Hence, the line utilization
2.76 percent.
To estimate the present hourly variation in ridership in MRTS a
survey on passenger boarding and alighting was carried out between 7:00 AM
and 11:00 AM in the morning and 4:00 PM and 8:00 PM in the evening.
At each station the total number of passengers boarding and alighting
was surveyed and passenger trips per hour per direction were calculated.
Figures 3.6 and Figure 3.7 below represent the same for morning and evening
hours.
The morning peak is observed between 8:00 AM and 10:00 AM
and the evening peak is observed between 5:00 PM and 7:00 PM. 75 percent
of passenger trips, south bound, is during 8:00 AM - 10:00.AM. The
frequency of train is higher during this period and these trips should be mostly
work trips. MRTS experiences directional patronage and Table 3.5 below
clearly indicates that southbound traffic is heavy during morning hours. The
directional split during the morning period is 68:32 (South: North). Table
40
shows that a morning passenger trip is 17850 and evening passenger trip is
18150. Peak period is observed between 8:00 AM and 10:00 AM and
5:00 PM and 7:00 PM. This value justifies that the evening passenger trips are
also return work trips.
200287
2256
2675
531
1522
870
2330
0500
10001500200025003000350040004500
7.00
-8.0
0A
M
8.00
-9.0
0A
M
9.00
-10
.00
AM
10.0
0-11
.00
AM
Morning Period
Num
ber o
f Pas
seng
er T
rips
Towards NorthTowards South
Figure 3.6 MRTS Passenger Loading Hour Wise Variation – Morning
Period
527 385
2216
778 1007 1013398
2545
0
500
1000
1500
2000
2500
3000
3500
4.00
-5.0
0P
M
5.00
-6.0
0P
M
6.00
-7.0
0P
M
7.00
-8.0
0P
M
Evening Period
Num
ber o
f Pas
seng
er T
rips Towards South
Toward North
Figure 3.7 MRTS Passenger Loading Hour Wise Variation – Evening
Period
41
Table 3.5 Directional Split of MRTS Passengers
Direction Morning Period
Directional split during
morning hours
Evening Period
Directional split during
Evening hours
Total Directional split for the entire day
Towards South
12524 68 % 5116 29 % 17640 49%
Towards North
5326 32 % 13034 71% 18360 51%
Total 17850 100 % 18150 100% 36000 100%
Inter-station spacing refers to the distance between the two stations.
It influences the journey speed of the transit between the stations. The
sectional speed of MRTS is 72 Kmph. However, the current average
operating speed is 21-25 Kmph from Beach to Velachery. This is due to the
presence of closely spaced stations. A station like Mandaveli has the least
station load of 585 and it hardly contributes to passenger trips. Except
Kasthuribai Nagar Thiruvanmiyur and Velachery station load in all the other
stations in Phase II is less than 1000. Table 3.6 gives the inter-station spacing
and the station load of all stations.
In Phase 1 of MRTS, passenger movement among the MRTS
stations is moderate, i.e. commuters move from Thirumailai to Chepauk,
Beach for various purpose of trips. However, in Phase 2-commuter
movement among the MRTS stations is completely nil. When stations are
spaced so close such as 0.8 Km, the demand in MRTS has been very low.
42
Table 3.6 Inter Station Spacing of MRTS
Origin Station Destination station Inter station Distance
in Km Beach Fort 1.70 Fort Park Town 0.84 Park Town Chintadripet 0.89 Chintadripet Chepauk 1.57 Chepauk Triplicane 0.74 Triplicane Light House 1.21 Light House Thirumailai 1.71 Thirumailai Mandaveli 1.04 Mandaveli Greenways Road 1.32 Greenways Road Kotturpuram 0.87 Kotturpuram Kasthuribai Nagar 0.93 Kasthuribai Nagar Indira Nagar 0.97 Indira Nagar Thiruvanmiyur 0.86 Thiruvanmiyur Taramani 1.92 Taramani Perungudi 1.14 Perungudi Velachery 1.63
3.4.2 Bus Transport System
The high capacity MRTS had been introduced along an alignment
that has already well established Bus routes. To find the potential passengers
to use MRTS, the assessment of total number of passengers making trips by
bus along the MRTS influence area is presented. In addition, total number of
buses operated parallel to MRTS is also reviewed in this section.
