Determining Truck Activity from Recorded Trajectory Data

• Published in: Procedia, social and behavioral sciences Vol. 20; pp. 796 - 805
• Main Authors: Fazekas, Zoltán, Gáspár, Péter, Kovács, Roland
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 2011
• Subjects: Route monitoring; Spatiotemporal description languages; Transportation fleet management; Transportation fleet management; Route monitoring; Spatiotemporal description languages
• ISSN: 1877-0428; 1877-0428
• DOI: 10.1016/j.sbspro.2011.08.088

It is a well-known and understandable intention of freight transportation companies worldwide to verify the proper use of their truck fleets. An important and a hardly avoidable step of monitoring truck routes is the visualization of collected route data. To improve the efficiency of visual monitoring, two preprocessing steps are proposed in the paper. The first of these is segmentation of routes into meaningful route sections; while the second is the automatic rendering of route sections to vehicle activity classes. The route segmentation method is based on spatiotemporal features of the trajectory. It is a linguistic approach that can cope with the multiple spatial and temporal resolutions necessary to characterize vehicle/driver activities. These activities range from fairly simple activities to complex maneuvers. Pointers to the detected activities and maneuvers in the trajectory data can be used for indexing purposes. The low-speed vehicle maneuvers and stoppages are looked at in particular. The rendering of route sections to vehicle activity classes is based primarily on the recorded trajectory and speed data, but transportation specific and general road data can be also taken into account. The transportation specific data may include geographic and descriptive data on the actual and regular truck destinations, as well as on roadside accommodations and refueling stations. The general road data provide designations of roads, maximum speed allowed and the socio-cultural structure (e.g., urban, rural) along the roads. The preprocessed truck trajectories could facilitate the detection of traffic rule infringements and suspect driver behavior.