M2SMART PROJECT

1M2SmartNewsLetter

Vol.6Dec 2019

JCC MEETING AT IITH (OCT 2019)

JCC AT IITH

4th Joint Coordination Committee (JCC)by Haruka Katarao, M2Smart Resident Coordinator

Warm welcome by Prof. Murty andcontinuous support from Prof.
Desai!
The 4th Joint Coordination Committee(JCC) was conducted at Indian Instituteof Technology (IITH) on 17 October
2019. Welcomed by Prof. Budaraju
Srinivas Murty, Director of IITH, theM2Smart researchers from IITH, Nihon
University and Nagoya Electric Works
exchanged their research updates. We
also had a participation from Japan
International Cooperation Agency(JICA).

Prior to the JCC meeting, theresearchers gathered and discussed
the detailed group activities and data
sharing between Ahmedabad City andHyderabad City and also among theresearchers in order to enhance thesmooth group collaborations.

Pre-meeting at IITH

JICA Presentation at the meetingM2SMART NEWSLETTER | VOL.6

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4th JCC pre-meeting and the prospect for the coming years
by Tsutomu Tsuboi, M2Smart Project Leader
The 4th JCC meeting has been held in the mid-term of M2Smart project and the transition of IITH since former IITH
The 4th JCC meeting has been held in the mid-term of M2Smart project and the transition of IITH since former IITH
director, Prof. U.B.Desai has become a Professor Emeritus. Thanks to his strong leadership, he will continue to support
the project as New IITH Project Manager from now on. Prof. Murty has been selected as the New Project Director fromthis JCC. We checked the current issues and confirmed solutions which required several improvements of the project
management and the test bed.
We finished almost of the installation of the IITH test bed equipment and will start to collect test data from each test
bed and real field. The project will be continued until June 2022 and produce “Hand-Book” towards low carbon urban
transportation, which is also driven by “100 Smart Cites” policy by the Indian Government. In this program, all ourresearch organizations make the state of art level research and development such as AI traffic video capturing
technology, real traffic micro simulation model based on actual real traffic field big data, sensing remote controlled
traffic monitoring camera, smart phone based multimodal applicatio n for analysis residential movement in the city,and prediction of traffic condition among the all tools and analysis. The next JCC will be planned by in May 2020 inJapan.

Pre-meetinat IITH

On November 8th, Director Murty and Professors of IITH team have visited Nagoya Electroic Works after JCC meeting.
Drector Murty and his team had a meeting with the President of Nagoya Electric Works Mr. Takaaki Hattori and joined
the factory tour. Nagoya Electric Works showed the current traffic information products such as VMS, Mobile VMS andParking System. During Nagoya meeting, we exchanged not only M2Smart project but also the potential internshipbetween IITH and Nagoya Electroic Works for mid and long-term Japan – India collaboration. We concluded to keep ourrelationship and more long-term collaboration between Industry field and Academia by using M2Smart project
activities.

Director Murty’s visit to Japan
Director Murty’s Japan visit

M2SMART NEWSLETTER | VOL.6

3Group meeting at IITH and data sharing
by Tetsuhiro Ishizaka, Group 2 Leader

On October 16, the day before the JCC meeting, research members from Japan and India had a meeting at the Indian Instituteof Technology, Hyderabad. At first, Dr. Tsuboi, Project Leader, briefly confirmed the research theme, research plan, thefuture schedule and the plan of operation. Then, the leaders of each research group introduced about the progress of
research and exchanged opinions among all members. And then the researchers were divided into the 4 groups to havefocused discussions and discuss next steps.
•Group 1 [Sensing]:Multi type vehicles recognition recognition by 3D sensor, LIDAR in IITH test bed and by AI image
processing on surveillance video camera in Ahamedabad.
•Group 2 [Big Data Analysis]: Started traffic flow analysis based on actual traffic data and multimodal traffic sensing analysis(BRT, Metro connectivity).
•Group 3 [Multimodal Application]: Construction of traffic micro simulator, development of driving cycle for E-Rickshaws
and implementation of Multimodal application (MMA) trials in Ahmedabad.
•Group 4 [CO2 Reduction]: Environment field test for PM2.5 in AMD; Situation of PM2.5 was clarified as a crucial observationand development of micro traffic simulation for Vasna and Paldi roads to verify the impact of CO2 emission reduction.
Lastly, The data sharing plan was announced for M2smart project researchers. The shared folder was categorized into fivedrives.
Drive1: Traffic data in Ahmedabad
Drive2: Traffic data in IITH
Drive3: Traffic movies of CCTV in Ahmedabad
Drive4: MMA data
Drive5: Shared documents for all member and each group
The drive 1-4 are limited to be accessed by related group member who is permitted by group readers.

