Defining Traffic States using Spatio-temporal Traffic Graphs

Roy, Debaditya and Kumar, K. Naveen and Mohan, C Krishna (2020) Defining Traffic States using Spatio-temporal Traffic Graphs. In: 23rd IEEE International Conference on Intelligent Transportation Systems, ITSC 2020, 20 September 2020through 23 September 2020, Rhodes.

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Abstract

Intersections are one of the main sources of congestion and hence, it is important to understand traffic behavior at intersections. Particularly, in developing countries with high vehicle 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 traffic graphs. The way these traffic graphs evolve over time reveals different traffic states - a) a congestion is forming (clumping), the congestion is dispersing (unclumping), or c) the traffic is flowing normally (neutral). We train a spatio-temporal deep network to identify 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 the EoT dataset show that the traffic graphs can help in correctly identifying congestion-prone behavior in different spatial regions of an intersection. © 2020 IEEE.

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IITH Creators:

IITH Creators	ORCiD
Mohan, C Krishna	https://orcid.org/0000-0002-7316-0836

Item Type:

Conference or Workshop Item (Paper)

Additional Information:

ACKNOWLEDGEMENT This work has been conducted as the part of SATREPS project entitled on “Smart Cities for Emerging Countries by Multimodal Transport System based on Sensing, Network and Big Data Analysis of Regional Transportation” (JPMJSA1606) funded by JST and JICA.

Uncontrolled Keywords:

Ahmedabad , india; Driving behavior; Mixed traffic; Spatial regions; Spatio temporal; Traffic behavior; Traffic state; Vehicle density

Subjects:

Computer science

Divisions:

Department of Computer Science & Engineering

Depositing User:

. LibTrainee 2021

Date Deposited:

29 Oct 2022 09:01

Last Modified:

29 Oct 2022 09:01