Traffic Congestion “Gap” Analysis in India

Tsutomu Tsuboi

and Tomoaki Mizutani

Nagoya Electric Works Co. Ltd., 29-1 Mentoku, Shinoda Ama-shit, Aichi, Japan

Keywords: Traffic Flow, Traffic Congestion, Developing County, Traffic Jam.

Abstract: This study is more than one-month traffic flow observation in India and introduces new traffic congestion

“Gap” from the analysis of real traffic flow analysis in India. Traffic congestion becomes serious problem

especially in developing countries such as India. In general, it is quite challenging to collect traffic data and

understand traffic congestion problem from its data analysis. In this study, it is the first time to show long

term traffic monitoring at one of major junction in Ahmedabad city of Gujarat state India. IAs for traffic

congestion analysis, the following challenges are executed with a collaboration from local city government.

Step 1 is to select location for 8 months observation by traffic monitoring camera in the city. Step 2 is to

analyse traffic flow at the junction from each direction traffic flow. Step 3 is to evaluate traffic congestion

with traffic flow parameter from traffic flow theory. Step 4 is to analyse geographical mapping by GIS tool.

Based on these steps, it reached to unique traffic congestion mechanism in the junction, which it is named

congestion “Gap” and large traffic volume is not always a case of traffic congestion. From this result, there is

a possibility to improve traffic management when more detail observation at certain time of traffic congestion

happing and environmental condition such as traffic signal control, road infrastructure structure and so on.

1 INTRODUCTION

This study is a series of the traffic flow analysis in

India under India and Japanese government funded

project as Science and Technology Research

Partnership for Sustainable Development or

“SATREPS”, which is an international joint research

targeting global issues.

In general, traffic congestion becomes global

issue for low carbon scenario especially in developing

countries such as India. Developing countries have

same kind of problem for traffic management because

of budgetary issue. The government faces un-balance

between their rapid economic development and

infrastructure improvement preparation. In order to

find actual problem for transportation, there are so

many things to be prepared at the same time－road

expiation, enough traffic signal installation, public

transportation support, and so on. In transportation

study for developing countries, they just started. For

example, A. Salim et al. used traffic density and space

headway parameters to analyze traffic congestion.

And B. Chanda reported vehicle probe data in terms

of the traffic volume and speed in Hyderabad, India,

https://orcid.org/0000-0002-6962-3447

based on the Indian Road Standard IRC-106-1990.

Those studies based on short time measurement like

four days so on.

In this study, we use traffic monitoring camera or

traffic monitoring camera for collecting traffic

condition on the road such as number of vehicles,

average vehicle speed, gap between vehicles, size of

vehicles very minute during eight months from

January 2019. The monitoring field is the west side of

Ahmedabad city of Gujarat state in India, where its

population is over 8 million in 2018 from 5 million in

2011 and the number of vehicles is about 4 million in

2017. More than 70% vehicle is two wheelers, which

is typical percentage in developing countries. The city

profile is shown in Table 1.

Table 1: Profile: Ahmedabad City.

Co-ordinates:

23.03° N 72.58° E

Area:

466 Sq.km. (year 2006)

Population:

55,77,940 (year 2011 Census)

Density:

11,948 /sq.km

Literacy Rate:

89.60 %

Average Annual

Rainfall:

782 mm

Tsuboi, T. and Mizutani, T.

Trafﬁc Congestion “Gap” Analysis in India.

DOI: 10.5220/0010444604810487

In Proceedings of the 7th International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS 2021), pages 481-487

ISBN: 978-989-758-513-5

481

In our relted studies, the traffic congestion

Tsuboi.T. shows that occupancy parameter is one of

capable parameter for traffic congestion condition,

especially in India. In general, traffic congestion is

caused by large traffic volume and slow vehicle

speed. From one-year traffic observation in

Ahmedabad city, the peak of traffic volume happens

in the morning and the second peak occurs in the

evening. However, the congestion occurs in the

second peak of traffic volume in the evening, which

means large traffic volume is not always main reason

for traffic congestion.

