Learning Traffic Speed Dynamics from Visualizations

Bilal Thonnam Thodi, Zaid Saeed Khan, Saif Eddin Jabari, Monica Menendez

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Space-time visualizations of macroscopic or microscopic traffic variables is a qualitative tool used by traffic engineers to understand and analyze different aspects of road traffic dynamics. We present a deep learning method to learn the macroscopic traffic speed dynamics from these space-time visualizations, and demonstrate its application in the framework of traffic state estimation. Compared to existing estimation approaches, our approach allows a finer estimation resolution, eliminates the dependence on the initial conditions, and is agnostic to external factors such as traffic demand, road inhomogeneities and driving behaviors. Our model respects causality in traffic dynamics, which improves the robustness of estimation. We present the high-resolution traffic speed fields estimated for several freeway sections using the data obtained from the Next Generation Simulation Program (NGSIM) and German Highway (HighD) datasets. We further demonstrate the quality and utility of the estimation by inferring vehicle trajectories from the estimated speed fields, and discuss the benefits of deep neural network models in approximating the traffic dynamics.

Original languageEnglish (US)
Title of host publication2021 IEEE International Intelligent Transportation Systems Conference, ITSC 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781728191423
StatePublished - Sep 19 2021
Event2021 IEEE International Intelligent Transportation Systems Conference, ITSC 2021 - Indianapolis, United States
Duration: Sep 19 2021Sep 22 2021

Publication series

NameIEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC


Conference2021 IEEE International Intelligent Transportation Systems Conference, ITSC 2021
Country/TerritoryUnited States

ASJC Scopus subject areas

  • Automotive Engineering
  • Mechanical Engineering
  • Computer Science Applications


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