Taxi is an important component of the urban transport system in most cities. Accurate taxi demand prediction can effectively reduce the waiting time of passengers and shorten the no-load travel of drivers, which is helpful in alleviating traffic congestion and improving traffic efficiency. Due to the complexity of the traffic system and spatiotemporal dependencies among regions in a road network, traditional prediction methods cannot predict taxi demands of different regions effectively. This paper introduces a Graph Multi-Attention Network (GMAN) to handle the taxi demand prediction problem with better performance, which aims to predict the taxi demands in all regions of a road network in the next time period. The effectiveness of the GMAN is validated based on a large-scale dataset of taxi demands from a real urban road network. Experimental results show that the GMAN outperforms 5 commonly used benchmarking models, including 3 state-of-the-art machine learning models.
Graph Multi-Attention Network-based Taxi Demand Prediction
2022-10-28
2230627 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Urban Taxi Demand Forecast Based on Graph Convolutional Network
Springer Verlag | 2022
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