From the above general condition, it is focused on

the traffic condition at one of major junction where

there are four traffic monitoring cameras in each

crossroad in order to measure all direction vehicle

movement. The other traffic monitoring cameras in

the city face one direction of their roads, therefore it

is difficult to observe total vehicle movement. In the

next Section 2, it is described the environment

condition including traffic monitoring camera

location and social information data e.g. population in

map. In Section 3, it shows measurement data of one-

month April 2019 example for eight months

monitoring and traffic flow analysis. In Section 4,

there is discussion about traffic congestion “Gap”,

which we find unique traffic flow phenomena

analysis result. And then in Section 5, we conclude

this study.

2 TRAFFIC OBERVATION FIELD

We choose Ahmedabad city of Gujarat state in India

as urban transportation analysis place. The selection

reason is that Ahmedabad is one of typical growing

city in India and there are negative impact caused by

heavy traffic congestion such as air pollution, traffic

fatality, accidents, logistic delay and economical loss

etc. On the other hand, local government, or

Ahmedabad Municipal Corporation (AMC) has lot of

improvement challenge such as Buss Rapid

Transportation (BRT), Metro development, and high-

speed train (Bullet Train) plan and so on.

2.1 Field Environment

The field environment is shown in Figure 1.

▪ The number is traffic monitoring camera

installed location

▪ Red and Blue line shows Metro (under

construction)

▪ Target junction is Paldi (Camera No.2001 ~

2004)

Figure 1: Traffic monitoring Field (number indicates traffic

monitoring camera location, d circle is Paldi junction

location, and red and blue line shows Metro

underdevelopment).

In terms of social information, Figure 2 shows 1-

kilometer mesh areal interpolation population and

traffic monitoring camera location. The dark colour

shows denser of population which shows black dot

mark. From Figure 2, a greater number of populations

is in the east side of the city across the river because

the east side is called “old town” and many residents

live there. On the other hand, the west side of city is

called “new town” and there are new office buildings

and shops. Therefore, it is expectable rush hours in

the morning and in the evening at Paldi junction.

Here is some assumption about North-South

traffic flow direction particularly at Paldi junction

from this social environment. This assumption is

explained in Section 3 later.

▪ People movement from North to South in the

morning

▪ People movement from South to North in the

evening

Paldi Junction

Sabarmati River

VEHITS 2021 - 7th International Conference on Vehicle Technology and Intelligent Transport Systems

482

Figure 2: 1-Kilometer mesh with areal interpolation

population and traffic monitoring camera location (red

circle shows Paldi junction location and black dot marks are

traffic monitoring camera position) .

2.2 Paldi Junction

In Figure 3, it shows more detail location for traffic

monitoring cameras at Paldi junction.

Figure 3: There are four traffic monitoring cameras and

each traffic monitoring camera face to the centre of the

junction (Number shows traffic monitoring camera

location).

Each traffic monitoring camera monitors the number

of vehicles and average speed. For example, it

measures its traffic flow data of the vehicles which

come from North to the junction at Camera 2004. At

the centre of Paldi junction, there is a Surveillance

camera which can take high definition visual

condition and is remote controlled 360 degree. In

Figure 4, traffic monitoring camera and Surveillant

camera pictures are shown.

Figure 4: Traffic monitoring camera (left picture) and

Surveillant camera (right picture).

3 MESUREMENT & ANALYSIS

In this section, it shows actual traffic measurement

data and analysis at Paldi junction as Step 1. The

traffic data is collected by four traffic monitoring

cameras during April 2019 as an example data from

eight months monitoring. As for comparison

reference, another hourly measurement data is shown

in Appendix later (traffic volume and average vehicle

speed in January 2019. The trend of traffic volume is

similar with that in April).

3.1 Traffic Flow Data

As Step2, it focuses on the traffic flow especially

from North to South which is measured by Camera

2002 and 2004 as mention in the previous assumption.

The camera 2001 and 2002 data are shown in

Appendix. At first, Figure 5 shows the time-based

traffic volume at camera 2002 and 2004. The traffic

volume is one of traffic flow parameter and it is

defined as number of passing vehicle on the road per

hour.

