Engineering System

Group 6Team leader: Arnav Kumar Agrawal

Assistant Team Leader: Gurniamat Kaur

Introduction

Aim : To gather, analyse and propose feasible solutions for traffic congestion

problems in Hyderabad.

Traffic Congestion: In brief when demand exceeds capacity.

Demand, Capacity : Defined in terms PCU(Passenger Car Unit).

PCU: A Passenger Car Equivalent is essentially the impact that a mode of transport has on

traffic variables (such as headway, speed, density) compared to a single car.

Private Car: 1 unit

Motorcycle: 0.5 unit

Bicycle: 0.2 unit

Trucks/Buses: 3.5 units

Sub - Systems

1. Gathering of Case Studies

Collects Raw Data in a tabulated manner.

2. Data Analysis

Analyses Data for problems and identifies causes.

3. Propose Solution

Proposes solutions and classifies them upon parameters.

4. Solution Analysis and Generalization

Analyses the feasibility of solution based on different metrics and check their

scalability.

Use Case Diagram

Work Done

1. Kukatpally

2. Mehdipatnam

3. Malkajgiri

We have worked on case studies in above three areas. For presentation we will

go through the work done in kukatpally Area.

Sub System 1

Data Collection

1. Purpose

Gather statistics/data

To enable analysis

Suggest Techniques to automate data gathering.

2. Requirement

Data collection team is asked to provide validated and reliable data that can be

used for further analysis on traffic congestion problems related to Hyderabad city.

Procedure

Data is collected through various techniques discussed further.

Help is taken from the agency involved in the surveying for the Government

which is Hyderabad Metropolitan Development Authority

The data is classified on the basis of different parameters discussed in the

next slide.

The correlation between different categories is established.

Simulation method for future has been proposed

Data Provided on Parameters

Road Network Inventory

Traffic Volume Count

Pedestrian count

Broad Lane used

Parking Inventory

Speed and Delay

Use Case Diagram

selecting the

appropriate area

and collecting all

the available data

Providing the

categorized data

according to the

demand of the next

subgroup

Work Done in Kukatpally Case Study

Automation Techniques

Camera based

Radar based

Overhead Toll Collection

Limitations

Data is not up to date

Real time data collection is not possible

No manual data collection.

Sub System 2

Data Analysis Subsystem

Aim: To home in on relevant data with the help of threshold parameters and

analyse the same to identify problems and causes.

Questions answered by the system(at least 2-3):

What is congestion ?

How did you arrive at the problem ?

How do you find causes for the problem and classify them?

Procedure

Initially, for each road whose data was given type of lane was identified.

Using the data, the roads and junctions were classified into low, medium,

high congestion.

The roads and junctions with medium and high congestion were taken and the

possible causes of congestion were analyzed using the relevant data and

topological analysis was carried out using google maps.

Other parameters which were situation specific ( population, bus stops,

railway stations, etc) were also explored.

Using these we tried to find their causes.

The Causes were classified as Strong or Weak depending on the quantity of

data to support our claim.

Measures Of Congestion

1) PCU/hr :

PCU - It is a vehicle unit used for expressing highway capacity.

Type Weight

Car ,taxi,pick up 1.0

Cycle 0.2

Bus, truck,tractor 3.5

Motor cycle 0.5

2) Average speed :-If traffic congestion is more , average speed will be low

else it will be high .

3) Pedestrian count: It is the number of people walking past a location per

unit time.

Ordinary two

lane road(30 ft

width)

750 Dual carriageway

(60 ft width)

3000

Three lane road

(central

overtaking)

1400 Motorway (3

lanes each way)

6000

Index of Saturation ( in PCU /hr)

Threshold values

Category Limit(kmph)

Low Speed Zone 10 – 20

Medium Speed Zone 20-30

High Speed Zone > 30

1) Average Speed

Category Limit(PCU/hr)

Low Congestion Zone < Index of saturation

Medium Congestion Zone Index of Saturation – 2* Index of

Saturation

High Congestion Zone > 2 * Index of Saturation

2) PCU/hr

3) Pedestrian Count

Category Limit(Pedestrian / hr)

Low Pedestrian Movement 0 – 100

Medium Pedestrian movement 100 - 300

High Pedestrian movement 300 - 500

Very High Pedestrian movement > 500

Use Case Diagram

Work Done in Kukatpally Case Study

PROBLEM 1: TRAFFIC CONGESTION IN NH9 HIGHWAY – ROAD 4 EXIT

Data used:

Traffic counts of mid-blocks located along NH9 (2 lane road) like KPHB bus

stop to JNTU and METRO to KPHB are 5683 PCU/hr and 5926 PCU/hr

respectively, hence they fall under high congestion zone.

