M2SMART PROJECT

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M2SmartNewsLetter

Vol.9
Oct 2020

M2SMART PROJECT ONLINE MEETING

M2Smart Project JST mid-term review andpresentation to the Indian Embassy in Tokyo
by Tsuboi Tsutomu (Project Leader)Continuation of the Project
remotelyJapan Science and Technology
Agency (JST) Intermidiat Review
presnantion was conducted on 22
July in Toky0 and we presented ourproject research activities to theexternal reviewers of the Scienceand Technology ResearchPartnership for Sustainable
Development (SATREPS)
organization. As the intermediate
review of the SATREPS, we recived“A” score.

Nagoya Electric Works had ameeting with Ms. Mona Khandhar,
Minister Economic & Commerce,
Indian Embassy in Tokyo and shared
our M2Smart Project activities. Afterthe meeting, Ms.Khandhar sent anemail to the Commissioner ofAhmedabad Municipal Corporation(AMC) about providing strong
support for M2Smart Project and werecived their support comment fromthe Commisioner of AMC.

Presentation slide for theJST Mid-term review

Ms. Khandhar and Ms. Kajimotofrom Indian Embassy in Tokyoand Mr.Hattori, CEO andMr.Muto, General Manager andDr.Tsuboi from Nagoya ElectricWorks, 6th of July

M2SMART NEWSLETTER | VOL.9Real-Time Detection of Motorcyclist without Helmet using
Cascade of CNNs on Edge-device
by C Krishna Mohan (Group 1 Leader)Fig1. Block diagram of proposed framework for the detection of motorcyclists without Helmet

Fig2. The architecture and user interface of the proposed edge computing-based framework for the detection of violators

The real-time detection of traffic rule violators in a city-wide surveillance network is a highly desirable but challenging task
because it needs to perform computationally complex analytics on the live video streams from a large number of cameras,
simultaneously. In this work, we propose an efficient framework using edge computing to deploy a system for automaticdetection of bike-riders without helmets. First, we propose a novel robust and compact method for the detection of
motorcyclists without helmets using convolutional neural networks (CNNs). Then, we scale it for the real-time performance onan edge-device by dropping redundant filters and quantizing the model weights. To reduce the network latency, we place thedetector module on edge-devices in the cameras. The edge-nodes send their detected alerts to a central alert database wherethe end users access these alerts through a web interface. To evaluate the proposed method, we have collected two datasets ofreal traffic videos, namely, IITH Helmet 1 which contains sparse traffic and IITH Helmet 2 which contains dense traffic. The
experimental results show that our method achieves a high detection accuracy of ≈ 95% while maintaining the real timeprocessing speed of ≈ 22f ps on Nvidia-TX1.

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M2SMART NEWSLETTER | VOL.9

3An intelligent transportation system is the integration of various advanced technologies such as intelligent computing, network
communications, visual representation, visual-based analysis, efficient sensor electronics. Since, motorcycles are an affordable
and daily mode of transport, there has been a rapid increase in motorcycle casualties due to the fact that most of themotorcyclists do not wear the helmet which makes it an ever-present danger every day to travel by motorcycle. The main
contributions of this paper are, a)Design of a robust and reliable method for detection of moving motorcyclists in real-time using
convolutional neural network (CNN) under the various challenging conditions, such as viewpoint, illumination effects, weatherchange, etc., b)Acceleration of the CNN model used to detect motorcyclists in real-time on the limited-resource embeddeddevice. c)Develop lightweight but powerful CNN model for efficient classification of head (i.e. violator) and helmet, with very
limited set of training samples. d)Use of an Edge-computing framework to overcome the communication overhead and network
latency.

The proposed framework for real-time detection of motorcyclists driving without helmets is able to perform in diverse
surveillance conditions. Also, there is a significant reduction in the number of false alarms because of the use of cascaded CNNs.
The placement of the detector modules in the vicinity of the capturing devices in an edge-computing framework reduces thecommunication overhead and solves the issue of network latency. The experimental results show the efficacy of the proposedapproach.

IITH_Helmet_1 classification accuracy during H-Net training

IITH_Helmet_2 classification accuracy during H-Net training[1] Q. Wang, J. Wan, and Y. Yuan, “Locality constraint distance metric learning for traffic congestion detection,” PatternRecognition, 2017.
[2] Y.-B. Lin and C.-P. Young, “High-precision bicycle detection on single side-view image based on the geometric relationship,”Pattern Recognition, vol. 63, pp. 334 – 354, 2017.
[3] D. Singh, C. Vishnu, and C. K. Mohan, “Visual big data analytics for traffic monitoring in smart city,” in IEEE ICMLA, Anaheim,California, 18–20 December 2016.