43
The Bus routes operating parallel to MRTS in the influence are
identified based on the following criteria;
Routes touching at least one MRTS station.
Routes running within 1.5 Km from MRTS.
Routes that originate beyond Velachery, Thiruvanmiyur and
end the trips in MRTS influence area.
Routes that originate beyond Beach station and end the trips in
MRTS influence area.
The identified routes taken up for analysis are shown in Figure 3.8.
Parrys Corner, Thirumailai, Thiruvanmiyur and Velachery are the important
nodes which operate buses parallel to MRTS. The list of bus routes, number
of service and number of singles operated per day are shown in Table 3.7.
About 195 services are operated along the MRTS corridor making 2882 trips
per day.
The over lap length of these buses and MRTS ranges from 5 Km to
17 Km. Routes operated parallel to MRTS were identified and total number
passengers making trip per day in these routes are computed as 184571 based
on the following criteria;
Passengers traveling parallel to MRTS to a minimum distance
of 4 Km.
Passengers having origin beyond Velachery, Thiruvanmiyur
and destination in MRTS influence area.
Passengers having origin beyond Beach station and
destination in MRTS influence area.
44
Figure 3.8 Bus Routes Running Parallel to MRTS Taken up for Study
45
Table 3.7 Passenger Movement and Bus Movement along MRTS
Corridor
ORIGIN Route No. Destination Number of Single/Day
Velachery
PP51/EXT Parrys Corner 68 21L Parrys Corner 198 5G Thiruvanmiyur 128 1G Thiruvottriyur 36
Thiruvanmiyur
5G Velachery 128 1C Ennore 56 I Thiruvottriyur 288 6E Tollgate 12 6D Tollgate 164
Adyar
19 Injambakkam 60 5 Parrys Corner 80 19B Soliganallur 48 19K Sirucherri 32 21B Parrys Corner 40 21H Kelabakkam 72 M5 Soliganallur 48 M19 Injambakkam 80
Thirumailai / Chepauk
3A Parrys Corner 22 5K Taramani 144 M15 Medavakkam 48 45E Keelakattalai 48
Parrys Corner
3A Mylapore 22 5 Adayar 80 5C Taramani 84 18D Keelakattalai 44 18P Velachery 16 18 Indiranagar 16 19E Kovalam 10 19G Kovalam 100 19G-CUT Srinivasapuram 28 PP19 Injambakkam 148 PP19 EXT Kovalam 74 21P Indiranagar 96 21H Kelambakkam 192 21L EXT Keelakattalai 28 52 K Keelakattalai 84
Total 2822
46
3.5 DATA COLLECTION
In this section, a detailed methodology is presented for conducting
various primary surveys needed to quantify the factors influencing mode
choice parameters like travel time, travel cost (mode wise) and accessibility to
bus stops and MRTS stations. In addition, to understand the travel
characteristics and travel pattern of the commuters the methodology to
conduct opinion survey is also explained.
3.5.1 Opinion Survey
The opinion survey helps one in identifying the actual desire of the
people. It provides the base for developing any model for likelihood
estimations. In this work the opinion survey was designed in such a way that
it helps to identify the influencing factors for travel of the people by MRTS
and bus. The opinion survey was conducted among the MRTS users, bus
users and employees having offices along the MRTS corridor.
The major objective of the opinion survey is to identify the reasons
for not preferring the MRTS and desirable factors for switch over. To enable
the process, a questionnaire was designed with multiple choice questions to
collect the people opinions. Formats of questionnaire are shown in Appendix
2 and 3. In this work direct interview method was adopted with survey
questionnaire to obtain the opinion. The persons to be interviewed were
selected by random sampling method.
