Abstract

Precise travel route identification is the guiding basis of feasible urban traffic planning and modeling. A range of map matching (MM) algorithms have been studied in previous research to integrate coordinate-based GPS data with digital road network information to identify personal travel route. However, these methods significantly depend on the high-density positioning data, which are usually unavailable or costly for transportation planning projects. Fortunately, mobile phones under 3 G, 4 G and 5 G networks can generate massive cell-based travel trajectory data with almost no additional cost. This data is potentially valuable for travel route identification if proper MM algorithms can be developed. This paper proposes an MM method to detect travel route by using handoff trajectory data from the cellular phone network. First, a wireless communication simulation model for dynamic cellular handoff-based traffic monitoring is developed for handoff data collection. Second, the Earth Mover’s Distance (EMD) algorithm is used to identify travel route by finding the time-space relationship among the cellular handoff patterns of the road network. Finally, by comparing the performance of the proposed method with classical sequence similarity algorithms, results indicate that the EMD-based MM algorithm is much more efficient for travel route identification, and it can detect small spacing parallel roads with a high accuracy.

摘要

精确的出行路线识别是可行的城市交通规划和建模的指导依据。之前的研究已经研究了一系列地图匹配 (MM) 算法，以将基于坐标的 GPS 数据与数字道路网络信息相结合，以识别个人旅行路线。然而，这些方法在很大程度上依赖于高密度定位数据，而这些数据对于交通规划项目来说通常是不可用的或成本高昂的。幸运的是，3G、4G和5G网络下的手机可以生成海量基于蜂窝的旅行轨迹数据，几乎不需要额外的成本。如果可以开发适当的 MM 算法，则该数据对于旅行路线识别具有潜在价值。本文提出了一种利用移动电话网络的切换轨迹数据来检测旅行路线的MM方法。第一的，开发了一种基于动态蜂窝切换的流量监控的无线通信仿真模型，用于收集切换数据。其次，地球移动者距离（EMD）算法通过寻找道路网络蜂窝切换模式之间的时空关系来识别旅行路线。最后，通过将所提方法与经典序列相似性算法的性能进行比较，结果表明基于 EMD 的 MM 算法在出行路线识别方面的效率更高，并且可以高精度地检测小间距平行道路。Earth Mover's Distance（EMD）算法通过寻找道路网络蜂窝切换模式之间的时空关系来识别旅行路线。最后，通过将所提方法与经典序列相似性算法的性能进行比较，结果表明基于 EMD 的 MM 算法在出行路线识别方面的效率更高，并且可以高精度地检测小间距平行道路。Earth Mover's Distance（EMD）算法通过寻找道路网络蜂窝切换模式之间的时空关系来识别旅行路线。最后，通过将所提方法与经典序列相似性算法的性能进行比较，结果表明基于 EMD 的 MM 算法在出行路线识别方面的效率更高，并且可以高精度地检测小间距平行道路。