Cause identified:

Buses utilize road 1 to pick up passengers from KPHB phase 1 and 2 areas.

They exit from road 4 into the NH9. The right turn bank over there causes

traffic. This is a strong cause.

PROBLEM 2: TRAFFIC CONGESTION AT NH9 - JNTU JUNCTION

Data used:

Traffic Counts of mid-blocks located along NH9 (2 lane road) like KPHB bus

stop to JNTU and METRO to KPHB are 5683 PCU/hr and 5926 PCU/hr

respectively, which fall under high congestion zone.

JNTU junction has wider roads (JNTU-KPHB is a 4 lane C/W road) connecting

it but it has a very high traffic count of 7567 PCU/hr.

The pedestrian count at the JNTU junction towards KHPB bus stop(2 lane) is

728ped/hr which is very high.

One of the Causes identified:

Short-route and long-route buses both travel from JNTU road which is already

heavily congested and then takes a right turn to the highway 9 which causes

congestion.

Future Scope and Limitations

We have also made a excel of our case study which can be used for

automation i.e. We can extract the relevant Google maps which can be

helpful for better representation of our causes and also helpful for the next

sub system for better representation of their solutions.

The analysis depends solely on the quality and quantity of data provided.

Sometimes based on threshold parameters for traffic count, pedestrian per

hour, etc. we may ar rive at the problem but not its cause.

The data needs to be updated for ensuring good analysis.

Sub System 3

Propose Solutions

Aim

To look at the problems along with their causes given by the previous sub-system. To come up with solutions and classify them based on whether they are short-term, long-term , policy-based etc.

Input:

Data analysis of previous system. Review from next sub-system.

Output:

As many solutions as possible classified whether they are - short-term, long-term, policy-based, infrastructure based or technology-based.

What we set out to do

We had tried to analyze the current solutions implemented and where were they lacking. However we could not find revelant data for it. Also we could not do a cost-based analysis.

Procedure

1. Read and understood the report submitted by the previous sub-system.

Looked at the problems , their data behind it and the reasons for it.

2. Came up with as many solutions as possible without any restrictions.

3. Then, we eliminated some solutions if they were too infeasible in

terms of cost, construction or technology.

4. Classified the solutions whether they are short-term , long-term,

policy-based , infrastructure-based or technology-based.

5. Documented them with along with proper images of annoted maps to

serve as examples.

Use Case Diagram

Work Done in Kukatpally Case StudyBelow are the solutions for only two of the problems presented for the case-study of Kukatpally.

Problem 1

As mentioned in previous slides, local and long-range buses use the heavily congested JNTU road and take a right on highway number 9, causing congestion.

Solution Proposed

Buses on JNTU road come from Malaysian township circle. Rather than going straight, route the long-route buses on the 9th Phase road. 9th Phase road is equally wide and can support further load.

Long-route buses would skip some bus-stops but would not be much of a hindrance.

Analysis of solution covered by the next sub-system.

Solution Type: Short-term, Policy-based

4/10/2014

4/10/2014

Problem 2

Congestion caused by local buses taking right by exiting at Road number 4 on Highway 9

Solution Proposed

Rather than taking a right at Road number 4, go ahead straight and come back later from the other side of the road. However this solution was scraped by the next sub-system for reasons which they will explain in a moment.

Revised Solution Proposed

Currently buses exit only from road number 4. This can be spread over all the other parallel roads. Traffic will get spilled over rather than being concentrated.

Category: Short-term , policy-based

4/10/2014

Limitations

Could not visit the actual places. Had to rely on maps , internet and accounts of

people living around there.

Lack of availablity of proper data regarding actual solutions implemented.

