M2SMART PROJECT

1M2Smart
NewsLetter

Vol.12June 2021

OVERALL SUMMARY & SPECIAL TOPICS

Overall Summary
Since there is big impact by COVID19 from March 2020. We had virtual
on-line meeting among teams. It isquite difficult for our research
especially for field test in Hyderabad
and Ahmedabad. Our trafficmonitoring system is able to collectbig data through internet, therefore
we are able to continue for traffic
flow rsearch indivisually. This is oneof our M2Smart project advantage.
At the same time, we are able tocommunicatewithregional
government organization such asAhmedabad Municipal Corporation(AMC), Smart City Ahmedabad
Development Ltd. (SCADL) for ourprogram. And we communicate with
Indian Embassy of Tokyo three times
for reporting our program status andrequest strong support from thegovernment. And Inidan embassy
supported to communicate with AMC,SCAL and CEPT University for fieldtest support in this year. This kind ofsevere situation COVID-19 makes

limited research activies but makes
establsishing strong collaboration
with Indian coverement.

Discussion with Indian Embassy Minister

Special Topics:
Indian Embassy Minister's Visitto Nihon University!
On 21 March 2021, Ms. MonaKhandhar of Indian Embassy ofTokyo visited Nihon University. Ms.
Kahdhar is the Minister of Economic& Commerce and she has lot ofexperience
forSmart
Citydevelopment in Ahmedabad Gujarat
state. At her visiting at Nihon
University, we exchanged M2Msrat
research acitivies among project
members (refer top piture). She
stated to provide strong support. The
following picture shows discussion atNihon Univweersity and visiting
facility of Nihon University.

Visiting facility of Nihon University

M2SMART NEWSLETTER | VOL.122GROUP REPORTS

Group 1: Image dehazing to improve scene sensing performanceby C. Krishna Mohan (Group 1 Leader), K Naveen Kumar, Pabbathi Uday Kumar, Sai Harsha Yelleni and Jaya Sharma

Group1 worked on restoring clear visible images from the hazy outdoor scenarios by reducing or removing interference
due to haze using the deep learning methods.
Due to smoke, dust, fumes, mist, and other floating particles in the atmosphere, images taken in such an atmosphere areoften subject to color distortion, blurring, low contrast, and further visible quality degradation. The hazy image input will
make it difficult to solve the visual tasks such as vehicle detection, vehicle count, and tracking.
Image dehazing is a pre-processing step for all tasks involving visual appearance as a piece of critical information foranalysis. Using image dehazing to the existing deep learning models during adverse weather conditions helps to improve
the quality of results and the model performance.
We have used publicly available datasets on various road scenarios in foggy and haze conditions to train a deep learningmodel to recover haze and unclear images. When given as the input to the vehicle detection or vehicle counting module
performs better with more reliable and accurate results.
Open Source Dehaze Image Datasets Used:

Datasets

Images Description

NYUv2

1399

Indoor and outdoor synthetic haze dataset

FRIDA2330

Images of 66 diverse foggy road scenes

Dense_Haze 66

Real haze and haze free indoor and outdoor images

O-HazeReal hazy and haze free outdoor scene images

90

We have used publicly available datasets on various road scenarios in foggy and haze conditions to train a deep learningmodel to recover haze and unclear images. When given as the input to the vehicle detection or vehicle counting module
performs better with more reliable and accurate results.

Methodology of Proposed Approach
1. We built a novel fusion architecture to restore haze-free images
2. We use local residual learning, which allows the less critical information such as thin haze region or low-frequency
to be bypassed through multiple local residual connections and letting the main network architecture focus on morepractical information
3. Feature attention module with pixel-wise and channel-wise attention is used to focus on thick haze pixels and morecritical channel information
4. We adopt feature fusion techniques to retain shallow layer information, pass into deep layers, and restore haze-freeimages.

M2SMART NEWSLETTER | VOL.12

3

GROUP REPORTS

OutcomeGiven a hazy video captured by autonomous vehicle or surveillance cameras at various outdoor locations, our model would
process each frame in the video and provide real-time high quality dehaze images for further analysis to any road scene
analysis task, providing better assistance to capture the scene information.