3.5.2 Estimation of Travel Time between Two Modes
The travel time between two nodes includes the in vehicle time,
dwell time and access time. The in-vehicle travel time is measured by using
47
the GPS. Conventionally the travel time was measured manually with stop
watch. The major disadvantage of this method was the manual assumption
involved and that may lead to mis-prediction and prone to error. Hence, GPS
was used in this study.
The probe vehicle and the route were selected first and the GPS
was fitted to the probe vehicle. In this work the probe vehicles were MRTS
and bus. The GPS will automatically record the data like altitude, speed,
acceleration, latitude and longitude. Finally when the destination was reached
the GPS recording was stopped. GPS Visualizer, Data Processing software
were used to extract the data and the processed data is then used to compute
the travel time precisely.
3.5.2.1 Dwell Time Survey
The dwell time is the time that bus spends at stopping to serve the
passengers at a specific stop. Levinson (1983) and Rajbhadri (2003) state that
the dwell time can account for up to 26 percent of the total travel time. Hence
a detailed study is carried out in this work. The survey location is selected
along the MRTS corridor. Three persons were involved in this study, one
person recorded the arrival time and departure time of buses, second person
recorded the number of alighting persons and standing inside the bus and the
third person recorded the number of boarding commuters in the bus. The
collected data was processed and dwell time model was developed.
The bus dwell time consists of the time needed for the following
events to occur; 1) passenger boarding, 2) passenger alighting. If the number
of passengers alighting and boarding are independent of each other, the dwell
time was expressed by the sum of two random variables as shown in
Equation 3.1. The marginal passenger alighting and boarding time per
48
passengers was assumed constant. The proposed model for estimating dwell
time is given in Equation 3.1.
αd = µb + σa + ρs (3.1)
where,
αd = Bus dwell time in Seconds. µb = Number of passengers boarding
σa = Number of Passengers alighting
ρs = Number of passengers standing
inside the bus
3.5.3 Accessibility Survey
The accessibility survey is the direct appraisal survey done by a
group of experts. The members assessed the status of accessibility level in
terms of feeder service, approach road conditions, parking facility, bus stop
locations, etc. The accessibility readings are analyzed and weightage is
assigned for each category of options as discussed in section 2.4.2.
Accessibility plays an important role in any system. If a system is provided with
excellent facilities and if there is no accessibility then it will discourage the
passengers to use the system. Table 3.8 shows the various accessibility
indices for the MRTS and bus system.
Table 3.8 Accessibility Indices
Attributes Nature of Attributes Weightage Assigned
Feeder Service availability at MRTS Stations
Yes 1 No 0
Location of Bus Stop from MRTS Stations
Bus stop < 200m 2 Bus stop with in 200-500m 1 Bus stop with in >500m 0
Approach Road condition Good 1 Poor 0
Parking Facility availability at MRTS Stations.
Available 1 Not Available 0
49
3.6 PROPOSED LOGIT MODEL FOR MODE CHOICE
In this section the proposed Logit model for mode choice
estimation is presented. The mathematical models derived from Random
Utility Theory is the richest and tried extensively for the simulation of
transport related choices and choices among discrete alternatives. Random
Utility Theory is based on the hypothesis that every individual is a rational
decision maker, maximizing utility relative to his/her choices. The underlying
important assumptions are:
An individual is faced with a finite set of choices from which
only one can be chosen.
Individuals belong to a homogenous population, act rationally,
and possess perfect information and always select the option
that maximizes their net personal utility.
If C is defined as the universal choice set of discrete
alternatives, and “ j ” the number of elements in C, then each
member of the population has some subset of C as his or her
choice set. Most decision-makers, however, have some subset
Cn, that is considerably smaller than C. It should be
recognized that defining a subset Cn, that is the feasible choice
set for an individual is not a trivial task; however, it is
assumed that it can be determined.
Decision-makers are endowed with a subset of attributes xn
X, all measured attributes relevant in the decision making
process.
