Abstract
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.
Highlights
Intelligent traffic information service is to make use of advanced technologies, transport operation data, analysis, and decision algorithms to obtain traffic knowledge for providing decisionmaking services for government, enterprises, and the public
Considering that there are inevitably location errors in both trajectory data and basic coach operation information, and the coach station has a certain spatial size, the buffer radius ds in the trajectory splitting introduced in Section 2.2 is set to 0.2km
We find that at the extracted location is a place where traffic jam occurs very frequently; the GPS trajectory points collected here are very dense, which causes it to be wrongly extracted since it has similar characteristics to coach stations
Summary
Intelligent traffic information service is to make use of advanced technologies (internet of things, cloud computing, etc.), transport operation data, analysis, and decision algorithms to obtain traffic knowledge for providing decisionmaking services for government, enterprises, and the public. Quality of intelligent traffic information service is affected by the accuracy and efficiency of analysis algorithms, and by the completeness and quality of transport operation data [10]. Taking the coach operation information in China as an example, a large number of passenger transport enterprises are involved in this industry, and the mode of operation in road passenger stations is isolated and low standardized [12]. It would be a very time-consuming and labor-intensive task to manually collect detailed coach operation information in a large area
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