Original haze image

Image after dehazing→→

References:
Qin, Xu, Zhilin Wang, Yuanchao Bai, Xiaodong Xie, and Huizhu Jia. "Ffa-net: Feature fusion attention network for single
image dehazing," in proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, no. 07, pp. 11908-11915. 2020.
Singh, Ayush, Ajay Bhave, and Dilip K. Prasad. "Single image dehazing for a variety of haze scenarios using back projectedpyramid network," in European Conference on Computer Vision, pp. 166-181. Springer, Cham, 2020.
Datasets : https://github.com/youngguncho/awesome-dehazing

M2SMART NEWSLETTER | VOL.124GROUP REPORTS

Group 3 – A : Vehicular exhaust emission under real-world driving
conditions / Role of electric-vehicles on reducing pollutant emission
by Digvijay S. Pawar (Group 3 Leader) and Chandrashker. CThe current study mainly focuses on the direct measurement of emissions from diesel auto-rickshaws for India's urban
and rural traffic conditions. A test route of 14 km stretch comprised of urban and rural traffic in Sangareddy city wasselected for the study.
The instantaneous emissions and speeds were measured by integrating the auto gas analyzer and Hd-VBox device. Twotest vehicles of different emission standards were chosen for the 10 trips data collection. The relationship between
emission factors and average speeds were developed for urban and rural traffic in this study. The speed- emission factor
models, were fitted with quadratic polynomial function with a good R2 value. From the result, the emission factors fromurban traffic were found to be substantial than rural traffic. The result showed that the lowest driving speed contributed toa significant portion of total CO2 and CO emissions over a trip, as shown in Figure 1.
The emission factors of CO obtained from our study were found to be approximately 10 times higher than prescribed
Bharat stage -Ⅲ (BS-Ⅲ) and Bharat stage - Ⅳ (BS-Ⅳ) emission standards values. The lowest emission factors wereobserved at a speed range of 25-30 kmph for rural and urban traffic conditions. The emission factors obtained from ourmodel does not meet Bharat stage Ⅲ and Ⅳ emission values at any speed band.
Figure 1. Emission factors v/s speed for diesel – auto-rickshaws in urban and rural traffic conditions

9080706050403020100Rural

1600

y = 0.1251x2 - 7.1234x + 105.33

1400

R² = 0.8921

1200

CO2 (g/km)

CO (g/km)

Urban

y = 1.3342x2 - 90.247x + 1633.6
R² = 0.9125

1000
800
600
400
200
005101520

25303540

45

05

Average speed(kmph)

Average speed (kmph)

Figure 2. Comparison of emission factor of CO obtained from ourstudy with the emission standards prescribed by ARAI

CO (g/km)

As shown in Figure 2, the standard emissions factors varied
significantly compared to our study. This may be because,standard emission factors were obtained from a laboratory test
using the standard Indian driving cycle (IDC) by the Automotive
Research Association of India (ARAI). The variation in emission
factors may be due to vehicle age, vehicle type underconsideration, vehicle kilometer traveled, vehicle maintenance,
etc. Therefore, emission factors obtained from a laboratory
test for emission inventories may underestimate the realworld emissions. Thus, the acquired real-world emission factors
from the study for a particular region/city may help policymakers
implement traffic management systems and urban policy
interventions to improve air quality.

10 15 20 25 30 35 40 45 50

14121086
4
20
BS-Ⅰ

BS-Ⅱ

BS-Ⅲ BS-Ⅳ

Emission standards

Our
Ourstudy study(BS-Ⅲ) (BS-Ⅳ)

M2SMART NEWSLETTER | VOL.125

GROUP REPORTS

References:
1.Climate and clean air coalition, ‘Three wheels united ’2018 https://www.ccacoalition.org/en/news-media
2. Olivier, J.G.J., & Peters, J. A. H. W. (2020). Trends in Global CO2 and Total Greenhouse Gas Emissions: Report 2019. PBL
Netherlands Environmental Assessment Agency.
3. Singh, N., Mishra, T., & Banerjee, R. (2019). Greenhouse gas emissions in India’s road transport sector. In Climate change
signals and response (pp. 197-209). Springer, Singapore.

