1M2SMART PROJECT

M2SmartNewsLetter

Vol.13
Oct 2021
SATREPS general meeting on 27th Sep
OVERALL SUMMARY & SPECIAL TOPICS

After intermittent hiatus, the phase of full-scale re-start has come!
Overall Summaryby Tsutomu Tsuboi (Project leader)Almost one and half year has passed
since Pandemic started March 2020.
All projects in SATREPS have to bedone under limited condition. In
January we have 6th wave for COVID19 infection spread and 3rd wave inIndia. From year 2020 to 2021, newcoronavirus variant has come.
However, we had on-line meeting
among teams, thanks for ICT
technology today. From this month
October, the situation becomes
better and better.
Let me introduce the local news of“Bangalore Mirror”(*). The new JICA
ODA project for improvement local
traffic was announced in August 8–
recently awarded the contract toNagoya Electric Works Co. Ltd. It
announced that the Japanesetechnology promises to reduce thewaiting time.
This news encouraged our SATREPS
and makes sure our contiguous
research importance.

Work on the handbook hasbegun in earnest.
by Misa Kitagawa (ResidentialCoordinator)
Progress management of thehandbook has launched, and a series
of internal meetings were actively
heldinlate
September.
Group 2 - 4 had the group meeting, inwhich the authors of the chapterswere assigned and their contents
were confirmed. The all-Japanese
member also gathered for 27th
SATREPS general meeting. Even
though it was held online, there was a
lot of lively discussion especiallyabout what measures could be taken
to ensure the smooth developmentof the handbook, which results inextension of the meeting beyond thescheduled 2hours.
Another topic was about the Cosymposium with Project inThailand(→See Page.7). It would be
effective to present a draft of thehandbook at the symposium, so wewill continue to try to facilitate thisprocess.

(*)Read more at:https://bangaloremirror.indiatimes.com/bangalore/cover-story/made-injapan/articleshow/85142932.cms?utm_sourc
e=contentofinterest&utm_medium=text&utm_campaign=cppst

Group 3 meeting on 29th Sep

Recent scenes of IITHM2SMART NEWSLETTER | VOL.13

2

GROUP REPORTS

Group 1: Multi-object tracking for traffic surveillance and controlby C. Krishna Mohan (Group 1 Leader), K Naveen Kumar, Vineel Abhinav G, Pabbathi Uday Kumar, Sai Harsha Yelleni,
Jaya Sharma
Group1 is working on formulating methods using deep learning frameworks to estimate the position and state of multiple
objects at every timestep in a traffic surveillance video. Tracking more than one object in the video raises several challenges,
including correct mapping of identity to each object throughout the video, frequent occlusions, initialization and terminationof tracks, and similar appearance of different objects.
To address these difficulties/challenges, we have used:1. Siamese neural networks to correctly map identities to correct objects.
2. Memory module to store the state of the object for a long time. It helps in re-initiating the state of an object that has
been lost due to occlusion.
3. Segmentation mask module for an accurate representation of objects. It classifies whether each pixel belongs to anobject or not.
Robust tracking of objects also helps in improving the performance of tasks such as vehicle counting and speed estimation
which require multiple objects to be tracked.

Figure 1: Proposed architecture for multi-object
tracking

Figure 1: Proposed architecture for multi-object tracking

Methodology of Proposed Approach
Figure 1 represents the architecture used to track multiple objects
1. We use YOLO object detection for the initial frame only to output bounding boxes of objects of interest. We callthem exemplar images.
2. We use a siamese neural network fӨ (ResNet-50 base) to extract features for each object (Feature Map X).
3. For each frame i after the first frame:a. For each object j, we take the region in frame i, which corresponds to the location of the larger crop of thebounding box of j in frame i-1. We call them the search regions.

M2SMART NEWSLETTER | VOL.13

3GROUP REPORTS

b. We then send these search regions into fӨ to obtain features for each object’s search region (Feature Map Y).
c. We use depthwise cross-correlation (*d) between feature maps of respective objects to obtain an activation map
representing the location of an object in frame i (Feature Map Z).
d. We use fully convolutional nets (hΦ) to obtain a mask for each object for accurate pixel-wise tracking.
e. bσ outputs a bounding box corresponding to the highest score sψ.

OutcomeGiven a video stream from a surveillance camera, our model would process each frame in the video to provide positions
and state information of multiple objects. Figure 2 represents two sequential frames of video where 7 cars (object ofinterest) are tracked along with segmentation

(a)

(b)
Figure 2: Tracking output 7 cars. (a) & (b) are two frames in video

References:[1] Wang, Qiang, Li Zhang, Luca Bertinetto, Weiming Hu, and Philip HS Torr. "Fast online object tracking and segmentation:
A unifying approach." In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp.
1328-1338.2019.
[2] Li, Bo, Junjie Yan, Wei Wu, Zheng Zhu, and Xiaolin Hu. "High performance visual tracking with siamese region proposalnetwork." In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp. 8971-8980. 2018.

