M2SMART PROJECT

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M2SmartNewsLetter

Vol.5
Sept 2019

MEETING AT IITH M2SMART LAB

M2Smart Lecture at IITB and Internshipby Tsutomu Tsuboi (Project Leader)
M2Smart Project is getting more
attention in India!
On 24th of July, Dr.Tsuboi was invited
to give a lecture at IIT Bombay. This
lecture contained the introduction ofthe M2Smart Project activities andthere were 20 students and 6
professors from 7 faculties. Prof. D.
Manjesth in the Electronic Engineering(EE) Department of IIT Bombay
organized this lecture and exchanged
traffic flow analysis information. His
laboratory has Indian traffic flow
behavior model by vector matrix,
which simulates Indian driving
movement.

Between 21-22nd of July, Dr.Tsuboi
visited Nippon Signal Company whichis the metro developer in Ahmedabad.
The road condition monitoring in
Ahmedabad by smartphone has been
conducted by Dr.Tsuboi.
From 19th of August to 30th, Nagoya
Electric Works had an intern from theUniversity of Tokyo Doctor course
student who has graduated from IIT
Hyderabad Master course. Dr. Tsuboi
and his team taught M2Smart traffic
flow analysis to him and he enjoyed
the research activities. Thechallenging topic was the comparison
between Greenshields model vsGreenburg model of traffic density totraffic speed characteristics based on
Ahmedabad traffic flow raw data.

Metro developer Nippon Signal Corp.
in Ahmedabad

Ahmedabad Road condition Monitoring
trial by smartphone

IITH alumni joined Nagoya Electric Works asinternship program two weeks in August

M2SMART NEWSLETTER | VOL.5

2Collision Warning System
by Bheemarjuna Reddy Tamma (Group 1, 2 & 4)
The goal of the Collision Warning System is to avoid accidents by giving timely alerts on potentially collidingvehicles sharing the road. Nowadays everyone has smartphones which are having Cellular and Wi-Fi radios. Forachieving this goal an Android app titled SaveNav is developed and made available on Google Play. Vehicles canshare their location and relevant data in both online mode (4G/Cloud) and offline mode (Peer-to-Peer over Wi-FiDirect) to nearby vehicles in the collison domain. This app is being extended for giving suggestions about speed
and acceleration to drivers, which will help to reduce journey time as well as CO2 emissions.

Multiscale Monitoring of Urban Air Quality: An Integral
Dimension of Smart Cities Initiativeby Soumya Jana (Group 4 Co-Leader)
In recent years, rapid industrialization and increased motor vehicle usage have made several developing cities
unsustainable in terms of air quality along with other factors. While air quality is monitored in such cities, monitoring
stations are geographically separated, and resulting measurements are often insufficient to infer pollution levels inrequisite spatial details. In addition, air quality information is often not communicated to the authorities in a usable
form leading to suboptimal policies and inefficient law enforcement. Further, without such information easily
available and clear guidance from policy and law, individual and economic activities do not take environmental
impact into account, worsening the associated problem.
To this end, we envisage an integrated multiscale system that not only makes measurements at traditional
monitoring stations, but also complements those with readings from densely-deployed low-cost compact sensors.
Subsequently, the proposed system fuses the collected data with the help of suitable transport models to generatedetailed pollution maps. Finally, the system would disseminate such maps to the relevant stakeholders withappropriate levels of abstraction. We believe such a systematic architecture, when implemented at the city-scale,will guide design of efficient policy, ensure transparent law enforcement, and maintain meaningful balance between
economic development and environmental sustainability.

