Freeway Traffic State Estimation Method Based on Multisource Data
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 148, Issue 4
Abstract
Accurate traffic state estimation is essential for the successful application of intelligent transportation systems (ITS). In the past, traffic state estimation methods based on the macro traffic flow model and data assimilation technology have been widely developed. Based on the data collected from video image detectors and the freeway charging system, this paper proposed a dynamic method to estimate the traffic state at an arbitrary cross section of the freeway. Firstly, the original static method was briefly described, including the vehicle average speed calculation, travel time estimation on road segments, and allocation of vehicle travel time. Then, congestion analysis and dynamic vehicle travel time allocation were introduced to compensate for the inapplicability of the static method to the change of the traffic state. Finally, the traffic volume at an arbitrary cross section in any period was directly derived. The proposed multisource data-based dynamic method was validated by real data and tested on different days. The results showed that the proposed dynamic method outperformed the original static method in traffic state estimation, especially in the case of congestion. In addition, the effect of setting different time intervals on the results was analyzed, and the analysis results suggested that the performance of the proposed method can be significantly improved when the time interval is set to 5 min.
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Data Availability Statement
All data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This work was supported by the Fundamental Research Funds for the Central Universities (Grant Nos. 2021YJS087, 2021PT206), and the National Natural Science Foundation of China (Grant Nos. 71621001, 71771021, 71931002, 71971015, 72101022).
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Journal of Transportation Engineering, Part A: Systems
Volume 148 • Issue 4 • April 2022
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© 2022 American Society of Civil Engineers.
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Received: Aug 5, 2021
Accepted: Dec 9, 2021
Published online: Jan 27, 2022
Published in print: Apr 1, 2022
Discussion open until: Jun 27, 2022
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Cited by
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