Abstract
Factors found to be influencing individual travel patterns have been explored in several studies. A number of studies have suggested an association between mobile location data and individual travel patterns. This paper proposes a novel deep learning framework to extract individual travel patterns by using large-scale mobile location data. The proposed framework includes methods for extracting origin and destination points based on spatiotemporal thresholds, matching the origin and destination with traffic analysis zone, and predicting based on natural language processing methods, in which a neural network is constructed to vectorize the traffic area on the basis of spatiotemporal information. A case study is performed using mobile location data involving more than 3 million users in Beijing, and the results show that our proposed framework can effectively identify individual travel patterns. The results of this study can further provide insights into traffic demand identification, bus network optimization, and other related research.
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Acknowledgements
The study was supported by National Key R&D Program of China (2021YFB1600100).
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Yan, Hy., Li, Yj., Liu, Xh., Chen, X., Ma, Xl. (2023). Learning Individual Travel Pattern by Using Large-Scale Mobile Location Data with Deep Learning. In: Wang, W., Wu, J., Jiang, X., Li, R., Zhang, H. (eds) Green Transportation and Low Carbon Mobility Safety. GITSS 2021. Lecture Notes in Electrical Engineering, vol 944. Springer, Singapore. https://doi.org/10.1007/978-981-19-5615-7_19
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DOI: https://doi.org/10.1007/978-981-19-5615-7_19
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