M2SMART PROJECT

1M2SmartNewsLetter

Vol.10Jan 2021

M2SMART PROJECT ONLINE MEETING

ODA White Paper was handed over to theIndian Embassy in Tokyo
by Tsuboi Tsutomu (Project Leader)

Overview M2Smart activities of2020 and next challenge in 2021
We were in the severe condition
under COVID-19 in 2020. The
COVID-19 has expanded its influence
just after IITH meeting andWorkshop in Ahmedabad in January
2020. And M2Smart activities has tobe conducted remotely almost
whole year. However, we did notstop our activites with all project
team membses’ strong efforts andsuccesfuly conducted 6 times on-line
meetings in 2020 for sharing eachrserach activities. In 2021, this year
will be essential final year ofM2Smart and we will do our best.

ODA White Paper has publishedfrom Ministry of ForeginAffarsof Japan
Dr. Tsuboi had a meeting with Ms.
Mona Khandhar, Minister Economic& Commerce, Indian Embassy inTokyo and handed over the Official
Development Assistance (ODA)
White Paper Book in which ourM2SmartProjecthasbeen
highlighted as one of theoutstanding overseas activities in2019. The Mister supported strongly
our M2Smart Project and she
showed high expectation as a good
example of the coming years of“Low Carbon” Smart City.

Dr.Tsuboi handed over ODA White
Paper to Ms. Khandhar, Ministerof Indian Embassy in Tokyo on
29th of December 2020

M2SMART NEWSLETTER | VOL.10

2

Preparation for the Handbook of Multimodal
Transport for Smart City-Application ofSensing, Networking and Big Data Analysis
by Atsushi Fukuda (Co-project manager /Group 4 leader) and Hiroki Kikuchi(Group 4)
This handbook aims to introduce advanced technologies to realizing smart
cities by multimodal transport by summarizing the results of M2 SMART. It
consists of two parts of “Role of Multimodal Transport for Smart City” (Part I)
and “Advanced Technologies to Realize Multimodal Transport” (Part II). In
Part I, the outline of multimodal transport for a smart city will be introduced.
In Part II, the developed individual methodologies for the smart cities bymultimodal transport will be presented with some case studies. Currently,
each research group is writing a manuscript, and Group 4 will proceed withediting work as soon as the manuscripts are collected. Please look forward toour handbook publishing!

Image of Handbook (Part II)"Scalable Coordinated Intelligent Traffic Light Controller for
Heterogeneous Traffic Scenarios Using UPPAAL STRATEGO",COMSNETS 2021
by Subrahmanyam Kalyanasundaram (Group 3), M.V. Panduranga Rao (Group 3 ) and Thamilselvam B
We propose a new approach for coordinating traffic
flows in large cities that helps in reducing the travel
time and carbon emissions from vehicles. We use theUPPAAL STRATEGO tool chain that leverages
statistical model checking and machine learning forsynthesizing optimal traffic coordination strategies.
Our approach employs a hierarchical view of the citywith two levels — individual traffic intersections andarea controllers. While the choice of a phase at anintersection is decided locally, the phase threshold isdecided at the level of an area consisting of severalintersections.
The algorithm and models that we report in this paper are a nontrivial generalization of previous approaches that used
UPPAAL STRATEGO. This generalization allows scaling to large cities with several traffic intersections, with improved
results. We compare our approach against other techniques including fixed-time and fully-actuated controllers.
Experiments show that the it performs better in terms of waiting time and carbon emissions, especially in scenarios ofchanging traffic loads. Our approach also reduces overall and individual delays at intersections.