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), Rachakonda. Y

Intersection Conflict Warning System (ICWS) is one of the applications of ITS technology that acts as the best solution toreduce crashes at uncontrolled intersections. It consists of activated LED warning signs and sensors that detect the vehicle
approaching and send warning sign to the adjacent approaching vehicles.
This study evaluates the performance of ICWS installed at typical four-legged uncontrolled intersection by examining theresponses of major road drivers towards the installed warning system. The ICWS performance is evaluated for threedifferent scenarios.
Figure 1: Experiment design with different scenarios

In scenario 1, no vehicle was present on the minor road therefore warning sign was not activated on the major road (seefigure 1(a)).
In scenario 2, crossing vehicle was present on the minor road and entering traffic warning signal was given to the majorroad approach (see figure 1(b)).
In scenario 3, participants were educated about the deployed conflict warning system and were asked to drive theintersection with the entering traffic warning signal on.

M2SMART NEWSLETTER | VOL.12
GROUP REPORTS

A total of 18 drivers data were collected using a high end GPS data logger. Speed and acceleration are the driving
performances measures considered for evaluation. ANOVA test was employed to study the statistical differences in driving
performance against three scenarios. Results indicate that a statistical significance difference among three scenarios
for mean speed and mean acceleration was observed.

Figure 2: Boxplots of mean speed and mean acceleration for three ICWS scenarios

Figure 2 represents the boxplot of mean speed and mean acceleration of drivers for three ICWS scenarios. It was observedthat the average mean speed of drivers for scenario 3 was lowered by 6.52 km/h compared with scenario 1 and the averagemean acceleration of drivers for scenario 3 was reduced by 0.33 m/s2 than scenario 1.

This implies that the drivers after being educated by the warning system result in lower mean speed because drivers
were alert about the approaching vehicle and responded before they approached the intersection. Further study will
be carried out by considering more drivers for evaluating ICWS performance, and the reaction time of drivers will beconsidered as another driving performance measure.

6

M2SMART NEWSLETTER | VOL.12

7OTHERS

Leaving of Haruka Katarao, M2smart Residential Coordinator
Hello everyone, I’m Misa, the new face in M2smart
by Misa Kitagawa (M2Smart Resident Coordinator)
A very warm greeting to all of you. I am Misa Kitagawa, successor of Haruka Katarao, who had
been M2smart Residential Coorinator. As you may already know, she has left the project inApril, and I am taking over her job, such as RA agreement, payment request, and issue of thenewsletter. Since my arrival to Hyderabad, originally scheduled for May, has been delayed due
to Covid-19 pandemic, I am waiting the day that I will be able to get there and fully carry out mymission.
For a self introduction, I had been worked as a project manager integrating the field of asset
management and data science as a new business consultant. I am very happy to be a part ofthis project, as I have been deeply interested in both of India as a promising country, and SDGs.
I am very excited about this research that will lead to smart cities in the country.
I'm looking forward to seeing you all in face-to-face soon!

Farewell and hope our paths cross again in the near future!
by Haruka Katarao (ex-M2Smart Resident Coordinator)

It has been a great pleasure working with M2Smart
Project team for the last 3 years. Due to thecontinuous Covid-19 pandemic, it was pity that I
could not meet you all in person but it was nicemeeting with Director Murty, Prof. Arjun and few
RAs at IITH in April 2021. I also appreciate all thethoughtful messages from each of you. The
intecutual situmilations and warm friendships are mylifetime treasure.
This Covid-19 pandemic made huge impact on eachone of us and it made us re-think about our common
future. In that sense, I truly wish the success of theM2Smart Project, which contributes to the low
carbon society. Now I have started to work on
another area of environmental project, marine plastic
waste issues, together with ASEAN countries.
All the best on the M2Smart Project and over to you
Misa!