Figure: Setup of compression and transmission of point cloud data

M2SMART NEWSLETTER | VOL.5

3Intelligent Traffic Light Coordination for Heterogeneous Traffic
Scenarios
by Subrahmanyam Kalyanasundaram (Group 2)
Good coordination and management of city traffic has numerous benefits; this canreduce travel time, improve throughput, and also less pollution due to CO and CO2
by SubrahmanyamGroup
2 deploy different kind of systems toemissions. Most Kalyanasundaram,
major cities across theworld
ensure smooth traffic. However, in Indian cities, traffic management presents
certain unique challenges. The main reason is the heterogeneity of the traffic
situation. We model this using a heterogeneous traffic model to simulate the realtime traffic scenarios.
We use the notion of sub lanes to make the simulation more realistic. This models
the situation where more than one vehicle in parallel can be simulated in a single
lane if its width fits. We simulate the demand using the open source traffic light
simulator SUMO. Using this, we adjust the source code to generate traffic demandfor our scenarios. We employ a coordinated intelligent traffic light controller, whereall the local decisions of traffic lights are updated into common and shared variable.
These variables can be accessed by neighboring lights in order to arrive at an
optimized decision. Appropriate physical properties such as acceleration,
deceleration, maximum speed, space headway, vehicle type are used to calculate
the CO and CO2 emission in the simulation.
Coordination among traffic lights ensures smooth traffic flow and increases thethroughput. Simulation results show that coordinated intelligent traffic lightcontrollers perform better than static and actuated controllers in terms of CO andCO2 emission, waiting time and throughput.

Max Traffic Scenario [1 hour]
CO (mg)CO (mg)Cumulative Waiting Time(Seconds)

Throughput (vehicle/ time)

Static(55,35)

45,220,836

882,081,354

6,194,296

0.33

Static(55,25)

60,765,790

1,128,626,076

9,050,884

0.29

Actuated

23,175,687

532,591,728

1,977,249

0.38Intelligent

11,159,914

332,334,221

2,052,688

0.49

2M2SMART NEWSLETTER | VOL.5

4

Display proposal for VMS: Effect on driver’s psychology
by Shinya Hanaoka and Tomoya Kawasaki (Group 3)
In Ahmedabad, Variable Messaging Sign (VMS) was established in 11 locations as a part of the Intelligent Transportation
System (ITS). Until now, the right half of this display panel is showing real-time traffic information which assists drivers’route choice, while the left half of this displays advertisement at this moment. In our research, we would like to examine
what type of display would be more meaningful to reduce traffic volume in the city.
Background- Rapid growth of motor vehicle which causes serious congestion and air
pollution- Development of the ass-transit metro system (MEGA): The North-Southand East-West corridors are expected to be completed by 2019

Fig1: Example of VMS in Ahmedabad

Potential displays to reduce the usage of private cars
1) Text with color and the number of lines
Figure 2 shows the example of VMS, which varies the color and the number of massage lines. In our research, we examine theeffects of color scheme and message lines of variable message signs on drivers’ phycological intentions to give up the usage ofprivate cars.
2) Strong expressions
We can sometimes see the strong expressions such as figure 3. This kind of stimulating pictures will give a great impact on viewers
so that they can remember the content of what they have seen. We may apply this idea to the private car usage which has adverse
effect on the environment.

Fig2: Example of VMS by color and lines

Fig3: Example of strong expressions in Tobacco industry

Research Direction
Considering that the objective of this VMS display research is to encourage drivers to avoid using private cars even if it is noteasy to be realized. From the background, we can see that Ahmedabad is a city with excessive motor vehicles which cause
by Soumya Jana , Group 4 Co-Leader
severe traffic congestions. The newly constructed/planned MEGA further encourage the government to promote modal shift topublic transportation. At this time, our research is to utilize this VMS to promote the usage of local public transportation
including local bus and MEGA. We will conduct experiments such as simulating tests to check the effects of these information
PM2.5
shown onthe profile
VMS.at 2 PM
At the same time, the way that the information is shown is another subject to us. Besides, how these factors in the message
concerning to driver’s response time and comprehension to the VMS is a point of this research. We will try to find out the best
combination of these elements to show an effective VMS display.

