M2SMART PROJECT

1M2Smart
NewsLetter

Vol.8
June 2020

M2SMART SKYPE MEETING WITH JAPAN BEFORE THE LOCKDOWN AT IITH

COVID-19 Impact on M2Smart Project
by Haruka Katarao, M2Smart Resident Coordinator

Through Virtual Communication
The Covid-19 has made a huge
impact on all of our lives including
our M2Smart Project. Japan has
introduced work from home since
February and IITH has been closed
since March.

As Jaya Sharma wrote in her writing,“Work from home under COVID-19
and lock-down situation” (Page 8),
we are experiencing such an
exceptional situation which nobody
could have imagined.

Prior to the total lockdown in India
from March 25, the M2Smart
Resarch Assistants (RAs) were sent
back to their hometown andcontinue their work from home.

In this issue, you can see how theM2Smart RAs continue theirresearch work under the difficult
situation. They have also
participated in the virtual online
conferences to present their papers.

M2Smart lab before the lockdown

These show us the silver lining in thecurrent situation.

M2Smart RA, working fromhomeM2SMART NEWSLETTER | VOL.8

Research on “Defining Traffic States using Spatio-temporal
Traffic Graphs” using Ahmedabad Aerial-view Dataset Accepted in IEEE ITSC-2020 Conference
by C Krishna Mohan (Group 1 Leader), Debaditya Roy and K Naveen Kumar

Fig.1 Different types of traffic states (a) clumping - the vehicles
bunch together rapidly, (b) neutral - the vehicles maintain the same
relative speed and gap, and (c) unclumping- the vehicles disperse away
rapidly

Intersections are one of the main sources of congestion andhence, it is important to understand traffic behavior atintersections. Particularly, in developing countries with highvehicle density, mixed traffic type, and lane-less driving behavior,
it is difficult to distinguish between congested and normal traffic
behavior. In this work, we propose a way to understand the traffic
state of smaller spatial regions at intersections using trafficgraphs. The way these traffic graphs evolve over time reveals
different traffic states - a) congestion is forming (clumping), b)
congestion is dispersing (unclumping), or c) traffic is flowing
normally (neutral). We train a spatio-temporal deep network toidentify these changes. Also, we introduce a large dataset called
EyeonTraffic (EoT) containing 3 hours of aerial videos collected at
3 busy intersections in Ahmedabad, India. Our experiments on theEoT dataset show that the traffic graphs can help in correctly
identifying congestion-prone behavior in different spatial regionsof an intersection.

Fig.2: Spatial regions considered for annotation at each of the threeintersections in the EoT dataset. Red denotes clumping, yellow
denotes neutral,and blue denotes unclumping. Each way is denoted bya number.

2

M2SMART NEWSLETTER | VOL.8Intersections are a major cause of congestion in urban networks especially in the case of lane-less mixed traffic where alarge number of smaller vehicles bunch together at stop signs in an irregular fashion. Determining the level of congestion
at intersections is mainly performed in existing works by counting the number of vehicles [1]. While this is suitable for lanebased traffic, these methods are not applicable to high irregular traffic density with varying sizes of vehicles in developing
countries like India. Furthermore, there is a high propensity for lateral movements and low gap maintenance in such trafficthat may indicate congestion but may actually denote normal traffic flow.

The aforementioned issues make it imperative to define a set of traffic states specifically for understanding mixed laneless traffic. Hence, in this work, we propose a novel characterization of traffic states using traffic graphs. The evolution ofthese interactions can be best represented using traffic graphs [2] that change over time. Particularly, we observed thatthe traffic graph structure of a particular spatial region changes when too many vehicles congregate or disperse in a shortspan of time. The temporal pattern of changing neighbors is best represented using the adjacency matrix of the trafficgraph. So, we propose to learn the spatio-temporal pattern of these adjacency matrices using a network consisting ofConvolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) units.

In this work, an approach to identify traffic states in lane-less traffic using temporal changes in the traffic graph wasproposed. We showed that a spatio-temporal CNN-GRU network trained on the adjacency matrix of a traffic graph canidentify clumping, unclumping, and neutral traffic states in various spatial regions of an intersection. Further, we showed
the effectiveness of the proposed approach on a large annotated aerial dataset called EyeonTraffic that covered 3
intersections in Ahmedabad, India. In the future, we would like to apply this approach to predict the onset of congestion byobserving the changes in traffic behavior.

