基于GCN和TCN的多因素城市路网出租车需求预测
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长安大学

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TP39

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国家重点研发计划(2020YFB1600400);陕西省重点研发计划(2019ZDLGY03-09-01,2020ZDLGY09-02,2022GY-063)


Multi Factor Taxi Demand Forecasting for Urban Road Network Based on GCN and TCN
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1.Chang'2.'3.an University

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    摘要:

    目前,在巡游模式下,出租车与乘客间供需不易匹配,造成出租车空载和乘客打车难现象并存,准确高效地实现路网出租车需求预测有利于有效缓解这一问题。针对现有交通流预测模型对空间特征提取不充分,特别是对城市路网内路段之间的空间关系没有全面挖掘这一问题,充分考虑了路网内路段间的三种空间关系,对其分别构建路段间的局部关系图、路段全局关系图和路段OD次数关系图。提出了一种由图卷积网络和时间卷积网络相结合构成的出租车需求预测模型。其中,采用图卷积网络对城市路网内路段的空间关系特征进行挖掘,采用时间卷积网络对交通数据集中的时间序列特征进行挖掘,并且考虑了外部因素的影响。实验中,首先从真实出租车GPS轨迹数据中提取城市路网中各个路段的出租车出行量,并利用道路上在多个时隙形成的出行量序列对预测模型进行验证。结果表明,相比其它交通流预测模型所提出的预测模型具有较优的平均绝对误差、均方根误差和平均绝对百分误差。

    Abstract:

    At present, in the cruise mode, the supply and demand between taxis and passengers is not easy to match, resulting in the coexistence of empty taxis and hard hailing phenomenon. Accurate and efficient realization of the road network taxi demand forecast is conducive to effectively alleviate this problem. In the view of that the existing traffic flow prediction models can not sufficiently extract spatial features, especially the spatial relationship between sections in urban road network, full consideration was given to the three kinds of spatial relationship between segments within the road network, and three types of graph were constructed correspondingly, i.e. the local relationship graph, the global relationship graph and the OD frequency relationship graph. This paper proposes a taxi demand prediction model which is composed of graph convolutional network and temporal convolutional network. The graph convolutional network is used to mine the spatial relationship features of sections in the urban road network, and the temporal convolutional network is used to mine the time series features of traffic data, and the influence of external factors is considered. In the experiment, the number of taxi trips in each section of the urban road network is extracted from the real taxi GPS trajectory data, and the series of trips number formed in multiple time slots on the road is used to verify the prediction model. The results show that the proposed model is superior to the common traffic prediction models, and has smaller mean absolute error, root mean square error and mean absolute percentage error.

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  • 收稿日期:2021-05-11
  • 最后修改日期:2022-02-25
  • 录用日期:2022-02-25
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