Deep spatial-temporal travel time prediction model based on trajectory feature

doi:10.1016/j.compeleceng.2023.108868

	Deep spatial-temporal travel time prediction model based on trajectory feature
	Sheng, Zhaoyu 1,2; Lv, Zhiqiang 1,2,3; Li, Jianbo 1; Xu, Zhihao 1,2
	2023-09-01
发表期刊	COMPUTERS & ELECTRICAL ENGINEERING
ISSN	0045-7906
卷号	110 页码:12
摘要	Research on travel time prediction shows its importance in the rational planning of travel arrangements and traffic congestion mitigation. The scale of taxi and online ride-hailing users is huge, and accurate travel time prediction is convenient for passengers to reasonably arrange travel planning. Unlike highways and buses, the trajectory of taxis is complex. At the same time, the travel time prediction of the taxi is affected by many factors. Existing models lack effective trajectory feature. They have low accuracy in predicting travel time in urban areas. This will affect the travel arrangement of passengers. In this paper, we propose a trajectory feature learning method based on the image processing method and time series prediction. Traffic congestion is quantified as the congestion value, and a variety of external factors are considered. The experimental results show that this model has advantages over some classical models in predicting travel time.
关键词	Travel time prediction Trajectory feature Traffic condition Spatial correlation Temporal dependence
DOI	10.1016/j.compeleceng.2023.108868
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Plan Key Special Projects, China[2018YFB2100303] ; Shandong Province Colleges and Universities Youth Innovation Technology Plan Innovation Team project, China[2020KJN011] ; Shandong Provincial Natural Science Foundation, China[ZR2020MF060] ; Program for Innovative Postdoctoral Talents in Shandong Province, China[40618030001] ; National Natural Science Foundation of China[61802216] ; Postdoctoral Science Foundation of China[2018M642613]
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Hardware & Architecture ; Computer Science, Interdisciplinary Applications ; Engineering, Electrical & Electronic
WOS记录号	WOS:001046097400001
出版者	PERGAMON-ELSEVIER SCIENCE LTD
引用统计	被引频次：3[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/21354
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Li, Jianbo
作者单位	1.Qingdao Univ, Coll Comp Sci & Technol, Ningxia Rd, Qingdao 266071, Shandong, Peoples R China 2.Inst Ubiquitous Networks & Urban Comp, Ningxia Rd, Qingdao 266071, Shandong, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, Haidian Rd, Beijing 100086, Peoples R China
推荐引用方式 GB/T 7714	Sheng, Zhaoyu,Lv, Zhiqiang,Li, Jianbo,et al. Deep spatial-temporal travel time prediction model based on trajectory feature[J]. COMPUTERS & ELECTRICAL ENGINEERING,2023,110:12.
APA	Sheng, Zhaoyu,Lv, Zhiqiang,Li, Jianbo,&Xu, Zhihao.(2023).Deep spatial-temporal travel time prediction model based on trajectory feature.COMPUTERS & ELECTRICAL ENGINEERING,110,12.
MLA	Sheng, Zhaoyu,et al."Deep spatial-temporal travel time prediction model based on trajectory feature".COMPUTERS & ELECTRICAL ENGINEERING 110(2023):12.