Detecting vehicle traffic patterns in urban environments using taxi trajectory intersection points

作者：Andreas Keler,Jukka M.Krisp,Linfang Ding

摘要：Detecting and describing movement of vehicles in established transportation infrastructures is an important task.It helps to predict periodical traffic patterns for optimizing traffic regulations and extending the functions of established transportation infrastructures.The detection of traffic patterns consists not only of analyses of arrangement patterns of multiple vehicle trajectories,but also of the inspection of the embedded geographical context.In this paper,we introduce a method for intersecting vehicle trajectories and extracting their intersection points for selected rush hours in urban environments.Those vehicle trajectory intersection points (TIP) are frequently visited locations within urban road networks and are subsequently formed into density-connected clusters,which are then represented as polygons.For representing temporal variations of the created polygons,we enrich these with vehicle trajectories of other times of the day and additional road network information.In a case study,we test our approach on massive taxi Floating Car Data (FCD) from Shanghai and road network data from the OpenStreetMap (OSM) project.The first test results show strong correlations with periodical traffic events in Shanghai.Based on these results,we reason out the usefulness of polygons representing frequently visited locations for analyses in urban planning and traffic engineering.

发文机构：Institute of Geography

关键词：FLOATINGCarData(FCD)movingobjectstransportationinfrastructureSPATIO-TEMPORALPATTERNSFloating Car Data (FCD)moving objectstransportation infrastructurespatio-temporal patterns

分类号： R73[医药卫生—肿瘤][医药卫生—临床医学]