Advances of positioning and wireless commu- nication technologies make it possible to collect a large number of trajectory data of moving vehicles in a fast and convenient fashion. The data can be applied to various f...Advances of positioning and wireless commu- nication technologies make it possible to collect a large number of trajectory data of moving vehicles in a fast and convenient fashion. The data can be applied to various fields such as traffic study. In derive average delay of traffic and verify the results with this paper, we attempt to flow arotmd intersections changes of time. The intersection zone is delineated first. Positioning points geographically located within this zone are selected, and then outliers are removed. Turn trips are extracted from selected trajectory data. Each trip, physically consisting of time-series positioning points, is identified with entry road segment and turning direction, i.e. target road segment. Turn trips are grouped into different categories according to their time attributes. Then, delay of each trip during a turn is calculated with its recorded speed. Delays of all trips in the same period of time are plotted to observe the change pattern of traffic conditions. Compared to conven- tional approaches, the proposed method can be applied to those intersections without fixed data collection devices such as loop detectors since a large number of trajectory data can always provide a more complete spatio-temporal picture of a road network. With respect to data availability, taxi trajectory data and an intersection in Shanghai are employed to test the proposed methodology. Results demonstrate its applicability.展开更多
文摘Advances of positioning and wireless commu- nication technologies make it possible to collect a large number of trajectory data of moving vehicles in a fast and convenient fashion. The data can be applied to various fields such as traffic study. In derive average delay of traffic and verify the results with this paper, we attempt to flow arotmd intersections changes of time. The intersection zone is delineated first. Positioning points geographically located within this zone are selected, and then outliers are removed. Turn trips are extracted from selected trajectory data. Each trip, physically consisting of time-series positioning points, is identified with entry road segment and turning direction, i.e. target road segment. Turn trips are grouped into different categories according to their time attributes. Then, delay of each trip during a turn is calculated with its recorded speed. Delays of all trips in the same period of time are plotted to observe the change pattern of traffic conditions. Compared to conven- tional approaches, the proposed method can be applied to those intersections without fixed data collection devices such as loop detectors since a large number of trajectory data can always provide a more complete spatio-temporal picture of a road network. With respect to data availability, taxi trajectory data and an intersection in Shanghai are employed to test the proposed methodology. Results demonstrate its applicability.