基于多源数据的车流量时空预测方法

doi:10.19562/j.chinasae.qcgc.2021.11.012

Abstract

Abstract:

In order to enhance the accuracy of traffic flow prediction， a traffic flow prediction method based on multi-source data and spatio-temporal prediction is proposed in this paper with comprehensive considerations of the effects of various factors on traffic flow from the perspectives of external features， time features and spatial features. In terms of external features， the influence of the features of date， weather and point of interest on traffic flow is explored in depth. In terms of time features， a time series prediction framework based on time convolution network is put forward， and the time prediction models are established with the neighbor cycle and daily cycle as base lines respectively. In terms of spatial features， a spatial feature extraction method based on graph representation learning is proposed to fulfill the extraction of the spatial correlation feature between adjacent road network nodes. The results show that compared with various existing prediction methods， the method adopted improves the prediction performance of medium- and long-term traffic flow with high prediction accuracy.

Key words: traffic flow, multi-source data, deep learning, spatio-temporal prediction

Jie Hu,Yongsheng Gong,Shijie Cai,Tengfei Huang. A Spatio-Temporal Prediction Method of Traffic Flow Based on Multi-Source Data[J].Automotive Engineering, 2021, 43(11): 1662-1672.

Figures/Tables 22

References 33

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索引	方向	道路ID	时间	卡口ID	车辆ID
0	5	5	2019-09-01 08：18：34	100117	VeeeoP
1	5	2	2019-09-01 08：19：39	100117	BippCK
3	3	3	2019-09-01 08：19：24	100117	WoouoU

出租车ID	经度	纬度	航向角/（°）	时间
Uf26f	120°404’E	36°1897’N	124	2019-08-01 07：35：36
Uf26f	120°407’E	36°1766’N	85	2019-08-01 07：46：44
Uf26f	120°421’E	36°2328’N	44	2019-08-01 07：00：43

时间/h	温度/℃	湿度/%	风速/（km·h^-1）	气压/hPa	天气状况
0	21	73	3.22	1 006.773	晴天
1	20	78	3.22	1 006.773	晴天
2	20	78	3.22	1 006.773	晴天

POI名称	POI类别	行政区	经度	纬度
骨汤麻辣烫	餐饮	市北区	120°3606 ’E	36°131700’N
凯景花园	住宅	市北区	120°3486 ’E	36°087257 ’N
川东印刷公司	公司	市北区	120°3528 ’E	36°132616 ’N

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A Spatio-Temporal Prediction Method of Traffic Flow Based on Multi-Source Data

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