Knowledge of current solutions would had helped us in coming up with better

solutions.

Data available is slightly old and thus may not be reliable.

Sub System 4

Solution Analysis and Generalisation

Aim : Given as input a problem statement ,its cause and the solution and

some specific data , the aim of our subsystem is to prepare a solution analysis

report by doing a qualitative and quantitative analysis of the solution and

identify the weak points and strong points of the solution and also give some

suggestions for improvement and alternate solutions and also comment on the

scalability of the solution.

Question and answer

Do we also look for past solutions to see the similarity to the problem or to

propose alternate solutions?

Yes , we do

How do we judge the feasibility of the solution?

We check the feasibility of the solution in terms of :-

1.cost

2.technology

3.long-term or short -term solution.

Procedure

1. Understand the problem in hand its cause and the solution proposed.

2. Try to find out the rationale behind implementing such a solution.

3. Do mathematical analysis on the data specific to the given problem if

possible.

4. Do an quantitative analysis based on the results of the mathematical analysis.

5. Also perform a qualitative analysis.

6. In this process , identify the weak and strong points of the solution and also

comment on its scalability.

7. Finally give any suggestions or alternate solutions.

Metrics used

Quantitative

1. PCU

2. Average speed

3. Pedestrian count

Qualitative

1. Eco friendly solution or not?

2. Does it compromise on the safety of commuters?

3. Does it lead to greater satisfaction of commuters?

Use case diagram

Kukatpally Analysis

Problem: High traffic congestion and slow moving traffic at the jntu

junction(intersection of jntu and highway 9).

Cause: Buses usually take a long time to take a right from the jntu junction to

the mumbai highway.

Solution proposed:

Route the long -route buses to from the 9 – phase kphb circle on to the 9-phase road

.From the 9th phase road,buses can enter the highway from road no 1.

Rationale:

1. Reduce load considerably on the JNTU road.

2. Some bus -stops would be skipped , but for long – route buses (going outside the

city),these donot matter very much.

Distance(phase circle 9 – jntu jnuction) = 1.47 km

Avg speed(phase circle 9 – jntu junction ) = 22 km/hr

Avg time taken(phase circle 9 – jntu junction ) = 4 min.

Traffic volume(jntu junction )= 7567 pcus/hr .

Traffic volume(phase circle 9 – jntu junction )=2155 pcus/hr

In 4 min , and over 1.47 km ,pcu = 2155*(4/60)=144 pcus.

For 500m , pcu = 144*(500/1470)=50 pcu .

Mathematical Analysis

Using data, There are 6 buses ( in 500 m ) , and the no of bikes and cars are

in the ratio of 1:1 bus- 4 pcu/vehicle. Bike-0.5 pcu/vehicle car-1 pcu

/vehicle.

No of buses in 500 m - 3

no of cars in 500m – 25

no of bikes in 500 m – 25

no of buses in 1.47 km – 9 buses . In ( 4 min).

In 60 min- 9*(60/4)= 135 buses. 60 buses are long route buses. Therefore

reduce in puc is 60*4= 240. So now Traffic volume(jntu junction )= 7327

PCUs/hr .

Still in the same range(saturated ). ( but it leads to 240 units decrease in PCU

and also the avg speed has come into the medium range).

As the avg. Speed increases , we say that the sol'n is good.

Traffic volume(phase circle 9 – jntu junction – road 1 int. Road)=1915 pcus/hr

Avg speed(phase circle 9 – jntu junction ) = 2155*22/1915=25 km/hr.

Distance(phase circle 9 – jntu junction – road 1 int.) = 2.2 km

Avg time(phase circle 9 – jntu junction – road 1 int.) = 8 min

Avg speed(phase circle 9 – jntu junction – road 1 int) = 17 km/hr

Avg speed(phase circle 9 – jntu junction ) = 22 km/hr

Distance(phase circle 9 – jntu jnuction) = 1.47 km

Therefore avg speed(jntu junction – road 1 int) = 7 km / hr.

So imp avg speed(phase circle 9 – jntu junction – road 1 int) = 7*(2.2-1.47) +

(1.47*25) = 19 km/hr

We see that now the avg speed comes now in the medium category.(almost 20-

30 km/hr.)