About Ieee vizag ConferenceM2SMART NEWSLETTER | VOL.5

5Driving Cycle Development for e-Rickshaw and Energy Consumption
from Longitudinal Vehicle Dynamic Characteristics
by Digvijay S. Pawar (Group 3 Leader)This study presents driving cycle and energy consumption for a three-wheeler electric rickshaw in heterogeneous traffic conditions
typical of developing countries. Vehicle speed, acceleration-deceleration, distance, latitude-longitude were recorded per second with afrequency of 10 Hz using HD V-box mounted on an e-rickshaw. The Electric Rickshaw Driving Cycle (ERDC) is constructed by connecting
the most representative (average driving characteristics with a variance <5%, compared to raw data) micro-trips until a driving cycle
duration of 1800 s is reached. The ERDC is then compared to Indian Driving Cycle (IDC) and World Motorcycle Test Cycle (WMTC) bymean values of the driving characteristics. The average speed of the ERDC (18.33 kmph) is about 5 times lower than both the standard
driving cycles. The average acceleration (0.598 m/s2) and deceleration (-0.608 m/s2) are also found to be lower compared to the standard
driving cycles. From the ERDC, a Speed-Acceleration Frequency Distribution (SAFD) was plotted, and an idle time of 13% was observedin the selected stretch. Further, the driving cycle along with other road and vehicle characteristics such as grade, rolling resistance andinternal force are used to estimate the energy consumption (150.74 Wh/km) during travel. Maximum power required to move the vehicle
on this driving cycle was found to be 22.82 KW for a cycle distance of 8.1 km. The insights from this preliminary study can be used tounderstand and model the performance of e-rickshaw, in terms of fuel consumption and driving characteristics, compared to other fossilfuel driven automobiles.

30Speed (kmph)

25

GPS enabled Video Vbox

20151050050010001500Time (s)Figure. Instrumented test vehicle

Figure. Developed version of new Electric
Rickshaw Driving Cycle

Dr. Digvijay S. Pawar at the Eastern Asia Society for Transportation Studies (EASTS) 2019 ConferenceM2SMART NEWSLETTER | VOL.5

6

Presentation of Big-data Analysis at the Eastern Asia Society forTransportation Studies (EASTS) Conference 2019By Tetsuhiro Ishizaka (Group 2 Leader) and Anand Kakarla
This study presents the route segment performance using a big data ofBRT in Ahmedabad regarding topics 2. It’s important to calculatereliability as an index to measure the service level of public
transportation. However, these service reliability metrics can’t accountfor spatio-temporal dynamics. Further, a comprehensive measure that
can isolate inefficient location and temporal zones is necessary tostrategize preventive measures.

1DEAF Score or BTI ValueDEA

In this paper, authors analyzed the service reliability of Ahmedabad BRT
across both space and time by diving the route into stop to stop levelsegments. Subsequently, a relative performance measure for aparticular route which incorporated multiple metrics was obtained using
data envelopment analysis (DEA). Measures including average travel
time and peak deviation were used in this analysis.

0.8

BTI

0.6

0.4

0.2

0

1

2

3

4

5

6

78

9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26
Segment

Figure. DEA scores and BTI values of all the segments

Based on the scores for each segment, the underperforming
locations were identified and ranked. Finally, the DEA scores werecompared against BTI to demonstrate its non-triviality anduniqueness. When the segments were ranked according to DEA andBTI, it that DEA indicated distinct ranks. DEA indicates that segment
10 is underperforming compared to segment 14 while BTI indicatesotherwise. This is probably because of BTI not considering peakdeviation. Furthermore, DEA scores were verified with thecorresponding traffic conditions and it was observed that segment
6, 10, 11 handle larger traffic volume and resulted in bad scores.
Such DEA analysis can be used when interaction between factors
contributing to reliability is unknown.

