Paper
20 February 2024 Prediction model for expressway traffic flow of regional central cities based on time-segments
Ruixin Zhu
Author Affiliations +
Proceedings Volume 13064, Seventh International Conference on Traffic Engineering and Transportation System (ICTETS 2023); 130643V (2024) https://doi.org/10.1117/12.3016110
Event: 7th International Conference on Traffic Engineering and Transportation System (ICTETS 2023), 2023, Dalian, China
Abstract
Accurate traffic flow prediction can effectively alleviate the traffic pressure on expressways. Compared with general cities, the traffic flow on expressways in regional central cities is highly unbalanced, which makes accurate prediction of traffic flow very difficult. For this issue, we observed that traffic flow mainly varies greatly in different time periods, and the proportion of high flow time periods is relatively small. The prediction model will treat high flow data as outliers, resulting in poor prediction performance. Therefore, this paper proposes a time segment based expressway traffic flow prediction model, which maps the high and low traffic flows to different time segments. Divide the training dataset into multiple time segments, and then train the model separately based on the partitioned dataset. The experiment shows that the expressway traffic flow prediction model based on the method proposed in this paper has better performance than similar models.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Ruixin Zhu "Prediction model for expressway traffic flow of regional central cities based on time-segments", Proc. SPIE 13064, Seventh International Conference on Traffic Engineering and Transportation System (ICTETS 2023), 130643V (20 February 2024); https://doi.org/10.1117/12.3016110
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KEYWORDS
Education and training

Performance modeling

Data modeling

Deep learning

Machine learning

Autoregressive models

Humidity

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