Graph Multi-Attention Network-based Taxi Demand Prediction

Haifan Tang, Youkai Wu, Zhaoxia Guo
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Abstract

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.
基于多关注网络的出租车需求预测
在大多数城市,出租车是城市交通系统的重要组成部分。准确的出租车需求预测可以有效减少乘客的等待时间,缩短司机的空载行程,有助于缓解交通拥堵,提高交通效率。由于交通系统的复杂性和路网区域间的时空依赖性,传统的预测方法无法有效预测不同区域的出租车需求。为了更好地处理出租车需求预测问题,本文引入了一种图多注意网络(GMAN),它旨在预测未来一段时间内道路网络中所有区域的出租车需求。基于来自真实城市道路网络的出租车需求的大规模数据集,验证了GMAN的有效性。实验结果表明,GMAN优于5种常用的基准测试模型,其中包括3种最先进的机器学习模型。
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