[1] P. Chakraborty, Y. O. Adu-Gyamfi, S. Poddar, V. Ahsani, A. Sharma,and S. Sarkar, “Traffic congestion detection from camera
images using deep convolution neural networks,” Transportation Research Record, vol. 2672, no. 45, pp. 222–231, 2018.
[2] L. Zhao, Y. Song, C. Zhang, Y. Liu, P. Wang, T. Lin, M. Deng, andH. Li, “T-gcn: A temporal graph convolutional network
for traffic prediction,” IEEE Transactions on Intelligent Transportation Systems, 2019.
[3] L. Wen, D. Du, Z. Cai, Z. Lei, M. Chang, H. Qi, J. Lim, M. Yang, and S. Lyu, “UA-DETRAC: A new benchmark and protocol for
multi-object detection and tracking,” Computer Vision and Image Understanding, 2020.

3

M2SMART NEWSLETTER | VOL.8

4IEEE Sensors Applications Symposium-2020
by P Rajalakshmi (Group 2), Bhaskar Anand, Vivek Barsaiyan

We have presented a paper titled “Coarse Object Tracking Technique for Point Clouds” at IEEE Sensors Applications
Symposium-2020. The venue for the conference was supposed to be in Kuala Lumpur, Malaysia. However, due to thepresent scenario of Corona pandemic, the conference took place on virtual platform. We are working with Dr. PRajalakshmi for traffic sensing using LiDAR in M2SMART project. As LiDAR becomes ubiquitous in intelligent
transportation systems, it becomes important to be able to accurately track objects using point cloud data. We haveproposed a coarse object tracking and object matching technique via voxels which reduces run time without compromisingon accuracy. It also allows for evaluation of quality of matches. The performed experiment and simulation results give an
initial direction for dynamic object tracking, which will be explored in greater detail in the future.

Fig:Experiment performed at IITH

Fig: Voxel-based dynamic object re-association

M2SMART NEWSLETTER | VOL.8

5Coordinated Intelligent Traffic Light Controller in 23
intersections in Ahmedabad Cityby Subrahmanyam Kalyanasundaram (Group 2), M V Panduranga Rao (Group 2), Thamilselvam B

We have analyzed the result of Coordinated Intelligent Traffic light with four different types of controller, Uppaal stratego
Single Intersection, Intelligent Traffic Light Controller without coordination, Fixed-time and Fully actuated. Our
Coordinated Intelligent Controller works better than all other controller in terms of carbon emission. Table 1 shows thesimulated result.

Table 1
The cumulative delays for the five different controllers at every 2 seconds are plotted in Fig 1. Since the area controllers
update local coordinated controllers’ maximum green time dynamically, the intelligent controller takes decision such thatto make traffic flow smooth and all cases there is the smooth traffic flow among the neighbouring junctions.

Figure 1. Cumulative waiting time of vehicle

M2SMART NEWSLETTER | VOL.8

6

We compare our model features with two different models designed in Eriksen et al. [1] Paper 1 and Thamilselvam,
Kalyanasundaram, and Rao [2] Paper 2 in Table 2.
FeaturesUppaal Stratego Paper 1

Coordinated Uppaal
Stratego Paper 2

Coordinated Intelligent
Present Work

Heterogeneous Vehicle type

No

No

Yes

Phases

224

Number of Intersections

1223

Coordination

No

Yes

Yes

Scalability

No

No

Yes

Input

Vehicle counts

Vehicle counts

Vehicle counts, Queue Length

Optimization parameter

Delay

Delay

Delay, Queue Length

Spatial priority

No

No

Yes

Table – 2
Ref: 1: Eriksen, A.B., Huang, C., Kildebogaard, J., Lahrmann, H., Larsen, K.G., Muniz, M., Taankvist, J.H.: Uppaal stratego
for intelligent traffic lights. In: 12th ITS European Congress. (2017)
2: Thamilselvam, B., Kalyanasundaram, S., Rao, M.P.: Coordinated intelligent traffic lights using uppaal stratego. In: 201911th International Conference on Communication Systems & Networks (COMSNETS), IEEE (2019) 789–794

Traffic Forecasting with Deep Learning
at IEEE Region 10 Symposium (TENSYMP) 2020
by Maunendra Sankar Desarkar (Group 2 Co-Leader), Srijith P. K.(Group 2) , Shounak Kundu

