Accurate taxi demand-supply forecasting is a challenging application of ITS (Intelligent Transportation Systems), due to the complex spatial and temporal patterns involved. We investigate the impact of different spatial partitioning techniques on the prediction performance of an LSTM (Long Short-Term Memory) network, in the context of taxi demand-supply forecasting. We consider two tessellation schemes: (i) the variable-sized Voronoi tessellation, and (ii) the fixed-sized Geohash tessellation. While the widely employed ConvLSTM (Convolutional LSTM) method can model fixed-sized Geohash partitions, the standard convolutional filters cannot be applied on variable-sized Voronoi partitions. To explore the impact of the Voronoi strategy, we propose the use of a GraphLSTM (Graph-based LSTM) model, by representing the Voronoi spatial partitions as nodes on an arbitrarily structured graph. The GraphLSTM model offers competitive performance against the ConvLSTM model, at a lower computational complexity, across three real-world large-scale taxi demand-supply data sets, with different performance metrics. To ensure superior performance across diverse settings, a HEDGE based ensemble learning algorithm is applied over the ConvLSTM and the GraphLSTM networks.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Grids Versus Graphs: Partitioning Space for Improved Taxi Demand-Supply Forecasts


    Beteiligte:
    Davis, Neema (Autor:in) / Raina, Gaurav (Autor:in) / Jagannathan, Krishna (Autor:in)


    Erscheinungsdatum :

    01.10.2021


    Format / Umfang :

    1883084 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Multiagent Reinforcement Learning-Based Taxi Predispatching Model to Balance Taxi Supply and Demand

    Yongjian Yang / Xintao Wang / Yuanbo Xu et al. | DOAJ | 2020

    Freier Zugriff

    Demand, Supply, and Performance of Street-Hail Taxi

    Zhang, Ruda / Ghanem, Roger | IEEE | 2020


    Taxi supply and demand imbalance road section detection method

    GUO YIWEN / ZHOU HUIDAN / TANG JIANBO et al. | Europäisches Patentamt | 2024

    Freier Zugriff

    Analysis of taxi demand and supply in New York City: implications of recent taxi regulations

    Kamga, Camille / Yazici, M. Anil / Singhal, Abhishek | Taylor & Francis Verlag | 2015