城市商业服务设施吸引力的空间相关性分析

提出了一种根据择路经验特征,利用证据理论检测出租车异常轨迹的方法。该方法考虑最短路径与轨迹长度的比值、规避路径代价、出行发生时间3个因素,利用证据理论综合这3个证据来判别轨迹的异常程度,检测出行距离和路径选择明显不同于正常情况下的异常轨迹。实验结果表明此方法能有效识别不符合正常认知的异常轨迹,不依赖于单一起始点和终点对(origin-destination,OD)中的轨迹数目,能快速处理海量GPS数据,可用于大规模浮动车数据择路行为分析前期的数据过滤。

Spatial Correlation Analysis of Attractiveness of Commercial Facilities

In the context of urban transportation, large-scale collections of floating car trajectories are constrained by low sampling rates due to concernsabout data processing and storage. This creates uncertainty when identifying movement trajectories that reflect true route choice behaviors. To reduce uncertainty, this paper presents an approach using experiential constraints based on evidence theory to detect anomalous trajectories in taxi GPS trajectories. The approach employs three factors including the ratio of travel length between GPS traces and shortest distance path, the cost index of experiential avoid roads, and travel start times. The evidences based on the three measurements are combined in an evidence theory framework in order to get the anomalous degree of each trajectory so that the anomalous trajectories whose travel distance and travel route are significantly different from normal ones can be detected. A case study is presented using real world GPS trajectories of over 11, 000 taxis. The experimental results in Wuhan show that our method, which is not influenced by the number of trajectories between a single OD pair, has the ability to detect anomalous trajectories and can be applied to clean biased data before route choice analysis using a large fleet of floating cars.