Understanding the predictability of path flow distribution in urban road networks using an information entropy approach

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Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Date
2024-06
Major/Subject
Mcode
Degree programme
Language
en
Pages
12
Series
Multimodal Transportation, Volume 3, issue 2, pp. 1-12
Abstract
Predicting the distributions of path flow between origin-destination (OD) pairs in an urban road network is crucial for developing efficient traffic control and management strategies. Here, we use the large-scale taxi GPS trajectory data of San Francisco and Shenzhen to study the predictability of path flow distribution in urban road networks. We develop an approach to project the time-varying path flow distributions into a high-dimensional space. In the high-dimensional space, information entropy is used to measure the predictability of path flow distribution. We find that the distributions of path flow between OD pairs are in general characterized with a high predictability. In addition, we analyze the factors affecting the predictability of path flow distribution. Finally, an n-gram model incorporating high-order gram and low-order gram is proposed to predict the distribution of path flow. A relatively high prediction accuracy is achieved.
Description
Publisher Copyright: © 2024
Keywords
Human mobility, Information entropy, Path flow distribution, Predictability
Other note
Citation
Guo, B, Huang, Z, Zheng, Z, Zhang, F & Wang, P 2024, ' Understanding the predictability of path flow distribution in urban road networks using an information entropy approach ', Multimodal Transportation, vol. 3, no. 2, 100135, pp. 1-12 . https://doi.org/10.1016/j.multra.2024.100135