M2SMART NEWSLETTER | VOL.13

4GROUP REPORTS

Group 2 – The Prediction of Road Traffic Statesby Tetsuhiro ISHIZAKA, Maunendra Sankar Desarkar
The Goal of group 2 is to generate insights to help in better city and travel planning to avoid congestion and thereby
reducing overall CO2 emission based on predicted traffic states and performance of road and public transportation
obtained from various sources.
In this newsletter, the research outcomes regarding on the prediction of road traffic states is shown. We have twoapproaches to estimate traffic states at Paldi intersection : “Traffic Volume Prediction” and “MFD Prediction”.

Traffic Volume Prediction using Observed Traffic Volume
Our project installed four traffic detectors and one CCTV camera at Paldiintersection. There was no traffic sensor along 6 km between APMC and Ashram
road before installation. Even if Ahamedabad with promoting of traffic sensor, there
are still sparse area with no observation of traffic state. In order to reveal traffic
state, the LSTM model is applied for estimation of traffic states at the target
intersection using observed traffic volume as following condition. As showing the Fig
2 and 3, The prediction model can perform with high accuracy.
Dataset: Total 139 days from 2018-11-19 to 2019-08-29
Train set :80% and Test set :20%Time Interval:15 min
Method: LSTM
Result: R2=0.957

Ashram

Shivranjani

Target zone:APMC

Paldi

17Figure 1: Study area and traffic detectors

MFD Prediction using Observed Traffic Volume

Predicted traffic volume

Production: average weighted flow

MFD is one of key performance indicator to express area traffic states. MFD expresses the interaction between area trafficstates which connecting the total number of cars on the road at any given time (the accumulation) with the rate at whichtrips reach their destinations (the output). The area is set as group of detectors in Figure 1. Paldi area (Yellow boundary) isa targeted area of estimation form other three area. Both MFD indicators were estimated with high accuracy incomparison with observed data form traffic detectors in Figure 4.

Observed traffic volume

Figure 2: Estimated traffic volume

Figure 3: Estimated & observed traffic volume

Observed
Predicted

Accumulation: average weighted density

Figure 4: Estimated MFD indicators

M2SMART NEWSLETTER | VOL.13

5GROUP REPORTS

Group 3 A – Vehicular exhaust emission under real-world driving
conditions :Role of electric-vehicles on reducing pollutant emission
by Dr. Digvijay S. Pawar (Group 3 Leader), Chandrashker. CThe road transportation sector contributes to greenhouse gas (GHG) emissions, accounting for approximately 20 percent
of carbon dioxide (CO2). The increase in vehicular growth and limited use of emission control strategies leads to asignificant contribution to road transport emission. Several studies developed an emission factor based on a laboratory
test. However, laboratory-based emission measurements do not replicate real-world driving conditions. Therefore, it iscrucial to accurately measure emissions from combustion engines under real-world conditions to provide realistic figures
for an emission inventory.
In view of the above, Group 3 has been working on the direct measurement of emissions from diesel auto-rickshaws for
India’s urban and rural traffic conditions, setting a test route of 14km stretch in Sangareddy city. In the previous Newsletter
Vol.12, we have pointed out the possibility that emission factors obtained from a laboratory test may underestimate thereal world emmissions based on the observation of our studies about relationship between emission factors and average
speeds.In this article, we will focus on the interpretation and insights for policymakers.

The summary of the results
Urban

500

Rural
500

400

CO2 (g/km)

CO2 (g/km)

400

300

300

200

200

100

10000(a)5 10 15 (b)20 25 30 35 40 45 50

Average Speed (kmph)

0
5 10 15 20 25 30 35 40 45 50

Average Speed (kmph)
Rural

2020

CO (g/km)

1515

105

100

500(c)

0(b)

Urban

25CO (g/km)

A speed-based emission model
was developed for both urban
and rural traffic conditions. Theemissions were found to behigh at both low and highspeeds and minimum atmoderate speeds. On-road
emission for urban traffic
conditions was found(a) to beslightly higher compared torural traffic. From the res ult,
the emission factors fromurban traffic were found to be
substantial than rural traffic.
The resu lt showed that thelowest driving speed
contributed to a significant
portion of total CO2 and CO
emissions over a trip, as shownin Figure 1 (a-d).

