A comparative study on filtering methods for online freeway traffic estimation using heterogeneous data

Xuan Sy Trinh; Dong Ngoduy; Mehdi Keyvan-Ekbatani; Blair Robertson

doi:10.1109/ITSC.2019.8917363

A comparative study on filtering methods for online freeway traffic estimation using heterogeneous data

Xuan Sy Trinh, Dong Ngoduy, Mehdi Keyvan-Ekbatani, Blair Robertson

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Other

6 Citations (Scopus)

Abstract

The paper addresses the problem of fusing multiple data sources in freeway networks. The incremental Unscented Kalman Filter (UKF) and the Unscented Information Filter (UIF) are developed based on cell-transmission model (CTM) of freeway traffic. The efficiency of the aforementioned methods are compared by applying to a toy network with the synthetic data obtained from microscopic traffic simulation. The results show that both are capable of fusing data with sufficient accuracy, even when only a small fraction of traffic information is provided in the data. However, the UKF works better in the case of correct noise covariances while the UIF has better performance when the covariances are incorrect.

Original language	English
Title of host publication	2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	4386-4391
Number of pages	6
ISBN (Electronic)	9781538670248
DOIs	https://doi.org/10.1109/ITSC.2019.8917363
Publication status	Published - Oct 2019
Externally published	Yes
Event	IEEE Conference on Intelligent Transportation Systems 2019 - Auckland, New Zealand Duration: 27 Oct 2019 → 30 Oct 2019 Conference number: 22nd https://ieeexplore.ieee.org/xpl/conhome/8907344/proceeding (Proceedings)

Publication series

Name	2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019

Conference

Conference	IEEE Conference on Intelligent Transportation Systems 2019
Abbreviated title	ITSC 2019
Country/Territory	New Zealand
City	Auckland
Period	27/10/19 → 30/10/19
Internet address	https://ieeexplore.ieee.org/xpl/conhome/8907344/proceeding (Proceedings)

Access to Document

10.1109/ITSC.2019.8917363

Cite this

Trinh, X. S., Ngoduy, D., Keyvan-Ekbatani, M., & Robertson, B. (2019). A comparative study on filtering methods for online freeway traffic estimation using heterogeneous data. In 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019 (pp. 4386-4391). (2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ITSC.2019.8917363

Trinh, Xuan Sy ; Ngoduy, Dong ; Keyvan-Ekbatani, Mehdi et al. / A comparative study on filtering methods for online freeway traffic estimation using heterogeneous data. 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019. IEEE, Institute of Electrical and Electronics Engineers, 2019. pp. 4386-4391 (2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019).

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title = "A comparative study on filtering methods for online freeway traffic estimation using heterogeneous data",

abstract = "The paper addresses the problem of fusing multiple data sources in freeway networks. The incremental Unscented Kalman Filter (UKF) and the Unscented Information Filter (UIF) are developed based on cell-transmission model (CTM) of freeway traffic. The efficiency of the aforementioned methods are compared by applying to a toy network with the synthetic data obtained from microscopic traffic simulation. The results show that both are capable of fusing data with sufficient accuracy, even when only a small fraction of traffic information is provided in the data. However, the UKF works better in the case of correct noise covariances while the UIF has better performance when the covariances are incorrect.",

author = "Trinh, {Xuan Sy} and Dong Ngoduy and Mehdi Keyvan-Ekbatani and Blair Robertson",

year = "2019",

month = oct,

doi = "10.1109/ITSC.2019.8917363",

language = "English",

series = "2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019",

publisher = "IEEE, Institute of Electrical and Electronics Engineers",

pages = "4386--4391",

booktitle = "2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019",

address = "United States of America",

note = "IEEE Conference on Intelligent Transportation Systems 2019, ITSC 2019 ; Conference date: 27-10-2019 Through 30-10-2019",

url = "https://ieeexplore.ieee.org/xpl/conhome/8907344/proceeding",

}

Trinh, XS, Ngoduy, D, Keyvan-Ekbatani, M & Robertson, B 2019, A comparative study on filtering methods for online freeway traffic estimation using heterogeneous data. in 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019. 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019, IEEE, Institute of Electrical and Electronics Engineers, pp. 4386-4391, IEEE Conference on Intelligent Transportation Systems 2019, Auckland, New Zealand, 27/10/19. https://doi.org/10.1109/ITSC.2019.8917363

A comparative study on filtering methods for online freeway traffic estimation using heterogeneous data. / Trinh, Xuan Sy; Ngoduy, Dong; Keyvan-Ekbatani, Mehdi et al.
2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019. IEEE, Institute of Electrical and Electronics Engineers, 2019. p. 4386-4391 (2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Other

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AB - The paper addresses the problem of fusing multiple data sources in freeway networks. The incremental Unscented Kalman Filter (UKF) and the Unscented Information Filter (UIF) are developed based on cell-transmission model (CTM) of freeway traffic. The efficiency of the aforementioned methods are compared by applying to a toy network with the synthetic data obtained from microscopic traffic simulation. The results show that both are capable of fusing data with sufficient accuracy, even when only a small fraction of traffic information is provided in the data. However, the UKF works better in the case of correct noise covariances while the UIF has better performance when the covariances are incorrect.

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Trinh XS, Ngoduy D, Keyvan-Ekbatani M, Robertson B. A comparative study on filtering methods for online freeway traffic estimation using heterogeneous data. In 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019. IEEE, Institute of Electrical and Electronics Engineers. 2019. p. 4386-4391. (2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019). doi: 10.1109/ITSC.2019.8917363

A comparative study on filtering methods for online freeway traffic estimation using heterogeneous data

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