Rainy-day travel time prediction based on Kalman filter algorithm

Hao Li; Zhitao Chen; Yanming Lu

doi:10.1117/12.3015852

20 February 2024 Rainy-day travel time prediction based on Kalman filter algorithm

Hao Li, Zhitao Chen, Yanming Lu

Proceedings Volume 13064, Seventh International Conference on Traffic Engineering and Transportation System (ICTETS 2023); 1306436 (2024) https://doi.org/10.1117/12.3015852
Event: 7th International Conference on Traffic Engineering and Transportation System (ICTETS 2023), 2023, Dalian, China

Abstract

Precipitation creates many uncertainties in travel times on urban road networks. Travel time prediction involves a multiparameter non-linear mapping relationship, so artificial neural networks are widely used in prediction. Artificial Intelligence This paper designs a travel time calculation method based on non-minimum sections, integrating floating car data and precipitation data. Accordingly, we propose a travel time prediction model based on Kalman Filter and design its effective algorithm. Finally, we use ten-day GPS data gathered from taxis in Zhongguancun West district in Beijing to verify the effectiveness of our proposed approach. These results show that the accuracy of this prediction algorithm in guaranteeing a prediction accuracy of more than 90% is 0.75 and the improved Kalman filter travel time prediction has higher accuracy than traditional.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Hao Li, Zhitao Chen, and Yanming Lu "Rainy-day travel time prediction based on Kalman filter algorithm", Proc. SPIE 13064, Seventh International Conference on Traffic Engineering and Transportation System (ICTETS 2023), 1306436 (20 February 2024); https://doi.org/10.1117/12.3015852

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