M2SMART NEWSLETTER | VOL.94“SafeNav: A Cooperative V2X System using Cellular and 802.11p
based Radios Opportunistically for Safe Navigation”
by Suhel Sajjan Magdum, Antony Franklin (G2 and G4), Bheemarjuna Reddy Tamma (M2Smart Co-Project Manager),
Digvijay S. Pawar (G3 Leader)

The V2X communication technologies are gaining popularity in recent times for various transport applications such as
autonomous driving, collision avoidance, traffic management, etc. All these come with the addition of communication
technologies in the vehicle. While there are various communication technologies being considered for V2X each one ofthem have their own advantages and disadvantages. Relying on a single radio or V2X mode of communication is notdesirable in the design of V2X systems of the future. In the absence of dedicated ITS communication infrastructure on theroad side, we can rely on other communications technologies such as Wi-Fi Direct for V2V (vehicle to vehicle)
communication and 4G/5G for V2N (vehicle to network) communication. If direct V2V communication using Wi-Fi is notpossible with other vehicles due to the absence of Wi-Fi or restricted communication range, vehicles can rely on 4G/5G
network for indirect mode of communication using V2N. To capitalize the benefits of both V2V and V2N, we proposed acooperative and distributed V2X architecture based on the radios present in the vehicles (WiFi or cellular) for reliable andscalable communication among the vehicles. Below figure shows the proposed distributed V2X architecture for safe
navigation which consists of three layers: vehicles, base stations, and an edge relay server. We can run safety applications
in vehicles or offload some tasks to edge server via base stations whenever possible. The effectiveness of the proposed
V2X system is demonstrated by developing a collision warning system for safe navigation named SafeNav. Simulation
results suggest that using both V2V and V2N modes of communication significantly improves the reliability of safety
message exchange and visibility of neighbouring vehicles especially in hybrid scenarios where the vehicles have either
cellular or WiFi based radios. An Android app has been developed to further demonstrate the usefulness of the proposedsystem for collision avoidance in a real-world scenario. Below figure shows a screenshot of SafeNav user interface. We
tested the app in a a cross section with three vehicles approaching towards each other. If any of other two vehicles comes
into the collision domain, visual and audible alerts are generated by changing the color of such vehicles to red and giving
beep sounds.

Fig: Distributed V2X architecture for safe navigation

Fig:SafeNav: Android App demonstrating three vehicles approaching at a junction

M2SMART NEWSLETTER | VOL.95

Network Traffic State around Paldi Intersection using Historical
Traffic Data and Microscopic Fundamental Diagramby Tesuhiro Ishizaka (Group 2 Leader)
The main goal of group 2 is to generate insights to help in better city and travel planning to avoid congestion based on not
only available information obtained from various sources, but also estimated traffic state using various cutting edgetechnogoly and theoritical traffic modeling. One trial of applying theoritical traffic modeling into rich historical traffic
camrea data is shown in this newsletter as a part of group activites. The histrical traffic data using the traffic camera covers
the area in west side of Ahamedabad citiy, including the Paldi intersection where is our main target point. However, The
traffic volume, speed et al collected by the traffic seonsor show only perspective of traffic state which is expressed on local
spot area under traffic camera. On the other hand, the Microscopic Fundamental Diagram(MFD) to express network traffic
state had been proposed by Carlos F. Daganzo(2007). We applied this model into the histrical traffic data around Paldi
insetersection to express wide network traffic state. We expect it might be one tool to analyze and evaluate impact on
traffic imporvement by Various Message Sign Boards. Anyway,in first trial of applying MFD, the general fundamental
diagram and MFD was drawn in the figure. In furthure study, we should consider quality of histrical traffic data and also thetheoritical condition of MFD.

Fig. General Fundamental Diagram of spot area under traffic camera

Fig. Microscopic Fundamental Diagram in three area surrounded Paldi intersection

Incident Detection From Social Media Targeting Indian Traffic
Scenario Using Transfer Learning
by Maunendra Sankar Desarkar (Group 2 Co-Leader), Shounak Kundu and Priyambada Ambastha
We have presented a paper titled “Incident Detection from Social Media targeting Indian Traffic Scenario using TransferLearning” in The 23rd IEEE International Conference on Intelligent Transportation Systems on September 21st, 2020. The
conference was to be held at Rhodes, Greece but due to unforeseen circumstances of the Covid-19 pandemic, the conference
was hosted at a virtual platform. In this paper, we have addressed the road congestion problem by developing a system to detect
traffic congestion in India using Twitter. Twitter has been gaining momentum for research in event detection for the past several
years because many commuters, as well as traffic authorities, tend to post traffic-related updates in real-time.
Since there is no such traffic-tweet dataset for the Indian traffic scenario, we developed one such dataset using 3 different
techniques. The dataset contains posts that talk about traffic incidents such as accidents, infrastructure damage, and also about
future planned events that can impact traffic flow concerning different Indian regions. The dataset has been labelled into twoclasses- Traffic Incident (TI) and Non-traffic Incident (NTI). We call our dataset as L-TWITS (Labelled-TWeets for Indian Traffic
Scenario).

