M2SMART PROJECT

1M2Smart
NewsLetter

Vol.2March 2019

AMD TRAFFIC CAMERA MONITORING AT IITH

AMD MEETING IN FEBRUARY 2019

The Multimodal Application
by Tsutomu Tsuboi
What’s happening in Ahmedabad?
The Multimodal application (MMA)
field test Phase 2 has been done from23rd of February to 28th. The field test
team at Phase 2 consisted of Metrocompany (MEGA: Metro Express
Gandhinagar Ahmedabad) employee
and 10 local University students. The
10 students invited additional
monitors during the test. The total
participants. In this test, somelimitations and software errors arefound, which will be fixed by the endof March.

There are two interview meetings withthe students on 27th and 28th.
According to their feedback, MMA is
the first navigation application
providing various transportations
options, i.e., not only bus (BRT, Citybus) but also taxi, rickshaw, private
vehicle to their destination. And
transportation choices are flexible
based on interest such as economicalway, time critical way, easy
connection way, etc.
Once the MMA software limitations
are fixed, mass release program will bestarted from April 2019.

Preparation meeting in Ahmedabad

M2SMART NEWSLETTER | VOL.22Vehicle sensing in CCTV video data
by C Krishna Mohan, Group 1 leader
Vehicle sensing aids in analyzing the traffic flow which includes tasks of counting number of vehicles, vehicle speed
estimation, vehicle trajectory prediction, gap acceptance, accident detection, etc. Vehicle sensing relies on suite ofsensors which produce CCTV video data, LiDAR data, etc. Several deep convolutional neural network architectures wereexplored to detect and track vehicles in dense Ahmedabad city traffic scenario. Vehicle tracking is formulated as decision
making in Markov Decision Processes (MDPs), where the lifetime of a vehicle is modeled with MDP. Vehicle detection andtracking tasks are performed in real-time with a processing speed of 15-20 frames per second. Counting number of tracks
of vehicles throughout the video feed yields vehicle count. Currently work is in progress related to vehicle counting andspeed estimation from various traffic datasets of Ahmedabad in continuous fashion.

Fig: Demo of vehicle Tracking

Fig: Demo of vehicle detectionFig: Demo of vehicle countVehicle sensing using LiDAR point cloud depth information
by P. Rajalakshmi, Group 1
LIDARs are becoming popular for traffic sensing because of their ability to sense the depth information. LiDAR point cloud contains
a huge number of points. To make the segmentation faster a pipeline is implemented which works in a parallel fashion. Also, LiDAR
data can be used to share the traffic information among vehicles or between a vehicle and Road Side Unit (RSU). To make thetransmission of huge point cloud data possible, an implementation set up was created in the lab. Here point cloud is compressed toget text file of much smaller size which can be easily transmitted for analysis.

Figure: Setup of compression and transmission of point cloud data

M2SMART NEWSLETTER | VOL.2

3Analyzing Route Segment Performance
Based on Multiple Indicators Using DEA
by Tesuhiro Ishizaka, Group 2 Leader

1DEA
DEAF Score or BTI Value

It’s important to calculate reliability as an index to measure the service level of
public transportation. However, these service reliability metrics can’t account for
spatio-temporal dynamics. Further, comprehensive measure that can isolate
inefficient location and temporal zones is necessary to strategize preventive
measures. In this work, we analyzed the service reliability of Ahmedabad BRT
across both space and time by diving the route into stop-to-stop level segments.
Subsequently, a relative performance measure for each segment of a particularroute which incorporated multiple metrics was obtained using data envelopment
analysis (DEA). Measures including average travel time and peak deviation wereused in this analysis. Based on the scores for each segment, the underperforming
locations were identified and ranked. Finally, the DEA scores were comparedagainst BTI (Buffer time index) to demonstrate its non-triviality and uniqueness.
When the segments were ranked according to DEA and BTI, DEA indicateddistinct ranks. Such DEA analysis can be used when interaction between factors
contributing to reliability is unknown. Additionally, considering routes as awhole, relative performance of each route was also obtained.

0.8

BTI

0.6

0.4

0.2

012345678

9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26Segment

DEA scores and BTI values of all the segments

Predicting future traffic volume using Data Analytics
by Maunendra Sankar Desarkar, Group 2 Co-leader
Planning of travel is often important to avoid unforeseen delays while commuting. Such delays can result in anxiety, loss ofbusiness hours, etc. As part of this project, algorithms are being developed to predict the traffic volume at different junctions of
the road network at future hours. This predicted information can be used for travel time estimation, route planning, taking
decisions regarding traffic regulation, etc. The approaches assume the presence of past traffic data in the form of number ofvehicles that crossed different junctions of the city to make the predictions. Several analyses were performed on available
benchmark data from other cities to observe patterns in traffic across days, months and years, and also to understand the impactof traffic across junctions. Few machine learning and deep learning-based models such as regression, time series modeling(ARIMA), Long- Short-Term-Memory (LSTM), etc. were developed to predict the traffic volumes for future time steps. Detailed
comparison was performed among the developed methods to understand their relative performances. In the comparisons, LSTMbased method was found to outperform other methods significantly for the vehicle count prediction task.