M2SMART NEWSLETTER | VOL.10

3Vehicular exhaust emission under real-world driving conditions:
Role of electric -vehicles on reducing pollutant emission
by Digvijay S. Pawar (Group 3 Leader), Chandrashker C, Prashansa Agrawal and Rachakonda Yashasvi
The current study mainly focuses on the development of driving cycle for E-rickshaw, to evaluate the impact of drivingcharacteristics. In this study, real-world driving data for 100 trips of E-rickshaw are collected on a road stretch passing
through the rural and urban setting. A high-end GPS data logger was used to collect vehicle kinematics such as continuous
speed profile, acceleration/deceleration, heading, and vehicle position coordinates. The driving characteristics
representing actual traffic condition are identified and used for developing E-rickshaw driving cycle (ERDC) using random
selection and k-means clustering method. Development of final driving cycle is shown in Figure 1. This study insight can beused to understand and model the performance of electric vehicles, in terms of energy consumption and driving
characteristics, compared to other fossil-fuel driven automobiles.
The study also measures vehicular exhaust emission under real-world driving conditions from auto rickshaw, to understandthe potential reduction of tail-pipe CO2 by shifting ICEVs to electric vehicles. In this study emissions (CO2 and CO) weredeveloped for diesel auto rickshaw using a Portable emission measurement system (PEMS) on rural and urban traffic
conditions in Hyderabad, India. The study investigates the effect of constant and real-world speeds on emission rate. Theresults show that the constant speed curve of the emission rate lies below the real-world speed curve shown in Figure 2.
The insight from the study can be used to understand the emission factors from diesel auto rickshaws and also the effectof speed on emission rate, which will be helpful for the implementation of congestion mitigation strategies and speed
management system to policy decision-makers for better air quality.

Figure 1. Development of driving cycle for e-rickshaw

Figure 2. Average CO2 emission for real-world and constant speed

M2SMART NEWSLETTER | VOL.104"Scalable Coordinated Intelligent Traffic Light Controller for
Heterogeneous Traffic Scenarios Using UPPAAL STRATEGO",
Effective Design of VMS for Drivers’ Mind Change
by Tomoya Kawasaki (Group 3)
In Ahmedabad, Variable Messaging Sign (VMS) areinstalled in 11 locations as a part of the Intelligent
Transportation System (ITS). We examine what type ofdisplay would be more meaningful to change driver’s
mind for the sustainable transportation system.

picturestexts

In the questionnaire survey, we indicate two types ofdisplays such as “pictogram” and “text” as shown in thefigure. Our proposed VMS varies the color and thenumber of massage lines. In our research, we examine
the effects of color scheme and message lines of VMSs
on drivers’ phycological intentions to change driver’smind. Collected data are analyzed by statistical tests
and obtained following insights.
(1) Possibility of changing driver’s behavior: Significant difference between pictures and texts was found only in "wear seatbelt.” The statistical results imply that low possibility of behavioral change in “eco-transportation".
(2) Understanding of display contents: One of the funfamental peoblems for VMS is to let drivers to understand theintention of displays. From the statistical analysis, text is significantly higher than logo for understaning the contents ofVMS.
Further studies on VMS display is to explore the effectiveness on “No Idling Policy” to improve air quality and reduce CO2emission as well as save their fuel cost. The other is reducing a sudden deceleration and acceleration for the same impact.
This is a future work in the final year of the project.

Machine Learning Method for Spatio-Temporal Prediction of Roadside
PM2.5 based on Sparse Mobile Sensing and Traffic Information
by Soumya Jana (Group 4 Co-leader) , Satish Kumar Reddy M V and Anand Kakarla
Estimating road-side PM2.5 can be important steps in understanding the carbon footprint of transportation sector.
Roadside PM2.5 have been estimated based on readings from road-side static sensors and can be extended by making
ambient atmospheric measurements at high spatio-temporal resolution near all road segments at hand so that thedependency between emission and traffic information could exhaustively be modeled. However, the cost of covering theroadways of an entire city with densely deployed sensors would likely be prohibitive even in developed countries. As analternative, mobile sensing has been suggested, and demonstrated using accurate but expensive portable equipment
including gas analyzers. To further bring down the cost, while sacrificing a degree of accuracy, mobile sensing using lowcost imprecise sensors has been suggested. Of course, one must meaningfully interpolate readings from mobile sensing,whose inherent spatio-temporal resolution could be inadequate for practical application. In this connection, a well known
spatial interpolation method makes use of land use regression (LUR) models involving certain explanatory variables that
empirically categorize road segments according to certain characteristics.