5 10 15 20 25 30 35 40 45
Average Speed (kmph)
(d)

0(d)

5 10 15 20 25 30 35 40 45

Average Speed (kmph)

Figure 1. Emission factor for different average speeds

Real-world emission and policy implementation
The insight from the study can be used to understand the emission factors from diesel auto-rickshaws which will be helpful
for policy formulation and traffic management system for better air quality.
The emission standards prescribed by the Automotive Research Association of India (ARAI) are based on laboratory testingof the vehicles, and there has been limited prominence on real-world emissions monitoring. Thus, it is crucial to verify
emission control system performance and durability, not just in laboratory certification tests of new engines but also in thereal-world driving conditions, which will be needful for emissions standard formulations. Finally, a combination of thepolicy decision, including congesting pricing, encouraging public transport services, and carpooling, would benecessary to reduce emissions drastically. Introducing e-rickshaw in urban traffic with proper charging infrastructure
could be a possible replacement for fossil fuel-based auto rickshaws to improve the city's air quality.

M2SMART NEWSLETTER | VOL.13

6

GROUP REPORTS

Group 3 B– Evaluation of major road driver response towards
intersection conflict warning system at an uncontrolled intersection
by Dr. Digvijay S. Pawar (Group 3 Leader), Chandrashker. C
Rural intersections are the critical at locations where most of the accidents take place. Crashes at these intersections
mainly occur due to poor sight distance, wrong judgment in gap acceptance by minor road vehicles, and major roadvehicles speed. An intelligent transportation system application known as Intersection Conflict Warning System (ICWS)
has been commonly used in developed countries like United States to reduce crash severity and enhance safety atintersections.
This study evaluates the performance of ICWS at an uncontrolled intersection by examining the drivers speed profiles forthree scenarios. A vehicle instrumented with GPS (Global Positioning System) device and video camera system was used
for data collection at uncontrolled intersection and at the end of the experiment, participants were asked to fill a post signquestionnaire to assess the degree to which warning sign assisted them to cross the intersection and also to assessparticipants’ perception of usefulness and acceptance of warning sign.
Figure 1 depicts the average speed profiles of all the drivers for three scenarios. The vertical line in the figure represents
the intersection where a minor road vehicle enters.
Figure 1(a) represents the average speed profile of all
drivers for scenario 1, i.e., a conflicting vehicle was notpresent on the minor road. The average speed of all
drivers was found to be 41.44 km/h.
Figure 1(b) represents drivers' average speed profiles for
scenario 2, i.e., a conflicting vehicle was present on theminor road. The average speed of all drivers was foundto be 39.63 km/h.
Figure 1(c) represents drivers' average speed profile for
scenario 3 i.e., drivers were educated about thedeployed warning system, and the conflicting vehicle
was present on the minor road. The average speed of all
drivers was found to be 34.92 km/h.
From the figure we can observe that in scenario 3 drivers
tend to reduce more speed compared to scenario 2 andscenario 1 when they approach the intersection. This
indicates that the drivers with a better understandingof the warning system could respond before they
approach the intersection and can avoid sudden
braking at the intersection. A post signage
questionnaire form results show a positive response
from drivers saying that the warning system would be
helpful in reducing collisions at uncontrolledintersections.

Further we plan to evaluate the performance of ICWS
for both major and minor road drivers approaching theintersection.
Figure 1. Average speed profiles of drivers for different scenarios (a) Scenario 1(b) Scenario 2 (c) ScenarioM2SMART NEWSLETTER | VOL.137GROUP REPORTS

Group 4 – Analysis of accessibility to facilities in Ahmedabad
by Atsushi Fukuda (Group 4 Leader), Hiroki Kikuchi
Improving the transportation system has a great impact on various activities in the city. One of the ultimate goals of Group
4 is to estimate the effect of introducing the multi-modal transportation system proposed in Ahmedabad, especially theeffect of reducing CO2 emissions. In this time, we will introduce the results of estimating accessibility based on Point ofinterest (POI) in the entire city, which is one index for evaluating the transportation system, for Ahmedabad. This is aprerequisite for estimating the reduction of CO2 emissions by introducing a multi-modal transportation system.
POI is geo-coded facilities such as office towers, residential estates, theaters, schools, restaurants, supermarkets, andhospitals that residents and visitors patronize on the daily basis and that are indispensable for cities and towns to function
properly. Compared to traditional datasets such as local land use maps, POI data are markedly advantageous in capturing
urban development.
Therefore, our group analyzed the accessibility of POIs from each mesh using a geographic information system (GIS) in
order to clarify the reachable facilities by multimodal transport. Figure 2 describes the number of reachable facilities(hospitals) in one hour for each mesh.

