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为了改善传统地图匹配算法在基于低采样率GPS浮动车的高速公路交通流状态监测系统中的应用性能,提出了一种基于Oracle空间数据模型的地图匹配算法.该算法选取Oracle道路网络模型对海量GPS位置数据和高速道路网之间的空间关系进行分析,建立了一种可有效寻找GPS位置点之间合理候选行驶路径的N-最短路模型,并用逻辑模糊模型进行最终路径匹配的判断.采用美国洛杉矶市高速公路的实际调查数据对模型进行计算和验证,得到所提算法的计算速度约为每秒135条GPS位置数据,准确率为98.9%.结果表明,所提算法可以高效准确地将海量GPS位置数据匹配到具有复杂几何特征的高速公路网上.
In order to improve the application performance of traditional map matching algorithm in expressway traffic flow condition monitoring system based on low sampling rate GPS floating car, a map matching algorithm based on Oracle spatial data model is proposed. This algorithm selects the Oracle road network model for mass The spatial relationship between GPS position data and expressway network is analyzed and an N-shortest path model that can effectively find the reasonable candidate driving path between GPS location points is established and the final path matching is judged by using the logic fuzzy model. The actual survey data of the highway in Los Angeles city of USA calculated and verified the model, and the calculated speed of the proposed algorithm was about 135 GPS positions per second with an accuracy rate of 98.9%. The results show that the proposed algorithm can efficiently and accurately Massive GPS location data is matched to expressways online with complex geometry.