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TBI2Flow: Travel behavioral inertia based long-term taxi passenger flow prediction

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Abstract

Taxis are one of the representative modes of traffic systems. However, with the emergence of shared cars led by DiDi and Uber in recent years, the traditional taxi companies are facing unprecedented competitions. Without personalized data collected from the mobile devices, passenger flow prediction based on vehicle GPS records presents a unique solution that can improve taxis’ operating efficiency while preserving personal privacy. In this paper, we propose the Travel Behavioral Inertia (TBI) from taxi GPS records, which embodies Driver Inertia (DI) and Passenger Inertia (PI). Then we integrate TBI with other features to construct multi-dimensional features and predict taxi passenger flow based on a deep learning algorithm. We call the entire framework TBI2Flow. Extensive experiments demonstrate that TBI features has outstanding contribution to passenger flow prediction and TBI2Flow outperforms state-of-the-art methods including time series-based method and other deep learning-based methods on long-term taxi passenger flow prediction.

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Notes

  1. This method is not only simple, but also can meet the requirements of most traffic researches. Other methods embody Voronoi tessellation division based on particles and division based on urban road network framework. Researches with special spatial needs can consider the above two complex spatial division methods.

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Acknowledgments

The authors extend their appreciation to the Deanship of Scientific Research at King Saud University for funding this work through research group No. RG- 1439-088. This work was partially supported by the National Natural Science Foundation of China (61572106), the Dalian Science and Technology Innovation Fund (2018J12GX048), and the Fundamental Research Funds for the Central Universities (DUT18JC09).

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Authors and Affiliations

  1. Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province, School of Software, Dalian University of Technology, Dalian, 116620, China

    Xiangjie Kong, Feng Xia & Zhenhuan Fu

  2. Indiana University Network Science Institute, Indiana University Bloomington, Bloomington, IN, 47408, USA

    Xiaoran Yan

  3. Computer Science Department, Community College, King Saud University, Riyadh, 11437, Saudi Arabia

    Amr Tolba & Zafer Almakhadmeh

  4. Faculty of Science, Mathematics and Computer Science Department, Menoufia University, Shebin-El-kom, 32511, Egypt

    Amr Tolba

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  1. Xiangjie Kong
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Correspondence to Feng Xia.

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This article belongs to the Topical Collection: Special Issue on Smart Computing and Cyber Technology for Cyberization

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Kong, X., Xia, F., Fu, Z. et al. TBI2Flow: Travel behavioral inertia based long-term taxi passenger flow prediction. World Wide Web 23, 1381–1405 (2020). https://doi.org/10.1007/s11280-019-00700-1

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