基于WiFi探针数据的城市出行轨迹提取

为了更便捷地提取城市居民的出行轨迹,从而分析个体的日常空间行为,进而为城市管理的各项措施决策提供数据支撑,本文提出基于WiFi探针数据的城市出行轨迹提取方法,主要解决WiFi探针数据的路网匹配及丢失轨迹重构问题。首先,通过对终端MAC码和时间戳进行多列排序后提取出轨迹记录序列,利用信号强度RSSI值为每条记录提取坐落在路网上的候选点集。其次,设计基于局部评价的算法,对于每一个候选点,利用其前后相邻的几条记录提取的候选点集与其之间的时空关系,先后对其进行时间一致性评价和空间一致性评价,再结合以时间反比动态构建的权函数,得到最终评分;然后将每个候选点集中评分最高的点作为最佳匹配点,至此完成轨迹记录的路网匹配。最后,先采用基于深度优先的路径搜索算法搜索出丢失轨迹上下点之间的所有可行路径,再基于TOPSIS法决策出最优的重构路径。本文以东莞市市中心区域收集的WiFi探针数据为实验数据进行测试,平均每日可提取6万多条轨迹,与其中获取的GPS数据相比较验证了方法的可行性,为城市出行轨迹挖掘提供了新的解决方案。

Urban Travel Trajectory Extraction based on WiFi Probe Data

In order to extract the travel trajectory of urban residents more conveniently, analyze the daily spatial behavior of individuals, and provide data support for the decision-making of urban management measures, this paper proposes an urban travel trajectory extraction method based on WiFi probe data, which mainly solves the problem of map matching and lost trajectory reconstruction of WiFi probe data. First, extract the track record sequence by sorting the terminal MAC code and timestamp in multiple columns, and use the RSSI value to extract the candidate point set located on the road network for each record. Secondly, an algorithm based on local evaluation is designed: for each candidate point, the spatio-temporal relationship between the candidate point set extracted from the adjacent records is used to evaluate its temporal consistency and spatial consistency, and then the final score is obtained by combining with the weight function dynamically constructed in inverse time ratio, then the highest score point in each candidate point set is selected as the best matching point. Finally, a depth-first-based path search algorithm is used to search for all feasible paths between the upper and lower points of the lost trajectory, and then the optimal reconstruction path is determined based on the TOPSIS method. In this paper, the WiFi probe data collected in the central area of Dongguan City is used as the experimental data to test, and more than 60 000 tracks can be extracted every day on average. Compared with the GPS data, the feasibility of the method is verified, which provides a new solution for urban travel trajectory mining.