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Sep 8, 2022
Urban Traffic Dynamic OD Prediction Based on Multi-Source Data
Authors: Qianqian Ye [email protected], Zhaoliang Li [email protected], Junyi Wu [email protected], and Lijing Cheng [email protected]Author Affiliations
Publication: CICTP 2022
ABSTRACT
Urban traffic OD prediction has always been a hot research topic in the field of transportation. However, most of the existing OD prediction researches are under normal conditions, without considering the influence of holidays, temperatures, weather, and other factors. This paper proposes an urban traffic OD prediction model based on multi-source data. Firstly, traffic modes are divided based on travel trajectory, speed, acceleration and other factors, and OD data within a certain time granularity are extracted. The OD pairs integrating multiple factors is predicted based on long short-term memory (LSTM) networks. By comparing with the model without using multi-source data, the results show that the LSTM model with multiple factors has higher prediction accuracy and is a better prediction method.
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Published online: Sep 8, 2022
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1Faculty of Transportation Engineering, Southeast Univ., Nanjing, China. Email: [email protected]
2Faculty of Transportation Engineering, Southeast Univ., Nanjing, China. Email: [email protected]
3Faculty of Transportation Engineering, National Univ. of Singapore, Kent Ridge, Singapore. Email: [email protected]
4Faculty of Transportation Engineering, Southeast Univ., Nanjing, China. Email: [email protected]
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Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.