大数据 ›› 2021, Vol. 7 ›› Issue (1): 107-123.doi: 10.11959/j.issn.2096-0271.2021008

• 研究 • 上一篇    

基于城市交通监控大数据的行程时间估计

李文明1, 刘芳1, 吕鹏1, 于彦伟2   

  1. 1 烟台大学计算机与控制工程学院,山东 烟台 264005
    2 中国海洋大学计算机科学与技术系,山东 青岛 266100
  • 出版日期:2021-01-15 发布日期:2021-01-01
  • 作者简介:李文明(1997- ),男,烟台大学计算机与控制工程学院硕士生,主要研究方向为时空数据挖掘
    刘芳(1994- ),女,烟台大学计算机与控制工程学院硕士生,主要研究方向为局部异常检测
    吕鹏(1995- ),男,烟台大学计算机与控制工程学院硕士生,主要研究方向为高维数据异常检测
    于彦伟(1986- ),男,博士,中国海洋大学计算机科学与技术系副教授,中国计算机学会会员,主要研究方向为时空数据挖掘、机器学习、分布式计算
  • 基金资助:
    国家自然科学基金资助项目(61773331)

Travel time estimation based on urban traffic surveillance data

Wenming LI1, Fang LIU1, Peng LYU1, Yanwei YU2   

  1. 1 School of Computer and Control Engineering, Yantai University, Yantai 264005, China
    2 Department of Computer Science and Technology, Ocean University of China, Qingdao 266100, China
  • Online:2021-01-15 Published:2021-01-01
  • Supported by:
    The National Natural Science Foundation of China(61773331)

摘要:

随着智慧交通的发展,越来越多的监控摄像头被安装在城市道路路口,这使得利用城市交通监控大数据进行车辆行程时间估计和路径查询成为可能。针对城市出行的行程时间估计问题,提出一种基于城市交通监控大数据的行程时间估计方法UTSD。首先,将交通监控摄像头映射到城市路网,并根据交通监控数据记录构建有向加权的城市路网图;然后,针对行程时间估计,构建时空索引和反向索引结构,时空索引用于快速检索所有车辆的摄像头记录,反向索引用于快速获取每辆车辆的行程时间和经过的摄像头轨迹,这两个索引大大提升了数据查询和行程时间估计的效率;最后,基于构建的索引,给出一种有效的行程时间估计和路径查询方法,根据出发时间、出发地和目的地,在时空索引结构上匹配出发地与目的地共有的车辆,再利用反向索引,快速获得行程时间估计与车辆路线。使用某省会城市的真实交通监控大数据进行实验评估,所提方法UTSD的准确率比基于有向图的Dijkstra最短路径算法和百度算法分别提高了65.02%和40.94%,且UTSD在以7天监控数据作为历史数据的情况下,平均查询时间低于0.3 s,验证了所提方法的有效性和高效性。

关键词: 城市交通监控大数据, 时空索引结构, 行程时间估计, 路线推荐

Abstract:

With the development of intelligent transportation, more and more surveillance cameras are deployed at the intersections of urban roads, which makes it possible to use the urban traffic surveillance data to estimate the vehicle travel time and query the route. Aiming at the problem of urban travel time estimation, a travel time estimation method based on the urban traffic surveillance data was proposed, which is called UTSD. Firstly, the traffic surveillance cameras were mapped into the urban road network, and a directed weighted urban road network graph was constructed based on traffic monitoring data recording. Secondly, a spatio-temporal index and a reverse index structure were built for travel time estimation, the former was used to quick search the camera records of all vehicles, and the latter was used to fast obtain the travel time and the passing camera trajectory of each vehicle. These two indexes significantly improved the efficiency of data query and travel time estimation. Finally, based on the constructed indexing structures, an effective travel time estimation and path query method was given. According to the departure time, origin and destination, the vehicles with the same origin and destination were matched on the spatio-temporal index structure, and then the reverse index was used to quickly obtain the travel time estimate and vehicle route. Using the real traffic monitoring big data of a provincial capital city for experimental evaluation, compared with Dijkstra shortest path algorithm based on directed graph and Baidu algorithm, the accuracy rate of the proposed method UTSD is improved by 65.02% and 40.94%, respectively. In addition, the average query time of UTSD is less than 0.3 s when the 7-day monitoring data is used as historical data, which verifies the effectiveness and efficiency of the proposed method.

Key words: urban traffic surveillance data, spatio-temporal index structure, travel time estimation, route recommendation

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