(a) Traffic Volume at 2002 (b) Traffic Volume at 2004

Figure 5: Traffic Volume at camera 2002 and 2004.

167 meter

174 meter

169 meter

149 meter

2002

2001

2003

2004

Surveillanc

e camera

New town

Old town

Sabarmati River

Trafﬁc Congestion “Gap” Analysis in India

483

From Figure 5, traffic volume peak point is contrary

relevant in the morning 10:00 and evening 18:00. In

the morning, many vehicles go from North to South

for their business, then the traffic volume at 2004 is

larger than that of 2002. In the evening, it supposes

majority vehicles direction between 2002 and 2004 is

changed because of returning home.

In terms of traffic congestion, traffic occupancy is

capable to indicate its congestion rather than traffic

volume from previous study. In Figure 6, the

occupancy is shown.

(a) Occupancy at 2002 (b) Occupancy at 2004

Figure 6: Occupancy at camera 2002 and 2004.

As Step 3, it introduces the occupancy (OC)

which is also one of traffic flow parameter to analyse

quantitative traffic congestion. This is defined as

vehicle occupation percentage of road. (OC) is

calculated from traffic volume (q) and average

vehicle speed (v) by Equation (1) from traffic flow

theory.

𝑂𝐶 = 100 ×

𝑞

𝑣

× 𝑙

(

)

(1)

where (q) is traffic volume, (v) is average vehicle

speed, and 𝑙

is average vehicle length.

When it is compared between traffic volume trend

of Figure 5 and occupancy trend of Figure 6, traffic

volume does not always show its traffic congestion.

For example, from traffic monitoring camera 2004,

the highest peak of traffic volume occurs at 10:00, but

congested peak by occupancy occurs at 20:00. From

traffic monitoring experience, heavy traffic

congestion occurs over 20% occupancy. Therefore, in

case of traffic monitoring camera 2002, the traffic

volume in the morning is high but occupancy level is

less than 20%. This case is North and South traffic

movement. In terms of traffic flow direction, each

traffic monitoring camera faces towards the centre of

the junction. The traffic monitoring camera 2002

faces to the South and camera 2004 faces to the North.

Therefore, in the morning, the traffic volume from

North to South which means traffic volume of camera

2004 is higher than that of camera 2002. So, majority

of traffic flow moves from North to South. On the

other hand, the traffic volume from South to North in

the evening which means traffic volume of camera

2002 is higher than that of camera 2004. Majority of

traffic flow moves from South to North. The traffic

congestion occurs at 20:00.

From Figure 6, the evening traffic congestion

occurs at 20:00. But from Figure 5, the evening traffic

volume becomes peak at 18:00. There is two hours

“Gap” between each peak of occupancy and traffic

volume somehow. This “Gap” comes from the

balance between traffic volume and average vehicle

speed from Equation (1). This point is discussed in

the next section.

At the end of section, let’s check traffic flow from

east to west, which is based on measurement data

from traffic monitoring Camera No.2001 and 2003.

The traffic volume of both traffic monitoring

Camera 2001 and 2003 is shown in Figure 7.

(a) Traffic volume at 2001 (b) Traffic volume at 2003

Figure 7: Traffic volume at camera 2001 and 2003.

From east-west traffic volume observation in Figure 7,

▪ People movement from West to East in the

morning

▪ People movement from East to West in the

evening

In terms of occupancy at camera 2001 and 2003, it

shows hourly occupancy trend in Figure 8.

(a) Traffic Volume at 2001 (b) Traffic Volume at 2003

(a) Occupancy at 2001 (b) Occupancy at 2003

Figure 8: Traffic Occupancy at Camera 2001 and 2003.

VEHITS 2021 - 7th International Conference on Vehicle Technology and Intelligent Transport Systems

484

In this case, traffic congestion occurs at 20:00 in both

location. Here is congestion “Gap” between traffic

volume and occupancy again.

