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CMT-Net: A Mutual Transition Aware Framework for Taxicab Pick-ups and Drop-offs Co-Prediction

Published: 15 February 2022 Publication History

Abstract

With increasing population of modern cities, accurate estimation of regional passenger demands is critical to online taxicab services as such platforms aim at a reformation of taxicab scheduling for a more efficient order dispatching. Though great efforts have been made on passenger demand predictions, existing works still have the following shortcomings: i) they mostly performed based on uniform grid partition, which results in the imbalance of demand volumes among regions and even non-vehicle regions in such partition, ii) none of previous demand forecasting efforts have highlighted the important mutual influences between pick-ups and drop-offs, which are of great significance for taxicab scheduling. To this end, we first devise a multi-kernel based clustering to achieve a taxicab-behavior and geographic-aware sub-region partition, hence a more balanced and compact regional division is obtained. Subsequently, we emphasize the essential factors with regard to mutual transition quantification in taxicab predictions, then propose a Transfer-LSTM and an Origin-Destination-based transition matrix to respectively capture the drop-to-pick and pick-to-drop spatiotemporal transition patterns. Hence, a novel mutual-transition-aware co-prediction framework is devised by capturing complex spatiotemporal interactions between pick-ups and drop-offs. Extensive experiments on two real-world taxicab datasets demonstrate our co-prediction framework is superior to state-of-the-art methods, thus providing novel perspectives to urban human mobility understanding and transition-based taxicab scheduling.

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      cover image ACM Conferences
      WSDM '22: Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining
      February 2022
      1690 pages
      ISBN:9781450391320
      DOI:10.1145/3488560
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      Published: 15 February 2022

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      • National Natural Science Foundation of China
      • Project of Stable Support for Youth Team in Basic Research Field, CAS
      • Zhejiang Lab's International Talent Fund for Young Professionals
      • Anhui Science Foundation for Distinguished Young Scholars
      • Jiangsu Natural Science Foundation

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      • (2024)Make bricks with a little strawProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence10.24963/ijcai.2024/264(2388-2396)Online publication date: 3-Aug-2024
      • (2024)Modeling Spatio-Temporal Mobility Across Data Silos via Personalized Federated LearningIEEE Transactions on Mobile Computing10.1109/TMC.2024.345365723:12(15289-15306)Online publication date: Dec-2024
      • (2024)Predicting Collective Human Mobility via Countering Spatiotemporal HeterogeneityIEEE Transactions on Mobile Computing10.1109/TMC.2023.329650123:5(4723-4738)Online publication date: May-2024
      • (2024)Spatial-Temporal Correlation Learning for Traffic Demand PredictionIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.344334125:11(15745-15758)Online publication date: Nov-2024
      • (2024)Adaptive and Interactive Multi-Level Spatio-Temporal Network for Traffic ForecastingIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.339297525:10(14070-14086)Online publication date: Oct-2024
      • (2024)Meta Koopman decomposition for time series forecasting under temporal distribution shiftsAdvanced Engineering Informatics10.1016/j.aei.2024.10284062(102840)Online publication date: Oct-2024
      • (2024)A Multi-Scale Residual Graph Convolution Network with hierarchical attention for predicting traffic flow in urban mobilityComplex & Intelligent Systems10.1007/s40747-023-01324-910:3(3305-3317)Online publication date: 29-Jan-2024
      • (2024)Learning dynamic and multi-scale graph structure for traffic demand predictionInternational Journal of Machine Learning and Cybernetics10.1007/s13042-024-02442-7Online publication date: 26-Nov-2024
      • (2023)Pattern Expansion and Consolidation on Evolving Graphs for Continual Traffic PredictionProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599463(2223-2232)Online publication date: 6-Aug-2023
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