CNN-based bus trip prediction method for the elderly in plateau cities

ChangLiang He; JiaYao Wang; JinFeng Xiao

doi:10.1117/12.2645595

23 November 2022 CNN-based bus trip prediction method for the elderly in plateau cities

ChangLiang He, JiaYao Wang, JinFeng Xiao

Proceedings Volume 12302, Seventh International Conference on Electromechanical Control Technology and Transportation (ICECTT 2022); 123023Y (2022) https://doi.org/10.1117/12.2645595
Event: Seventh International Conference on Electromechanical Control Technology and Transportation (ICECTT 2022), 2022, Guangzhou, China

Abstract

Accurate traffic trip prediction is an important guarantee for achieving long-term development of intelligent transportation. To cope with the complex and variable factors of elderly people's public transport travel decision-making behavior as well as to improve elderly people's travel, this paper proposes a deep learning-based convolutional neural network for predicting urban elderly people's public transport travel. Taking the plateau city of Lhasa as an example, the data from October 2018-December 2019 of elderly people's bus passes are selected for deep convolutional neural network and regression model modelling respectively, and the results obtained are compared through the model for prediction experiments on January 2020 data. The experimental results show that the mean absolute error (MAE) as well as the root mean square error (RMSE) are reduced by 0.112 and 0.156 compared with the traditional regression prediction model, further indicating that the model has higher prediction accuracy, and is reliable and practical in predicting elderly people's public transport trips.

Citation Download Citation

ChangLiang He, JiaYao Wang, and JinFeng Xiao "CNN-based bus trip prediction method for the elderly in plateau cities", Proc. SPIE 12302, Seventh International Conference on Electromechanical Control Technology and Transportation (ICECTT 2022), 123023Y (23 November 2022); https://doi.org/10.1117/12.2645595

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