Abstract
Arrival time is a key aspect of passenger information systems. Provision of accurate bus arrival information is essential for delivering an attractive service and necessary to passengers for reducing their waiting time and bus stops and choosing alternative routes. Recently, the same information is used in smart-phone trip planners. In this paper, we explore an LSTM neural network model for bus arrival time prediction. We take into account heterogeneous information about the transport situation, directly or indirectly affecting the prediction travel time. We evaluate the proposed models with bus operation data from Samara, Russia. Evaluation results show that the proposed model outperforms some typical prediction algorithms.
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Acknowledgments
The work was supported by the Ministry of Science and Higher Education of the Russian Federation (unique project identifier RFMEFI57518X0177).
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Agafonov, A., Yumaganov, A. (2019). Bus Arrival Time Prediction with LSTM Neural Network. In: Lu, H., Tang, H., Wang, Z. (eds) Advances in Neural Networks – ISNN 2019. ISNN 2019. Lecture Notes in Computer Science(), vol 11554. Springer, Cham. https://doi.org/10.1007/978-3-030-22796-8_2
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DOI: https://doi.org/10.1007/978-3-030-22796-8_2
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