Distance(phase circle 9 -road 1 int) = 2.1 km

Avg time ( phase circle 9 – road 1 int) = 5 min.

Avg speed (phase circle 9 – road 1 int) = 24 km / hr.

Pcu increase = 240 pcu /hr .

Reduce in avg speed ( phase circle 9 – road 1 int ) = 3 km / hr .

Avg speed ( phase circle 9 – road 1 int ) = 21 km/ hr.

We see that the avg speed in this road is still in the medium category(20-30

km/hr).

Quantitative Analysis

Reduce in Traffic volume(jntu junction )= 240 pcus/hr .

Reduce in Traffic volume(phase circle 9 – jntu junction )=240 pcus/hr

Increase in avg speed( phase circle 9 – jntu junction – road 1 int ) =2 km/ hr.

Increase in avg speed( phase circle 9 – jntu junction ) =3 km/ hr

Reduce in avg time (phase circle 9 – jntu junction ) = 0.5 min

Reduce in avg time (phase circle 9 – jntu junction – road 1 int ) = 1 min.

The solution proposed is effective.It leads to a decrease in traffic volume on the

busy JNTU junction.It also reduces the avg time taken by the other commuters

by atleast 1 min.Also the alternate route suggested is infact 100 m short in the

distance and the avg time taken on it is only 5 min and after the routing it may

increase by atmost 1 min . This is surely a minor inconvenience as compared to

the previous scenario . Also on that course very less bustops or no would be

acually affected. Although it leads to an increase in 240 pcu on the routed road

, the tradeoff works in our favour as the jntu junction is relieved of the slowly

turning buses.

Cost of the solution is none.

Technologically feasible.

Feasible in sense of time taken to implement.(very less).

Qualititative Analysis

1. Eco -friendly solution.

2. Leads to greater satisfaction amongst commuters on the road.

3. Leads to greater safety of commuters crossing the jntu junction road by

decreasing the no of buses entering on the junction and hence decreasing

the heavy traffic.

Long – term or short –term:

The proposed solution would not be hold for long . As the population increases ,

the no of vehicles would increase on the road , and once again there will be a

problem of congestion at the jntu junction.

Scalability:

Its not necessary that the proposed solution can be generalised to any such sort of

case of traffic congestion . It worked in this case because of the characteristics of

the logistics of the alternate routing road and because of the presence of it.

Suggestions:

In the initial phases, a constable can be appointed at the JNTU junction to make

sure that none of the long route buses use the route and if they use they will have

to pay a penalty.

Try to search if more solutions exist , that have the least no of right turns and try to

maximise the left turns as left turn is a free turn.

Alternate Solutions:

none.

Problem 2

Problem:

Slowing down of traffic on Mumbai Highway (especially at the road-1 and road-4 intersection with the highway).

Cause:

Buses usually take a long time to take a U-turn leading in slowing down of traffic.

Solution proposed:

Let the buses instead go straight into the other side of the road and then route back. Buses on Road no 4 can use Road number 6 to go straight into the other side and then gradually take a U-turn and re-enter the highway from a bit further down the road.

Rationale:

This will increase the speed of flow and also allow buses to pick up more passengers.

Analysis

What is being proposed is actually not feasible in the sense that minor details of the problem were not looked in the detail. The problem is that the solution proposed that talks about going straight instead of taking a U-turn is not feasible as there is no straight path to that road. One can not

Go on that road unless one takes a U-turn from further down the highway. So after the bus has taken the U-turn it may as well take that highway route instead of going for the longer and less wide road route.

Alternate solutions:

To address the problem of slowing down of traffic on highway

What can be done is , not all buses exit from road no 4 but some of them exit from road no -2 , some from road no -3 and some from road no -5. So atleast the overall load is not only on one turn and it gets distributed.

The initial analysis itself showed flaws in the solution and in the understanding of the problem statement .Therefore further analysis wasnot thought to be necessary.

Challenges:

To improve our analysis by improving the metrics used for analysing the solution and

if necessary including extra.

Limitations:

There are many parameters required to judge such kind of a solution so we have

restricted as to a limited number of parameters.

Challenges and Limitations

System Interface

(Web Portal)