M2SMART NEWSLETTER | VOL.96Figure 1 shows the word clouds for our dataset and an existing dataset collected from the US. It can be seen that theprominent words present in our dataset are different from that in the US dataset. However, heavy usage of location
mentions like highway, lane, street etc. in US tweets and marg, road, flyover etc. in Indian tweets indicate contextual
similarity between many terms in the data acorss the two datasets. We can observe that factors affecting Indian traffic areroad jam, congestion, protests, accidents, broken down vehicles etc. and for US, words like crash, block, roadworks,construction, disabled vehicles etc. are cited as reasons for traffic incidents.
Basic practice in literature for traffic event detection problems is to collect a large amount of data, obtaining its annotationand then further analysis for event extraction. Such approaches often require a considerable amount of time for labellingthe data. To address this shortcoming the proposed method uses a Transfer learning-based classifier that generally performs
well even with less data. ULMFiT model has been used as a Transfer Learning approach for classifying the tweet samples
into “Traffic incident related” or “Non-Traffic incident related” category. The performance of different deep learning

Figure 2: Map with markers for the tweet locations

Figure 1: Word cloud distribution for the datasets

algorithms on our dataset are shown in Table 1. It can be seen that ULMFiT performs batter than the other methods in termsof Precision, Recall and Macro-F1 score. Figure 3 shows the performance of the ULMFiT algorithm for different sizes of thetraining data. One can observe that ULMFiT can surpass the performances of the CNN and LSTM methods by just using 50%of the training data from the new dataset. To summarize, experimental results on our labelled dataset show that ULMFiToutperforms other classification models even with very small number of training data making our model a convenient one
for extracting traffic-related information targeting Indian scenario. A validation dataset is created which contains tweets
associated with heavy rains, floods and religious events from India. ULMFiT performed with 0.87 F1-Score for TI class invalidation dataset which indicates that the model is not biased towards protest like traffic events and performed similarly
on random data sample of different events.

Table 1: Performance cmparison between different algorithms on the L-TWITS dataset

Figure3: Performance of ULMFiT on L-TWITS test set. x-axis

M2SMART NEWSLETTER | VOL.97Publications
Conference presentation
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Debaditya Roy, K. Naveen Kumar, C. Krishna Mohan, “Defining Traffic States using Spatio-temporal Traffic Graphs”, The 23rdIEEE International Conference on Intelligent Transportation Systems, September 20-23, 2020, Virtual Conference. (Virtualpresentation date :22 September 2020)
Dinesh Singh, C. Vishnu and C. Krishna Mohan, “Real-Time Detection of Motorcyclist without Helmet using Cascade of CNNs
on Edge-device”, The 23rd IEEE International Conference on Intelligent Transportation Systems, September 20-23, 2020,
Virtual Conference. (Virtual presentation date :23 September 2020)
Suhel Sajjan Magdum, Antony Franklin, Bheemarjuna Reddy Tamma, Digvijay S. Pawar, “SafeNav: A Cooperative V2X
System using Cellular and 802.11p based Radios Opportunistically for Safe Navigation”, The 23rd IEEE International
Conference on Intelligent Transportation Systems, September 20-23, 2020, Virtual Conference.
Priyambada Ambastha, Maunendra Sankar Desarkar, “Incident Detection From Social Media Targeting Indian Traffic
Scenario Using Transfer Learning”, 23rd IEEE Intelligent Transportation Systems Conference, IEEE ITSC 2020, Rhodes,Greece, September 20-23, 2020. (Virtual presentation date: 21 September 2020)
Tsutomu Tsuboi, Noriaki Yoshikawa, “Traffic flow analysis in Ahmedabad (India)”, Case Studies on Transport
PolicyVolume 8, Issue 1, March 2020, pp.215-228. DOI: 10.1016/j.cstp.2019.06.001
Tsutomu Tsuboi, “Quantitative Traffic Safety Analysis for India by Japanese Experience”, Sociology Study Jan.-Feb. 2020
Vol.10, No.1, pp.1-14. DOI: 10.17265/2159-5526/2020.01.001
Tsutomu Tsuboi, “Traffic Congestion Analysis and Occupancy Parameter in India”, International Conference on DataAnalytics and Management: An Indo-European Conference (ICDAM-2020), June 18, 2020.
Tsutomu Tsuboi, “New Traffic Congestion Analysis Method in Developing Countries (India)”, 6th International
Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS), 2-4 May 2020.
Tsutomu Tsuboi, “Challenge of Traffic Flow Analysis in India by Traffic Theory”, The 8th World Sustainable Froum 2020,
16 September 2020.

M2SmartNewsLetterVol. 9
Oct 2020

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