Proactive taxi dispatching is of great importance to balance taxi demand-supply gaps among different locations in a city. Recent advances primarily rely on deep reinforcement learning (DRL) to directly learn the optimal dispatching policy. These works, however, are still not sufficiently efficient because they overlook several pieces of valuable context information. As a result, they may generate quite a few improper actions and introduce unnecessary coordination costs. To improve existing works, we present COX – a context-aware taxi dispatching approach that incorporates rich contexts into DRL modeling for more efficient taxi reallocations. Specifically, rather than simply dividing the service area into grids, COX proposes a road connectivity aware clustering algorithm to divide the road network graph into zones for practical taxi dispatching. In addition, COX comprehensively analyzes zone-level taxi demands and supplies through accurate taxi demand prediction and timely updates of taxi statuses. COX improves the DRL modeling by integrating these derived contexts, e.g., state representation with complete demand/supply data and sequential action generation with full coordination among idle taxis. In particular, we implement an environment simulator to train and evaluate COX using a large real-world taxi dataset. Extensive experiments show that COX outperforms state-of-the-art approaches on various performance metrics, e.g., on average improving the total order values by 6.74%, while reducing the number of unserved taxi orders and passengers’ waiting time by 4.92% and 44.84%, respectively.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Context-Aware Taxi Dispatching at City-Scale Using Deep Reinforcement Learning


    Contributors:
    Liu, Zhidan (author) / Li, Jiangzhou (author) / Wu, Kaishun (author)


    Publication date :

    2022-03-01


    Size :

    6090465 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    DEEP REINFORCEMENT LEARNING-BASED TAXI DISPATCHING METHOD AND SYSTEM

    LIU ZHIDAN / LI JIANGZHOU / WU KAISHUN | European Patent Office | 2021

    Free access


    A Clustering-Based Multi-Agent Reinforcement Learning Framework for Finer-Grained Taxi Dispatching

    Rajeh, Taha M. / Luo, Zhipeng / Javed, Muhammad Hafeez et al. | IEEE | 2024


    A Smart Taxi Dispatching System

    Awang, R.-M. / ITS Congress Association | British Library Conference Proceedings | 2000


    Taxi dispatch control system and method for taxi dispatching

    NAKAMURA KAZUTO | European Patent Office | 2021

    Free access