Group 2

Group 1Group 3 & 4

M2SMART NEWSLETTER | VOL.6

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IEEE ITSC-2019 Conference – Auckland, New Zealandby C Krishina Mohan (Group 1 Leader) and Debaditya RoyIEEE ITSC Conference in Oct 2019

IEEE Intelligent Transportation Systems Conference (ITSC) 2019 was held in Auckland, New Zealand from October 27-30, 2019. Iattended the conference with Prof. C. Krishna Mohan from IIT Hyderabad to present the paper titled “Vehicle Trajectory Prediction
at Intersections using Interaction based Generative Adversarial Networks”. The paper was part of the Regular Session on Traveland Traffic Behavior (I) on October 29. We received a lot of queries on our paper regarding the use of Generative Adversarial
Networks (GAN) for traffic modeling in lane-less traffic. Especially, there was one interesting question by Dr. Pavlo Tkachenko ofJohannes Kepler University regarding how we modified the loss function of GAN to provide multiple output future trajectories forthe same input trajectory. In the paper, we have used Variety Loss that evaluates the likelihood of multiple outputs for the same
input generated by sampling the random noise in the generator of the GAN. These questions will help us in planning our future
research better by understanding the association of vehicle type with vehicle behavior. Finding such associations can help inunderstanding traffic conditions that cause excess release of CO2 in line with the mission of the M2Smart project.
The keynote session on October 28 was really inspiring where Prof. Fei-Yue Wang, from the Chinese Academy of Sciences, talked
about the future of Intelligent Transportation. The title of the talk was “AI and Grand Challenges for ITS: Parallel Transportation
Systems and Transportation 5.0” that focused on knowledge automation for building better transportation systems that mimic
human intuition and enhance safety. His talk also focused on using the small observational driving data in the real world to create
realistic driving simulators in the virtual world and gaining deep intelligence from the same to enhance the driving capabilities ofautonomous cars. He terms this process as “Parallel Driving in Cyber-Physical Social Systems (CPSS)” that features coevolving
physical and artificial drivers [1].
The next keynote address on October 29 was by Prof. Miguel Ángel Sotelo from the University of Alcala, titled “AdvancedPrediction of Intentions - an Essential Feature for Efficient and Safer Autonomous Driving”. From pose estimation of pedestrians thathelps in uniquely identifying pedestrian intention [2] to understanding the braking behavior of vehicles for better lane navigation[3], Prof. Sotelo showed alert systems for drivers that use the information obtained from these methods. Overall it was a talk thatshowed the transition of ideas from theory to practice.
[1] Wang, Fei-Yue, et al. "From intelligent vehicles to smart societies: A parallel driving approach." IEEE Transactions on
Computational Social Systems 5.3 (2018): 594-604.
[2] Peng, Liqun, et al. "Rough Set Based Method for Vehicle Collision Risk Assessment Through Inferring Driver's Braking Actions
in Near-Crash Situations." IEEE Intelligent Transportation Systems Magazine 11.2 (2019): 54-69.
[3] Mínguez, Raúl Quintero, et al. "Pedestrian path, pose, and intention prediction through gaussian process dynamical models
and pedestrian activity recognition." IEEE Transactions on Intelligent Transportation Systems 20.5 (2018): 1803-1814.

M2SMART NEWSLETTER | VOL.6

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Research on ‘Comprehensive Air Quality Systems For Rapidly
Growing Cities In Developing Countries’ presented in IEEE
Global Humanitarian Technology Conference
by Soumya Jana (Group 4 Co-Leader) and Anand Kakarla
We have attended IEEE Global Humanitarian Technology Conference (GHTC) 2019 held at Seattle from 17th Oct to 20th Oct topresent our research on ‘Comprehensive Air Quality Systems For Rapidly Growing Cities In Developing Countries’ and also to gain
some insight on sustainable developing activities organized by IEEE. We started my conference tour with a workshop on IOT
architectures. For our air quality systems, sensor networking is one of the important aspects to obtain air quality samples remotely.
This session helps me understand many of the implementation schemes of networking such as Lora, how to setup an open data basefor data logging. I also personally interacted with him on how to setup such free open source cloud data base and what all resources areneeded. We attended some of the talks on machine learning papers in these three days such as fire detection for disaster scenarios
and realized their diverse applications and learnt a few techniques.
Finally, Anand presented current work about an architecture which helps the governments monitor the air quality efficiently. He got afew suggestions on how to improve the networking aspect to scale up sensor deployment around the city were given. Few commentson the effectiveness of measuring CO2 from ambient measurements was also discussed in this session.
An overview of our M2smart project in was discussed with some delegates of the conference. The certain members of the session
appreciated the basic idea and they also shared about similar projects being run in their respective countries.

