An Automatic Extraction Method of Coach Operation Information from Historical Trajectory Data

• Published in: Journal of advanced transportation Vol. 2019; no. 2019; pp. 1 - 15
• Main Authors: Xu, Yubin, Ma, Yan, Zhu, Yan, Li, Qingqi, Li, Jun, Xie, Chao
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 01.01.2019; Hindawi; Hindawi Limited; Wiley
• Subjects: Algorithms; Clustering; Data collection; Data mining; Decision making; Economic impact; Global positioning systems; GPS; Information processing; Information services; Labor; Methods; Road transportation; Schedules; Stations; Tourist attractions; Traffic control; Traffic flow; Traffic information; Trajectory analysis; Transportation planning; Vehicles; China
• ISSN: 0197-6729; 2042-3195
• DOI: 10.1155/2019/3634942

Quality of travel service for road transport relies heavily on richness of transport operation data. Currently, most types of data including coach operation data are collected by manual investigation which is time-consuming and labor-intensive, and this significantly hinders the realization of intelligent traffic information service. In view of the above problems, this paper is aimed at introducing a method of automatically extracting coach operation information using historical GPS trajectory data of massive coaches. The method first analyzes trajectory characteristics of coaches within stations and identifies the highly dense point clusters as coach stations using the DBSCAN clustering algorithm. Then the schedule information is obtained by conducting error adjustment on the actual arrival and departure time series of multiple shifts, and the name of coach station is queried from point of interest (POI) and geographical name database provided by online map. Finally, the regular driving route of coaches is extracted by an incremental trajectory merging method. The proposed method is applied in handling historical trajectory data in the Beijing-Tianjin-Hebei region in China, and experimental results show that the extraction accuracy is 84% and verify its effectiveness and feasibility. The proposed method makes use of data mining techniques to extract coach operation information from big trajectory data and saves a lot of labor work, time, and economic cost required by on-site investigation.