The theme of IEEE Region 10 Symposium (TENSYMP) 2020 was “Technology for Impactful Sustainable Development.” Theobjective of submitting a paper to this conference was to get the ongoing work reviewed and acknowledged by peers.
Another purpose was to understand the activities that are being performed across the globe on technologies for sustainable
development. The work on “Traffic Forecasting with Deep Learning” done by Group 2 of the M2Smart project was a goot fit
for the conferene theme as well.
Timely forecast of traffic is very much needed for smart cities, which allows travelers and government agencies to makevarious decisions based on traffic flow. This would result in reduced traffic congestion and carbon dioxide emission. Theproblem tackled in this paper is the Traffic Count Prediction problem. It can be seen as a time-series prediction problem,
where one collects the traffic counts in a junction at some time intervals, and wants to predict the unobserved count at somefuture time interval. Vehicle count data at junctions can be obtained from various sensors available or from cameras. An
accurate prediction of vehicle count will help the authorities to plan, monitor and handle the road network more efficiently.

M2SMART NEWSLETTER | VOL.87However, the traffic count data is quite complex, often noisy, and exhibits highly nonlinear characteristics. Traditional
approaches based on time series models fail to capture these aspects of the data as they mostly assume a linear relationship.
In this paper, the team investigates how the deep learning models can capture these characteristics and provide better
predictive performance over standard time series and regression models. The performances of state-of-the-art deeplearning models on two traffic flow data sets were compared and their effectiveness in traffic flow prediction over traditionalmodels was demonostrated.

Fig: Stacked Convolutional LSTM based forecasting model

Table: Performance analysis of the different forecasting methods

Two common baselines for predicting traffic ounts are the Ridge regression (Linear Regression with L2 Regularization) and
Vector Auto Regression. However, both these models consider linear relationship among the observations acrosstimestamps. Recent Deep Learning Models such as LSTM capture nonlinear temportal relationship in the data. Although
LSTMs can model the nonlinear relationship, traffic forecasting needs to make use of spatial information as well. Traffic ata junction is influenced by the traffic at nearby junctions. Capturing such spatial relationships can be done using
Convolutional Neural Networks (CNN). However, CNNs can not capture the temporal relationship in the data. Since both
temporal and spatial relationships are important in traffic forecasting, convolutional LSTM model was used for the task. Fiveconvolutional LSTM layers were stacked together to get the final neural model for the forecasting. Empirical evaluations on
two benchmark datasets from US and UK show that Convolutional LSTM is indeed able to predict the traffic volume withhigher accuracy. The tables above present the performance comparison of the different models for this forecasting task.
The work was received well by the attendees in the conference. There were few follow up queries that were addressedsatisfactorily.

M2SMART NEWSLETTER | VOL.8

8Publications
Conference presentation⚫

Anjani Josyula, Bhaskar Anand, Vivek Barsaiyan, Mrinal Senapati and P Rajalakshmi, “ Coarse Object Tracking Technique forPoint Clouds”, IEEE Sensors Applications Symposium, March 9-11, 2020, Kuala Lumpur, Malaysia. (Virtual presentation date :15th March 2020)⚫

Shounak Kundu, Maunendra Sankar Desarkar, Srijith P.K, “Traffic Forecasting with Deep Learning”, 2020 IEEE Region 10
Symposium (TENSYMP), 5-7 June 2020, Dhaka, Bangladesh. (Virtual presentation date : 7th June 2020)

Work from home under COVID-19 and lock-down situation
by Jaya Sharma
In this current situation of COVID-19, whole world is in sorrow and is facing tremendous difficulties. Everyone wants to be with
their family to support each other and IIT Hyderabad has taken this step before the lock-down situation arose in India, so as toprotect us and send all the students back to their home safely and has given us work from home facility. Also, when the world isgoing to face the recession period again and many people are still not sure whether their job is going to last or not? We are
grateful to our institute, director, faculty, staffs, JICA (with whom we are working in collaboration) and everyone that we are thestudents of IIT Hyderabad who are still getting their stipends on time, and if there is any problem regarding VPN connection or
any help that we need from institute, our institute staff is always available to solve it out on a single mail.
Though, it is bit difficult for us to work from home as we face various issues like internet connection and slow speed, serverconnection, but we are still managing to continue our research works and trying to get the results.
I have started to work on a new task that is Accident Detection. I have been doing the literature survey and exploring different
methods to use it on our data-sets and to come up with a novel approach so that I can also write a paper on it.

Working from home, Chhattisgarh

M2SmartNewsLetterVol. 8June 2020

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