As Step 4 when it focuses on traffic congestion of

Paldi junction at 20:00, the three-dimensional

occupancy condition in Figure 9 provides its traffic

congestion image. In case of North and South, there

is heavy congestion in North. In case of East and West,

there are light congestions in both side. In Figure 10,

two GIS map at 10:00 and 20:00 in 26

of April are

shown as an example of traffic congestion situation.

Figure 9: Occupancy at Paldi junction in April 2019 20:00

(value shows the level of occupancy).

(a) Paldi junction occupancy at 10:00

(b) Paldi junction occupancy at 20:00

Figure 10: Occupancy at Paldi junction in 26

of April.

From Figure 9, it is clear that traffic congestion location is

different―south area in the morning and north area in the

evening.

3.2 Congestion Analysis

In the previous section, occupancy is one of

appropriate parameter for showing traffic congestion.

When we investigate the relationship between traffic

volume and occupancy from measurement, it shows

the summary of traffic volume and occupancy among

four traffic monitoring measurement on 26

of April

2019 in Figure 11. The number of traffic volume is

average among four traffic monitoring cameras and

its data unit is unified number of vehicles per hour per

lane.

(a) Traffic Volume at Paldi junction

(b) Occupancy at Paldi junction

Figure 11: Comparison between Traffic Volume and

Occupancy at Paldi junction on 26

of April 2019.

As mentioned earlier, there is congestion “Gap”

between traffic volume and occupancy peak. The

traffic volume peak occurs at 10:00 and 18:00 and the

Occupancy peak at 10:00 and 20:00. Time “Gap”

between traffic volume and occupancy in the evening

is two hours.

From this analysis result, traffic congestion is not

always happed under heavy traffic volume and there

must be some other reason behind. If heavy traffic

volume creates congestion, it should be happened in

the morning. But based on one-month traffic flow

observation, there is no traffic congestion in the

morning. And another important fact is why traffic

congestion “Gap” occurs in the evening, NOT in the

morning. These two points are decried in the next

Discussion section.

Trafﬁc Congestion “Gap” Analysis in India

485

4 DISCUSSION

Let’s take one moment for traffic volume trend at

Paldi junction again. In Figure 12, it shows total four

traffic monitoring cameras time-based traffic volume

change in April 2019.

Figure 12: Accumulated Traffic Volume time-based change

at Paldi junction.

There are two peaks of traffic volume at 10:00 and

18:00. In case of occupancy, it shows total four traffic

monitoring cameras time-based traffic volume

change in April 2019 in Figure 13.

Figure 13: Accumulated Occupancy time-based change at

Paldi junction. From Figure 12, there are two peaks of

occupancy at 10:00 and 20:00. It is clear that there is

congestion “Gap” between traffic volume and

occupancy.

Table 2: Summary of Traffic Congestion “Gap”.

Cam

Congestion time

q max time

Congestion

“Gap” (hours)

No.

2001

―

20:00

10:00

18:00

―

2002

10:00

20:00

12:00

18:00

2003

10:00

20:00

10:00

18:00

2004

11:00

20:00

11:00

18:00

Table 2 summaries comparison between

congestion peek time from occupancy and traffic

volume peek time from traffic volume. There is two

hours congestion “Gap” of all location at Paldi

junction. In case of camera 2001, occupancy peek in

the morning occurs at 5:00. This situation comes from

no congestion of camera 2001 at 10:00 from Figure

13. Other camera 2002, 2003, and 2004 have third

peek of occupancy at 5:00 as well.

The Paldi junction environment is shown in

Figure 14 as an example of snapshot. In the junction,

there are traffic signal lights at each corner. And the

traffic signal control is used round lobbing access

with fixed time interval. There are several fixed time

interval selections and it is selected depend on its

traffic flow condition. This is typical Indian traffic

signal control method and it is necessary to have

detail traffic flow analysis related with traffic signal

control system in future.

Figure 14: Example Paldi junction traffic condition.