Figure 1: Analysis result of reachable facilities(hospitals) from each mesh in Ahmedabad

The finding shows that accessibility is the highest in the urban areas, and it is possible to access about 100% of the facilities
within the coverage area within one hour. Even within a radius of 8 km from the city center of urban areas, the accessibility
is lower than 10%. This means that there is a significant disparity in accessibility even within the urban areas.
We will estimate the changes in accessibility and analyze these disparities when the entire portion of the metro in theAhmedabad line opens. Also, we will analyze the impact of long-term policy implementation using the land-use andtransportation model and will report those results in our following newsletter.

M2SMART NEWSLETTER | VOL.13

8OTHERS

Co-Symposium with "Smart Transport Strategy for Realizing Thailand
4.0” is planned in early 2022
by Atsushi Fukuda (Group 4 Leader)
The group headed by Prof. Yoshitsugu Hayashi of Chubu University, Japan and Prof. Thanaruk Thiramunkon, Thammasat
University, Thailand is conducting "Smart Transport Strategy for Thailand 4.0" which is another smart transport related
SATREPS project, that mainly focuses on Bangkok, Thailand.
Main objectives of this project are : 1) to achieve leapfrog growth based on the "Smart Transport Strategy" and 2) tocontribute to “Thailand 4.0” and SDGs through the "Sukhumvit Model". As you can see from this, this project and ourM2SMART project have much in common, hence, JST has advised us to hold a joint symposium.
So both projets are planning to hold the joint seminar to exchange information, insights, views among each other, withthe aim of networking and creating synergies between researchers.
The tentative plan of the programme at the moment will be as following:
1. An introduction to both projects
2. Evaluation and social implementation
3. Data collection and analysis
4. Challenges and future perspectives for both projects.
Although it will be held online, we hope that this joint symposium will be fruitful for both parties.

IITH is gradually normalizing
by Misa Kitagawa (Residential Coordinator)
After long standbytime, I’ve started to commute to IITH daily this week.
The situation seems to be better compared to my last visit in the end of the August, assome Phd students are coming back, however lectures for undergraduates students isbeing held online only and some faculty members are still working from home. The
cafeteria remains closed and I imagined that students would be smiling again there.

My Timing & Location is below,.
Please feel free to have a casual talk!
•••

9:00-12:00am
Monday to FridayC-113/A

M2SMART NEWSLETTER | VOL.13

9OTHERS

Publications
Journals⚫

Chandrashekar, C., Agrawal, P., Chatterjee, P., and Pawar, D. S. (2021). Development of E-rickshaw Driving Cycle (ERDC)
Based on Micro-trip Segments Using Random Selection and K-means Clustering Techniques. IATSS Research. (In press)(DOI:https://doi.org/10.1016/j.iatssr.2021.07.001).

⚫

Chandrashekar, C., Chatterjee, P., and Pawar, D. S. (2022). Development of real-world CO2 and CO emission factors
from diesel-auto rickshaws in Indian Urban and Rural Driving Conditions. Proceedings (CD-ROM) of 101th
Transportation Research Board Annual Meeting, Washington D. C.

⚫

Rachakonda, Y., and Pawar, D. S. (2022). A Review on Evaluation of Intersection Conflict Warning System at RuralIntersections. Proceedings (CD-ROM) of 101th Transportation Research Board Annual Meeting, Washington D. C.

Conference presentations⚫

Vehicular Technology Conference (VTC) Fall 2021 by IEEE Vehicular Technology (Virtual presentation date : 30th Sep2021), by Dr. Antony Franklin, “Traffic-Aware Sensing-Based Semi-Persistent Scheduling for High Efficacy of C-V2X
Networks”“This paper explicates the impact of pKeep(probability of keeping a radio resource) and RC(Resource Counter) on theoverall performance of C-V2X system. And a new algorithm called Traffic-Aware SPS (TA-SPS) is then proposed, which
optimally configures these parameters according to the current density of road traffic by estimating it from periodic
messages broadcasted by the vehicles. By reducing the number of resource collisions, TA-SPS increases transmission
reliability of the C-V2X system and outperforms the traditional SPS(Semi-Persistent Scheduling) in mixed road trafficscenarios.”

Upcoming Conference⚫

The 24th Brazilian Symposium on Formal Methods (SBMF 2021)(Virtual event), 6-10th December.

⚫

The Transportation Research Board (TRB) 101st Annual Meeting, 9-13th January.

M2SmartNewsLetterVol. 13Oct 2021

M2 Smart Website: http://m2smart.org/en/
M2 Smart Facebook: https://www.facebook.com/M2SmartUnderSATREPS/