Predicting citywide passenger demand via reinforcement learning from spatio-temporal dynamics

Xiaodong Ning, Lina Yao, Xianzhi Wanga, Boualem Benatallah , Flora Salim, Pari Delir Haghighi

    Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

    Abstract

    The global urbanization imposes unprecedented pressure on urban infrastructure and public resources. The population explosion has made it challenging to satisfy the daily needs of urban residents. 'Smart City' is a solution that utilizes different types of data collection sensors to help manage assets and resources intelligently and more efficiently. Under the Smart City umbrella, the primary research initiative in improving the efficiency of car-hailing services is to predict the citywide passenger demand to address the imbalance between the demand and supply. However, predicting the passenger demand requires analysis on various data such as historical passenger demand, crowd outflow, and weather information, and it remains challenging to discover the latent relationships among these data. To address this challenge, we propose to improve the passenger demand prediction via learning the salient spatial-temporal dynamics within a reinforcement learning framework. Our model employs an information selection mechanism to focus on the most distinctive data in historical observations. This mechanism can automatically adjust the information zone according to the prediction performance to find the optimal choice. It also ensures the prediction model to take full advantage of the available data by introducing the positive and excluding the negative correlations. We have conducted experiments on a large-scale real-world dataset that covers 1.5 million people in a major city in China. The results show our model outperforms state-of-the-art and a series of baselines by a large margin.
    Original languageEnglish
    Title of host publicationProceedings of the 15th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services
    Subtitle of host publication5-7 November 2018, New York City, NY, United States - Mobiquitous 2018
    EditorsCristian Borcea, Shiwen Mao, Jian Tang
    Place of PublicationNew York NY USA
    PublisherAssociation for Computing Machinery (ACM)
    Pages19-28
    Number of pages10
    ISBN (Electronic)9781450360937
    DOIs
    Publication statusPublished - 2018
    EventInternational Conference on Mobile and Ubiquitous Systems: Networks and Services 2018 - New York, United States of America
    Duration: 5 Nov 20187 Nov 2018
    Conference number: 15th
    http://mobiquitous2018.eai-conferences.org/

    Conference

    ConferenceInternational Conference on Mobile and Ubiquitous Systems: Networks and Services 2018
    Abbreviated titleMobiQuitous 2018
    CountryUnited States of America
    CityNew York
    Period5/11/187/11/18
    Internet address

    Cite this

    Ning, X., Yao, L., Wanga, X., Benatallah , B., Salim, F., & Delir Haghighi, P. (2018). Predicting citywide passenger demand via reinforcement learning from spatio-temporal dynamics. In C. Borcea, S. Mao, & J. Tang (Eds.), Proceedings of the 15th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services: 5-7 November 2018, New York City, NY, United States - Mobiquitous 2018 (pp. 19-28). Association for Computing Machinery (ACM). https://doi.org/10.1145/3286978.3286991