From the above all traffic flow measurement

observation long term and traffic flow analysis, it is

not clear why traffic congestion occurs in the evening,

NOT in the morning. We have several local traffic

officer’s discussions about Ahmedabad traffic

congestion issues and we found one of congestion

reason was transport behaviour as follows:

▪ Residents go straight to their office in the

morning by own private vehicles and then they

park their vehicle at certain regular parking

space.

▪ Some residents return straight to their home

after work but some go to shopping and or

restaurants for dinner in the evening. And there

is some difficulty to find parking space. In

general, there are not so many appropriate

parking area in India. Some people park along

the street when they are lucky to find the space.

If not, some people park their vehicles not

allowance space on the street, which makes

narrow the road width eventually.

▪ Local police officer does some time control its

traffic by manual because the fixed time interval

control does not effectively work for traffic

congestion, especially in the evening.

VEHITS 2021 - 7th International Conference on Vehicle Technology and Intelligent Transport Systems

486

In order to find out the reason for daily traffic

congestion in Ahmedabad, it is not only to have more

data but also check actual traffic condition time and

day such as investigation at 20:00 weekday. It is also

worth to have workshop among road management

group including traffic police and interviews to

residents. We also have other traffic monitoring

cameras s under the project and continue this traffic

management research by March 2022. As mentioned

earlier, the first city Metro is under development in

Ahmedabad and it will help to provide more

appropriate transportation choice to residents in near

future.

ACKNOWLEDGEMENTS

We appreciate support and collaboration from

Ahmedabad Municipal Corporation and local road

management authority as city traffic police and Smart

City Ahmedabad Development Ltd. This study

project was part of Program ID JPMJSA1606 of the

International Science and Technology Cooperation

Program (SATREPS) for global challenges in 2016.

REFERENCES

A.Salim, L.Vanajakshi, C.Subramanian, Estimation of

Average Space Headway under Heterogeneous Traffic

Conditions, International of Recent Trends in

Engineering and Technology, Vol. 3, No.5, 2010.

M.Goutham and B.Chanda, Introduction to the selection of

corridor and requirement, implementation of IHVS

(Intelligent Vehicle Highway System) In Hyderabad,

International Journal of Modern Engineering Research,

Vol.4, Iss.7, 2014, pp.49-54.

Population of India [Internet] 2020 Available From:

https://indiapopulation2019.com/population-of-ahme

dabad-2019.html [Accessed: 2020-08-21]

Registered number of vehicles Ahmedabad India FY 2006-

2017 Available From: https://www.statista.com/statis

tics/665754/total-number-of-vehicles-in-ahmedabad-

india/ [Accessed: 2020-08-21]

Ahmedabad Municipal Corporation Avilable From:

https://ahmedabadcity.gov.in/portal/jsp/Static_pages/i

ntroduction_of_amdavad.jsp [Accessed: 2020-08-21]

Tsuboi, T., 2018, Traffic Service Quantitative Analysis

Method under Developing Country, The 7th

International Conference on Advances in Computing,

Communications and Informatics (ICACCI).

Tsuboi, T., 2019, Traffic Congestion Visualization by

Traffic Parameters in India, 2nd International

Conference on Innovative Computing and

Communication (ICICC).

Tsuboi, T., 2019, Time Zone Impact for Traffic Flow

Analysis of Ahmedabad city in India, 4

International

Conference on Vehicle Technology and Intelligent

Transport System (VEHITS).

Tsuboi, T., 2020, New Traffic Congestion Analysis Method

in Developing Countries (India), 5

International

Conference on Vehicle Technology and Intelligent

Transport Systems (VEHITS).

APPENDIX

Here is a reference data of traffic volume and

occupancy during 8 months from January to August

2019 in Figure A. The characteristics of traffic

volume and occupancy is based on all four traffic

monitoring camera and its value is used as average

data. Each characteristics are same trend by each

month, day, and time. Therefore, the analysis result

which is described in this paper is same result even if

it is taken other month or day.

(a) Traffic Volume trend during January to August 2019 at

Paldi junction

(b) Occupancy trend during January to August 2019 at Paldi

junction

Figure A: Long term Q and OC trend at Paldi junction.

Trafﬁc Congestion “Gap” Analysis in India

487