Figure. BTI across both space andtimeTable. Comparing performance ranks according to DEA and BTIRank12345678910Top 10
segments
accordingto DEA
132
54138
18
19
12Top 10segments
accordingto BTI
31819
1452
13
15
20Rank12345678
910

Worst 10segments
accordingto DEA
61110
7
14
16
15
23
20
17

Worst 10segments
accordingto BTI
6147
111081222179

M2SMART NEWSLETTER | VOL.57Poster Session of the Eastern Asia Society for TransportationStudies (EASTS) Conference 2019 in Colombo
by Atsushi Fukuda (Group 4 Leader), Tetsuhiro Ishizaka (Group 2 Leader), Keisuke Yoshioka (Group 3), Hiroki Kikuchi andXin Guo
At the EASTS Conference 2019 in Colombo, the three poster presentations related to the M2Smart project were carried out on thesession (3) and (4) on September 10 and 11.
Two of them focused on the behavior and characteristics of Auto-rickshaw passengers based on the pre-MMA experiments and/orquestionnaire survey. Especially on the result of questionnaire survey of pre-MMA experiment, more than 60% of participants relatively
felt dissatisfaction on waiting time inside auto–rickshaw after riding until departure. These factors were drastically improved byproviding auto-rickshaws as a feeder service timely assigned by the MMA and other call service.

One of them presented the possibility of CO2 reduction by micro traffic simulation analysis under the assumption of demand shifting
to other routes based on VMS message. To carry out the simulation, estimation of OD traffic volume by using traffic data extracted
from traffic counts and drone observations were tried.

Paldi junction

Anjali circle

200000 μg/m30Figure. Micro traffic simulation

Figure. CO2 emission distribution

Photos: Poster Presentation at the EASTS Conference 2019

M2SMART NEWSLETTER | VOL.5

8Publications
Journal⚫

⚫

Tsutomu Tsuboi, “Quantative Analysis Method of Traffic Service by Traffic Congestion under Developing Country”, International
Symposium of Transport Simulation (ISTS’18) and the International Workshop on Traffic Data Collection and its Standardization(IWTDCS’ 18).
Tsutomu Tsuboi, “Quantative Traffic Congestion Analysis Approach in Ahmedabad”, Advances in Science, Technology andEngineering Systems Journal Vol. 4, No.2, July 2019.

Conference papers⚫

⚫

Debaditya Roy, Tetsuhiro Ishizaka, Krishna Mohan Chalavadi, Atsushi Fukuda, “Vehicle Trajectory Prediction at Intersections usingInteraction based Generative Adversarial Networks”, Conference: 2019 22nd Intelligent Transportation Systems Conference(ITSC).
Ryohei Hashimoto, Anand Kakarla, Tetsuhiro Ishizaka, Atsushi Fukuda, “Analyzing Route Segment Performance Based on Multiple
Indicators Using DEA: A Case Study on Ahmedabad BRT”, The 13th International Conference of Eastern Asia Society forTransportation Studies, 2019.

Conference poster presentation⚫

⚫
⚫

Tetsuhiro Ishizaka, Anand Kakarla, Atsushi Fukuda, Digvijay S. Pawar and Hiroki Kikuchi, “Analysis on Last One-Mile Connectivity
by Providing Auto-rickshaw Feeder Service and Navigation in India”, The 13th International Conference of Eastern Asia Society forTransportation Studies, 2019.
Xin Guo, Tomoya Kawasaki, Shinya Hanaoka, “Clarification of the characteristics of Auto-rickshaw passengers in Ahmedabad”,
The 13th International Conference of Eastern Asia Society for Transportation Studies”, 2019.
Keisuke Yoshioka, “A Trial of OD Traffic Volume Estimation through the Fusion of Various Types of Traffic Data and CO2 Emissions
Estimation by Microsimulation”, The 13th International Conference of Eastern Asia Society for Transportation Studies, 2019.

M2SmartNewsLetterVol. 5
Sept 2019

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