M2SMART NEWSLETTER | VOL.2

4

ITS Test bed for traffic safety and to reduce vehicle emissions
by Digvijay S. Pawar, Group 3 Leader

1) Intersection conflict warning system: The goal of theIntersection Conflict Warning System (ICWS) is to reduce fatal andserious injury crashes at un-signalized intersections by warning
the drivers in real-time about the presence of conflicting traffic at
the intersection. This system will also help to manage the traffic
efficiently by increasing the throughput by avoiding unnecessary
accelerations and decelerations and therefore reducing thevehicle emissions. The research team at IITH has designed several
kinds for smart traffic signages that address the safety concerns
at un-signalized intersections. As per the road accident statistics,
around 70% of accidents are recorded at rural unsignalizedintersections. The ICWS developed at IITH, if deployed at large
scale on rural intersections, will tend to reduce the crashes by 50%thus dipping the congestion, travel time, and therefore reducing
the emissions.
Benefits• Reduce right angle (T-bone) crashes.
• Real-time warning of traffic.
• Reduce traffic congestion, queue length and emissions.
Current status of the work:• Critical gap analysis completed. Critical gap size is used toidentify the location and placement of traffic detection zones
and the respective sensor poles.
• Installation of poles is in progress at all test bed locations.

2) Advanced driver assistance system for safe merging: It is a traffic waring system designed to regulate the flow of traffic entering
freeways according to current traffic conditions. This system will lead to a) Improved System Operation: Ramp metering essentially aims tocontrol the access to a freeway to reduce congestion b) Improved Safety: Reducing the risk of accidents arising out of sudden driver decisions.
b) Reduced vehicle operating expense and emission: Ramp metering essentially reduces the number of stops and delays for the freeway as wellas the ramps.

3) Speed Enforcement and Warning System: - This system is designed to improve the driver compliance with speed limits on thehighways. The fuel economy of a vehicle varies with the speed of the vehicle. The fuel economy of the vehicles is maximized at optimum speed,which will be ensured through this system. Observing/enforcing current highway posted speed limits or the calculated optimum speeds will
result in the reduction of GHG emissions.
reduction of GHG emissions
vehicles is maximised at speeds between 50 and 90 km/h

M2SMART NEWSLETTER | VOL.25Measuring PM 2.5 in dense traffic areasby Atsushi Fukuda, Group 4 Leader

PM2.5 profile at 2 PM

Since WHO reported 14 of the world's polluted 15 cities are in India, it is important to clarify thecontribution of roadside emission as the source of pollution. Even though the main task of
M2Smart project is to propose multimodal strategies and estimate the resulting GHG emission
reduction, development of the sensing technologies for roadside emissions will assist in such
GHG measurements. Specifically, monitoring of emission and analyzing the spatial andtemporal distribution of pollution will be very essential. Such monitoring can be efficiently
made through mobile sampling. As a preliminary work, we choose a commercial area named
Iskcon crossroads in Ahmedabad and sampled PM2.5 in a moving vehicle with a low-cost
sensor at different times of the day.
Main observations include.
•Average PM concentration in the afternoon was less than that of evening time.
•Pollution reading dropped immediately as the vehicle moved far away from the intersections
indicating a spatial trend

PM2.5 profile at 6 PM

However, it was observed that measurements must be made extensively across space and time
which shall be made in the future.

PUBLICATIONS
Conference papers⚫

Anjani Josyula, Bhaskar Anand, and P. Rajalakshmi, "Fast Object Segmentation Pipeline for Point Clouds Using Robot OperatingSystem", IEEE 5th World Forum on Internet of Things, 15-18th April, 2019, Limerick, Ireland.

Workshop papers⚫

Bhaskar Anand, Vivek Barsaiyan, Mrinal Senapati, and P. Rajalakshmi, "Real Time LiDAR Point Cloud Compression and Transmission
for Intelligent Transportation System", 1st International Workshop on Internet of Autonomous Vehicles (INAVEC), co-located withVTC2019, 28th April - 1st May, 2019, Kuala Lumpur, Malaysia.

M2SmartNewsLetterVol.2March 2019

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