Last IITH M2Smart Lab visit, April 2021

M2SMART NEWSLETTER | VOL.12OTHERS

Message from Graduating RA
Traffic Aware V2X Solutions for Intelligent Transport Systems
by Anshika Chourasia
I would like to thank Dr. Bheemarjuna Reddy Tamma and Dr. Antony Franklin for giving me this opportunity and theirguidance. I would also like to thank “M2Smart: Smart Cities for Emerging Countries based on Sensing, Network and Big
Data Analysis of Multimodal Regional Transport System”, JST/JICA SATREPS, Japan” project for their support.
I’ve explored the Wi-Fi and Cellular-based V2X technology for improving road safety and traffic efficiency. Intelligent
Transport System (ITS) performance is improved by proposing two traffic-aware solutions, first exploiting Wi-Fi
technology for traffic monitoring and then introducing a traffic-aware semi-persistent resource selection scheme for CV2X networks. To ease traffic congestion and keep a smooth traffic flow, the Wi-Fi-based road traffic monitoring systemwith channel hopping functionality is studied, which helps estimate traffic stats (travel time, average speed, and trafficflow) of road segments. Moreover, to increase the safety performance of decentralised C-V2X Mode 4 networks a TrafficAware Semi-Persistent Scheduling (SPS), an enhancement to the traditional SPS scheme is introduced, which enhances
the transmission reliability of C-V2X Mode 4 networks.

Road traffic detection using Social Media & M2Smart Bot
by Priyambada Ambastha
I express my deepest gratitude to Dr. Maunendra Sankar Desarkar for giving me the opportunity to be a part of theM2Smart family as a Research Assistant. My work at M2Smart revolved around “Road traffic detection using SocialMedia” and “M2Smart Bot: A transport domain chatbot for commuters”. This work would never have been successful
without his excellent guidance, constant support, encouragement and sincere advice.
I am thankful to “M2Smart: Smart Cities for Emerging Countries based on Sensing, Network and Big Data Analysis ofMultimodal Regional Transport System”, supported by JST/JICA SATREPS, Japan project for supporting my work in all
possible ways throughout my journey.
My sincere thanks to all the faculty members for their invaluable guidance and insightful comments. Lastly, I would like tothank all my fellow team members for all the fun moments we had in the lab and virtually.

Travel time and traffic incident duration prediction
by Prashansa Agrawal
I would like to express my deepest gratitude to Dr. Antony Franklin and Dr. Bheemarjun Reddy Tamma for selecting me asa part of this project. The completion of my dissertation would not have been possible without their consistent support andguidance. I would also like to show my deepest appreciation to Dr. Digvijay Pawar for his constant mentoring and support. I
am also extremely greatful to Dr. Srijith PK for guiding me with invaluable suggestions and warm encouragement. My
sincere thanks to JST/JICA SATREPS, Japan for their support. I had great pleasure working with IITH M2Smart team. A
special mention to Chandrashekhar C for the collaborative work.
I have worked extensively on traffic related datasets with respect to travel time and traffic incident duration prediction. Thework included data analysis using various Machine Learning models in combination with Natural Language Processing. I was
also involved in the collaborative work with respect to E-Rikshaw Driving Cycle Development.

8M2SMART NEWSLETTER | VOL.12

9PublicationsJournals⚫

Tsutomu Tsuboi, Tomoaki Mizutani, “Traffic Congestion “Gap” Analysis in India”, 6th International Conference onVehicle Technology and Intelligent Transport Systems April 2021 (VEHITS)

Conference presentations⚫

Prashansa Agrawal, Jahnavi Yarlagadda, Antony Franklin, Dr. Digvijay Pawar, “Bus Travel Time Prediction using Extreme
Gradient Boosting” ASCE ICTD 2021 (Virtual Event). (Virtual presentation date : 8th June 2021)

M2SmartNewsLetterVol. 12June 2021

M2 Smart Website: http://m2smart.org/en/
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