摘要
As the original Global Position System (GPS) data in Floating Car Data have the accuracy problem,this paper proposes a heuristic path-estimating algorithm for large-scale real-time traffic information calculating. It uses the heuristic search method,imports the restriction with geometric operation,and makes comparison between the vectors composed of the vehicular GPS points and the special road network model to search the set of vehicular traveling route candidates. Finally,it chooses the most optimal one according to weight. Experimental results indicate that the algorithm has considerable efficiency in accuracy (over 92.7%) and com-putational speed (max 8000 GPS records per second) when handling the GPS tracking data whose sampling rate is larger than 1 min even under complex road network conditions.
As the original Global Position System (GPS) data in Floating Car Data have the accuracy problem,this paper proposes a heuristic path-estimating algorithm for large-scale real-time traffic information calculating. It uses the heuristic search method,imports the restriction with geometric operation,and makes comparison between the vectors composed of the vehicular GPS points and the special road network model to search the set of vehicular traveling route candidates. Finally,it chooses the most optimal one according to weight. Experimental results indicate that the algorithm has considerable efficiency in accuracy (over 92.7%) and com-putational speed (max 8000 GPS records per second) when handling the GPS tracking data whose sampling rate is larger than 1 min even under complex road network conditions.
作者
L WeiFeng1,ZHU TongYu1,WU DongDong2,DAI Hong3 & HUANG Jian1 1 State Key Laboratory for Software Development Environment,Beihang University,Beijing 100083,China
2 Beijing Transportation Information Center,Beijing 100055,China
3 College of Arts & Science,Beijing Union University,Beijing 100083,China
基金
the National Basic Research Program of China ("973") (Grant No. 2005CB321900)
the National Hi-Tech Research and Devel- opment Program of China (Grant No.2006AA12Z315)
