Traffic State Estimation Based on Kalman Filter Technique using Connected Vehicle V2V Basic Safety Messages

Rongsheng Chen; Michael W. Levin

doi:10.1109/ITSC.2019.8917343

Traffic State Estimation Based on Kalman Filter Technique using Connected Vehicle V2V Basic Safety Messages

Rongsheng Chen, Michael W. Levin

Civil, Environmental, and Geo- Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

6 Scopus citations

Abstract

In the near future, vehicles will be equipped to receive and broadcast Basic Safety Messages (BSMs), which includes the vehicle position, speed, heading, and acceleration, to effectively avoid potential road collisions. This data with high resolution can be used to provide road information for traffic operation and management. This study proposed an algorithm using BSM data to estimate traffic states, including flow, density, and speed, based on the Kalman Filter and cell transmission model (CTM). The algorithm was tested using vehicle trajectory data generated by a CTM-based simulator. The result showed that the algorithm performed well with known parameters and had poor performance when parameter values were unknown, and the parameters were hard to be calibrated with the data from the CTM-based simulator.

Original language	English (US)
Title of host publication	2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	4380-4385
Number of pages	6
ISBN (Electronic)	9781538670248
DOIs	https://doi.org/10.1109/ITSC.2019.8917343
State	Published - Oct 2019
Event	2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019 - Auckland, New Zealand Duration: Oct 27 2019 → Oct 30 2019

Publication series

Name	2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019

Conference

Conference	2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019
Country/Territory	New Zealand
City	Auckland
Period	10/27/19 → 10/30/19

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/ITSC.2019.8917343

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Chen, R., & Levin, M. W. (2019). Traffic State Estimation Based on Kalman Filter Technique using Connected Vehicle V2V Basic Safety Messages. In 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019 (pp. 4380-4385). Article 8917343 (2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ITSC.2019.8917343

Traffic State Estimation Based on Kalman Filter Technique using Connected Vehicle V2V Basic Safety Messages. / Chen, Rongsheng; Levin, Michael W.
2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 4380-4385 8917343 (2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Chen, R & Levin, MW 2019, Traffic State Estimation Based on Kalman Filter Technique using Connected Vehicle V2V Basic Safety Messages. in 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019., 8917343, 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019, Institute of Electrical and Electronics Engineers Inc., pp. 4380-4385, 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019, Auckland, New Zealand, 10/27/19. https://doi.org/10.1109/ITSC.2019.8917343

Chen R, Levin MW. Traffic State Estimation Based on Kalman Filter Technique using Connected Vehicle V2V Basic Safety Messages. In 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 4380-4385. 8917343. (2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019). doi: 10.1109/ITSC.2019.8917343

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Traffic State Estimation Based on Kalman Filter Technique using Connected Vehicle V2V Basic Safety Messages

Abstract

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Bibliographical note

UN SDGs

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