M2SMART NEWSLETTER | VOL.10

5However, LUR models, being temporally invariant, cannot satisfactorily characterize urban road segments as dynamicenvironments. We presently attempt at filling the gap by proposing a spatio-temporal prediction technique, which, insteadof mapping road segments to explanatory variables, obtains a direct relationship between emittant concentration andlocation, time and traffic level and related information using machine learning (ML). In the process of prediction, theFigure: (a) Compact PM2.5 sensor; (b) Auto-rickshaw used for data collection; (c) Google map of ROI showing the chosen routes along withthe corresponding color-coded PM2.5 concentration values.

proposed technique does not merely interpolate, but tends to suitably modify the readings to ensure consistency amongpredicted values in terms of suitable criterion.In our work, we elaborate on the proposed prediction principle, anddemonstrate it by relating traffic information with low-cost readings of PM2.5, a source of health hazard, with significant
contribution from vehicular emission.
In our experiment, each of the volunteers recorded PM2.5 readings at different time instants using a compact sensor(Figure (a)), while traversing road segments located in a commercial area of the Indian city of Ahmedabad in a threewheeled vehicle (auto-rickshaw, Figure (b)). The overall trajectory thus described is shown in Figure (c). Along with eachreading, traffic levels were subjectively recorded as well. Based on collected data, we pose a regression problem to predict
the PM2.5 level as a function of location, time and traffic level.
Subsequently, we develop candidate solutions based on select well known ML models, including ensemble methods suchas Random Forests (RF), Light Gradient Boosting Machine (LGB), Extreme Gradient Boosting (XGBoost, XGB), andcategorical Boosting (CB), as well as Support Vector Regression (SVR). We compare test performances based on rootmean-squared error criteria, and pick the superior model as our proposed method. As expected, we observe that theprediction is more accurate, when information about traffic level is considered.

Table: Performance comparison (superior value in boldface)M2SMART NEWSLETTER | VOL.10

6

Chemiresistive Gas Sensor for Monitoring of low ppm CO gas
with high Selectivity at Room Temperature
by Shiv Govind Singh (Group 4) and Venkata Ramesh N
In recent years, the development of gas sensors has attracted attention due to the air pollution caused mostly by incombustion of fossil fuels in automobiles, fuel-based home appliances and various industries. At present, 90% of theworld’s population lives in areas that exceed the world health organization (WHO) limit on air pollution. Air pollutants
include carbon monoxide (CO), nitrogen oxides (NO 2), sulfur dioxide, and volatile organic compounds (VOCs) have caused
harmful effects on human health. Among the various toxic pollutants, CO and NO2 causes greater concern to humanhealth in the sub-ppm concentration level. Thus, public health and safety controls are much needed for environmental
monitoring to protect them against toxic pollutants, and therefore low cost and highly effective sensor system aredesperately needed. Generally, the toxic air pollutants are being detected with the conventional techniques such as gas
chromatography and mass spectroscopy, involving tedious measurements which make expensive and may take a longer
time for analysis. However, low-cost gas sensor systems often display promising sensing characteristics such as highresponse, short response time and recovery time, long-term stability, excellent reproducibility, and operating at lowtemperatures. Nevertheless, these gas sensors have numerous limitations include low selectivity and high detection limits,which reduces the usage in practical applications. In this aspect, we have fabricated graphene-cerium oxide-based CO gas
sensor, which has a superior selectivity over many atmospheric gases (NO 2, NH3, SO2, CO2) and displayed excellent
performance to detect the low concentrations of CO gas. The schematic diagram of the fabricated gas sensor and sensing
characteristics are as shown in figure below.

a)b)c)

Fig. (a) Schematic diagram of gas sensor. (b) Dynamic sensing response of the sensor from 2 ppm to 10 ppm.
(c) Selectivity of the sensor over other interfere gases in the air pollution.

