Short term OD prediction of urban rail transit based on vector autoregression

Lin Zhou; Shengyong Yao; Shuning Li; Fei Xue

doi:10.1117/12.3050919

20 January 2025 Short term OD prediction of urban rail transit based on vector autoregression

Lin Zhou, Shengyong Yao, Shuning Li, Fei Xue

Author Affiliations +

Proceedings Volume 13422, Fourth International Conference on Intelligent Traffic Systems and Smart City (ITSSC 2024); 134221B (2025) https://doi.org/10.1117/12.3050919
Event: Fourth International Conference on Intelligent Traffic Systems and Smart City (ITSSC 2024), 2024, Xi'an, China

Abstract

This study focuses on the importance of spatiotemporal correlation when predicting the traffic status of urban expressways in the short term. Firstly, by analyzing the data of detectors in the upstream and downstream of the expressway, we can understand the changes in traffic flow and speed, and use an ordered sample optimal segmentation algorithm to divide the day into different time periods in order to identify the time periods when the traffic status is relatively stable. Subsequently, a spatial vector autoregressive model considering the impact of upstream and downstream road segments was established to predict traffic flow and speed at target locations at different time periods. The research results indicate that this is particularly true during peak hours, where the traffic status of downstream sections has a significant impact on the upstream. During peak hours, the traffic status of the target location is mainly determined by the upstream section, while the impact of the downstream section is relatively small. Therefore, in response to this situation, sufficient prediction accuracy can be achieved by solely utilizing the traffic status of the upstream section. In addition, compared with traditional ARIMA prediction models and historical mean prediction models, the VAR model proposed in this study can more comprehensively consider the spatiotemporal dynamic characteristics of traffic status, thereby more accurately predicting the traffic status of urban expressways.

(2025) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Lin Zhou, Shengyong Yao, Shuning Li, and Fei Xue "Short term OD prediction of urban rail transit based on vector autoregression", Proc. SPIE 13422, Fourth International Conference on Intelligent Traffic Systems and Smart City (ITSSC 2024), 134221B (20 January 2025); https://doi.org/10.1117/12.3050919

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KEYWORDS

Data modeling

Autoregressive models

Sensors

Systems modeling

Roads

Transportation

Data analysis

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