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Travel time prediction based on route links’ similarity

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Abstract

Accurate travel time prediction allows passengers to schedule their journeys efficiently. However, cyclical factors (time intervals of the day, weather conditions, and holidays), unpredictable factors (incidents, abnormal weather), and other complicated factors (dynamic traffic conditions, dwell times, and variation in travel demand) make accurate bus travel time prediction complicated. This paper aims to achieve accurate travel time prediction. To do so, we propose a clustering method that identifies travel time paradigms of different route links and clusters them based on their similarity using the nonnegative matrix factorization algorithm. Additionally, we propose a deep learning model based on CNN with spatial–temporal attention and gating mechanisms to select the most relevant features and capture their dependencies and correlations. For each defined cluster, we train a separate model to predict the travel time at various time intervals over the day. As a result, the travel times of all journey links from related prediction models are aggregated to predict the total journey time. Extensive experiments using data collected from four different bus lines in Beijing show that our method outperforms the compared baselines.

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Data availability

The data for bus line 4A in Copenhagen are publicly available at: https://github.com/niklascp/bus-arrival-convlstm.. The data for bus lines 1, 2, 3, and 4 were collected by Beijing Public Transport Group and are available from the corresponding author upon reasonable request and with Beijing Public Transport Group’s permission.

Notes

  1. http://home.ku.edu.tr/~moolibrary/.

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Acknowledgement

This work is supported in part by the National Key Research and Development Program of China (no. 2021ZD0112400), and also in part by the National Natural Science Foundation of China under grant 62276044.

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Authors and Affiliations

  1. School of Computer Science and Technology, Dalian University of Technology, Dalian, 116024, China

    Khaled Alkilane, M. Tag Elsir Alfateh & Shen Yanming

  2. Key Laboratory of Intelligent Control and Optimization for Industrial Equipment, Ministry of Education, Dalian University of Technology, Dalian, China

    Shen Yanming

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  1. Khaled Alkilane
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Correspondence to Shen Yanming.

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Alkilane, K., Alfateh, M.T.E. & Yanming, S. Travel time prediction based on route links’ similarity. Neural Comput & Applic 35, 3991–4007 (2023). https://doi.org/10.1007/s00521-022-07926-7

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