M2SMART NEWSLETTER | VOL.10

7

Message from Graduating RA
Traffic forecasting using Deep Learningby Shounak Kundu
I would like to thank Dr. Maunendra Sankar Desarkar and Dr.
Srijith P.K. for giving me this opportunity and their guidance.I also
would like to thank M2Smart Project, JICA and JST.
I have explored the Ahmedabad dataset and used several deeplearning techniques. I have explored the Social LSTM and Deep
Transformers model apart from other Deep Learning-basedmodels. I have taken the Ashram Road junction (Figure: 1) for theexperiment. The data is collected from August 1 ,2018, to August
31, 2019. It contains 5,19,556 data points.
Transformers model is extensively used in Natural Language
Processing tasks (like Text Translation, ChatBot, Text
Summarization etc.). I have also used the Transformers basedmodel on the Ahmedabad dataset to predict traffic count. I foundout that the Deep Transformer model has out-performed otherapproaches (except on the MAE evaluation metric).

Fig 1 : Ahmedabad Junction

Model architecture is shown in Figure 2. I found out that the Deep
Transformer model has out-performed other approaches (except on the MAE evaluation metric). (Figure 3 and Figure 4 )Fig 3 : Comparison for various models

Fig 4:

Fig 2: Deep Transformer Model architecture
Fig: 2 Model architecture

Fig 4: Mean Absolute Performance Error (MAPE) for various traffic
count bins for various models on Ahmedabad dataset

M2SMART NEWSLETTER | VOL.10

8PublicationsJournal⚫
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Tsutomu Tsuboi, “Traffic Congestion Triangle based on more than one-month real traffic big data analysis in India”,
Advances in Science, Technology and Engineering Systems Journal Vol. 5, No. 6, pp.588-593 (2020).
Tsutomu Tsuboi, Testuhiro Ishizaka, Hiroto Seki, Daisuke Nishiwaki, Roy Debaditya, Satoshi Takahashi,”Detection ofMulti Type Vehciles under Mixed Traffic Flow Using Deep Learning in Inida”, pp.54-57, Traffic Engineering Vol.56 No.1
(2020).

Conference presentation
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Bhaskar Anand, Vivek Barsaiyan, Mrinal Senapati and P. Rajalakshmi, "Region of Interest and Car Detection using
LiDAR data for Advanced Traffic Management System," 2020 IEEE 6th World Forum on Internet of Things (WF-IoT),
New Orleans, LA, USA, 2020, pp. 1-5, doi: 10.1109/WF-IoT48130.2020.9221354. (Virtual presentation date: 2 June2020)
Bhaskar Anand, Vivek Barsaiyan, Mrinal Senapati and P. Rajalakshmi, "An experimental analysis of various multichannel LiDAR systems," 2020 IEEE International Conference on Computing, Power and Communication
Technologies (GUCON), Greater Noida, India, 2020, pp. 644-649, doi: 10.1109/GUCON48875.2020.9231195. (Virtualpresentation date: 3 October 2020)
Mrinal Senapati, Bhaskar Anand, Vivek Barsaiyan and P. Rajalakshmi, "Geo-referencing system for locating objects
globally in LiDAR point cloud," 2020 IEEE 6th World Forum on Internet of Things (WF-IoT), New Orleans, LA, USA,2020, pp. 1-5, doi: 10.1109/WF-IoT48130.2020.9221162.(Virtual presentation date: 2 June 2020)
Bhaskar Anand, Anuj G. Patil, Mrinal Senapati, Vivek Barsaiyan and P. Rajalakshmi, "Comparative Run Time Analysisof LiDAR Point Cloud Processing with GPU and CPU," 2020 IEEE International Conference on Computing, Power andCommunication Technologies (GUCON), Greater Noida, India, 2020, pp. 650-654, doi:
10.1109/GUCON48875.2020.9231067. (Virtual presentation date: 3 October 2020)
Thamilselvam B, Subrahmanyam Kalyanasundaram, Panduranga Rao Marella,"Scalable Coordinated Intelligent Traffic Light
Controller for Heterogeneous Traffic Scenarios Using UPPAAL STRATEGO", COMSNETS 2021, January 6-8, 2021, VirtualConference. (Virtual presentation date :6 January 2021)
Anshika Chourasia, Bheemarjuna Reddy Tamma, and Antony Franklin A. "Wi-Fi based Road Traffic Monitoring Systemwith Channel Hopping”, COMSNETS Workshop on ITS , Virtual Conference. (Virtual presentation date :9 January 2021)

M2SmartNewsLetterVol. 10Jan 2021

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