In the case of only two alternatives A and B in a choice set (like
Bus and MRTS) and if the alternatives have systematic utilities of say UBUS,
50
UMRTS, the measure of utilities UBus, UMRTS is the function of travel time,
travel cost and accessibility which may be expressed as in Equation 3.2
U Bus / MRTS = a0 + al x1 + a2.x2 + a3.x2 …….an.xn (3.2)
where
UC BUS : Utility measure of Bus transport
UC MRTS : Utility measure of MRTS
a1, a2, a3, an : Utility coefficients of Bus/ MRTS
x1, x2, x3, xn : Utility Parameters
Based on the above utility equations, the proportion of travel
demand is estimated using the multinomial Logit model as given in
Equations 3.3 and 3.4. In this model e(i)’s of choice utility function are all
assumed to be independent and are identically distributed exponential
function.
Bus
Bus M RT S
( U )
( U ( U) )P(BUS)
(3.3)
MRTS
Bus MRTS
(U )
(U (U) )P(MRTS)
(3.4)
where,
P(BUS) : Proportion of travel demand for Bus
P(MRTS) : Proportion of travel demand for MRTS
3.7 PROPOSED ANN MODEL FOR MODE CHOICE
In this study an ANN architecture which was never adopted before
in literature is used to address some of the main drawbacks of existing ANN
models, namely the unclear interpretation of parameters and the absence of an
explicit utility function, which do not allow for elasticity analysis and, more
51
generally, make the generalization of the resulting models arguable. The
proposed architecture separately specifies the function, assumed linear,
between attributes and mode systematic utility. The function is modeled
through a hidden layer between mode systematic utility and choice
probabilities. This way, an explicit utility function can be specified, as
commonly for Random Utility Models, the calibration of such structure
allows an interpretation of input variables, while the relative weights allow to
carry out an elasticity analysis.
The proposed architecture contains four layers as shown in
Figure 3.9; 0. Attribute (Input) layer, regarding relevant attributes, with one
processing unit for each attributes 1. Utility layer, regarding systematic utility,
with one processing unit for each transportation mode, 2. Hidden layer,
regarding effect of utility on choices, with a number of processing units to be
defined, 3. Probability (Output) layer, regarding choice probabilities, with one
processing unit for each transportation mode. The whole model can be
described by equations between the processing units of each pair of
successive layers, as described below.
Layer 0. Given the values of j-th attribute for each mode k: xkj
calibration; the transfer function being the identity.
Layer 1. For each processing unit k (one per mode) of the utility
layer Vk as in Equation 3.5
vk = ∑j β kj xkj + ASAk (3.5)
where the weights kj, and the biases ASAk are to be estimated.
Layer 2. For a processing unit yn (their number being a design
option) of the hidden layer, Equation 3.6
52
yn = φ (∑k wnk vk + bn) (3.6)
where the weights wnk, and the biases bn are to be estimated during the
calibration; the transfer function φ( ●) being a design option.
Figure 3.9 Proposed Architecture of ANN Model
Layer 3. For a processing unit Pm (one per mode) of the
probability layer, Equation 3.7
Рm = Ψ (∑n znk yn + cm) (3.7)
where the weights znk, and the biases cn are to be estimated during the
calibration; the transfer function φ(●) being a design option. Eventually, to
compensate numerical errors, the output (probability) values Pm (non-negative
in any case) are normalized so that their sum is equal to one.
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3.8 SUMMARY
In this chapter, a brief description of the methodology of the work
carried out in this thesis is explained. Model development and data collection
process were given more importance. The land use disposition along the
corridor was analyzed and it was found that 35 percent are residential, 25
percent are institutional and about 15 percent are open and vacant land, 12
percent are industrial and 13 percent are commercial. The various travel
characteristics of MRTS system was studied and it was found that Beach,
Velachery and Thirumailai stations are the well-patronized stations. The
travel pattern of the people was analyzed and it was found that 184572
passengers are moving along the MRTS corridor by bus and they are the
potential passengers likely to get shifted to MRTS.
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