GHTC conference networking session

Dr. Jana in one of the sessions

GHTC conference during keynote

Anand presenting at GHTC conferenceM2SMART NEWSLETTER | VOL.6

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Micro Traffic Simulation for CO2 Emission Estimation
by Keisuke Yoshioka (Group 3), Atsushi Fukuda (Group 4 Leader), Tetsuhiro Ishizaka (Group 2 Leader) and Digvijay S.Pawar (Group 3)To understand the effect of the CO2 emission reduction by multimodal shift transportation, we are carrying out the sensitivity analysis by micro trafficsimulation.
A study area is around Ashram Street (about 5.0 km) in the southwestern part of Ahmedabad city. In this area, traffic congestion occurs because ofcommuting traffic toward the central area during peak hours. On the other hands, a south - north metro line is scheduled for opening in 2021 and weare going to provide the dynamic information of predicted required time by VMS (Variable Massaging Sign) which was installed near the APMC metrostation.
In this part, we will find out the relationship between the reduction of traffic volume
by modal shift and the effect to the amount of CO2 emission. Micro simulation
analysis consists of 4 steps as below;
STEP1: Collecting the traffic data
Collecting the traffic data is necessary for performing micro traffic simulation.
Traffic data is measured by various methods which is tried in this project Group 1.
STEP2: Estimation of OD matrix
Create the OD matrix which is the input data of micro simulation by applying theextended entropy maximization method, which considers the difference of theaccuracy of data acquired by various methods.

Figure. Micro traffic simulation

Extended Entropy Maximization Method
1. Set the Priori matrix with 3 pattern volume (heavy:500, middle:100, light:10)
2. Introduce two parameters; connecting the priori OD table and observation
link volume, and representing the observation reliability
3. Estimate these parameters which minimize the cost function
4. Calculate each OD pattern volume with two parameters
5. Reset the priori matrix with estimated OD volume, then estimate parametersagain in the same way (Repeat 100 times)
STEP3: Micro simulation
Reproduce the traffic conditions by micro traffic simulation software VISSIM andestimate CO2 emissions. The reproducibility of the simulation is verified by saturation
flow rate and vehicle trajectory on a time–space diagram based on floating car data.

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101010101010100
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1010101010101010101010103110100101010101010101010101041
10101005001010101010101010104210101050001010101010101010105110101010100100
101010101010105210101010101000101010101010106110010101010101005001010101010192131414251526162717281829919210 1842
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2712117266718122302754862100
1010101010105000101010101071101010101010101010010010101072100101010101010101010001010108110101010101010101010100100
10Priori Matrix

31413442384
57
01210022 1500
10131013162312165729397914175642486
5810628010102114858
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1612530713582
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0913681148557
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0Estimated Matrix

Figure. Estimation of OD matrix

STEP4: Scenario analysis
Analyze the relation between the extent of the traffic mode shift and CO2 emission reduction under several scenarios due to the opening of the metroand information provision.
Currently, we are conducting the trial calculation by using some tentative traffic data.

SimDistance [km]54

Paldi junction

3Anjali circle

21Saturationflow rate [pcu/1h]

Observed(floating car data)

68000

Paldi junction
60005426

5352

4000
2000

Anjali circle

0Sim

Observed

00300

600
Time [sec]

900

1200

Figure. Validation Results of Time space diagram

200000 μg/m30Figure. Validation Results ofSaturation flowrate in PCU
at Paldi Junction

Figure. CO2 emission distribution

M2SMART NEWSLETTER | VOL.6

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Traffic sensing from ITS test bed-IIT Hyderabadby Dr.Digvijay S. Pawar (Group3 Leader), Chandrashekar C and Pabbathi Uday Kumar
Introduction
Currently we are in process of analyzing and validating the data collected through various ITS systems deployed under M2SmartProject. In this meeting, we reviewed the performance of The Infra-Red Traffic Logger (TIRTL) deployed on NH65 for counting andclassifying the traffic data. The system records the traffic data in one direction i.e., from Mumbai to Hyderabad. The TIRTL is a traffic
surveillance system that is non-intrusive and capable of highly advanced functionality. The general operation mechanism of TIRTL isshown in the below Figure.
General operation
TIRTL uses Electronic devices (Receiver and Transmitter) mounted on either side of the road. It uses a two-infrared beam above theroad surface to detect and record the vehicle. It also passes signal to conflict warning system located on minor road near IITH exit gate
for safe merging of the vehicle into major roads.

TIRTL
TIRTL installation near IITH exit gate

Passes signal
Conflict Warning system

Data analysis

TIRTL installation Near IITH gate controller room

Traffic data analysis from sensor

M2SMART NEWSLETTER | VOL.68Weekly M2Smart Resaech Assistant (RA) Meeting Gallery
20 IITH RAs are working on this project!

M2SmartNewsLetterVol. 6Dec 2019

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