Tracking vehicle trajectories by local dynamic time warping of mobile phone signal strengths and its potential in travel-time estimation

Charith D. Chitraranjan, A. Perera, A. Denton
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引用次数: 3

Abstract

Tracking vehicles has many applications, especially in traffic engineering, including estimation of travel time/speed, traffic density, and Origin-Destination matrices. In this paper, we propose local alignment of mobile phone signal strength measurements to track the movement of vehicles, and demonstrate its application to travel-time estimation for a road segment. We use local alignment instead of the traditionally used global alignment to allow for vehicles changing roads. More specifically, we use local dynamic time warping (LDTW) to align the signal strength trace of a phone carried in a vehicle, to a reference trace that we had collected for the relevant road segment. The signal strength trace from a mobile phone includes the strength of the signals received from the serving cell and six neighbor cells that form a multivariate time series. We perform the alignments on these multi-dimensional time series as they provide better location specificity than the univariate time series of the strongest cell, used in existing alignment-based methods. Experiments on drive test data show that our LDTW-based algorithm yields a lower positioning error with respect to ground truth (GPS traces), than comparison methods. Application of LDTW on real world call traces, made available to us by a mobile service provider, produced travel-time estimates with an average error of 11% and significant correlation with respect to travel-times computed through manual number plate recognition of vehicles.
手机信号强度局部动态时间翘曲跟踪车辆轨迹及其在行驶时间估计中的潜力
跟踪车辆有很多应用,特别是在交通工程中,包括估计行驶时间/速度、交通密度和出发地-目的地矩阵。在本文中,我们提出了移动电话信号强度测量的局部对齐来跟踪车辆的运动,并演示了其在路段旅行时间估计中的应用。我们使用局部对齐而不是传统的全局对齐来允许车辆改变道路。更具体地说,我们使用本地动态时间规整(LDTW)将车辆携带的手机的信号强度轨迹与我们为相关路段收集的参考轨迹对齐。来自移动电话的信号强度迹包括从服务小区和形成多元时间序列的六个相邻小区接收的信号的强度。我们对这些多维时间序列进行比对,因为它们比现有基于比对的方法中使用的最强单元的单变量时间序列提供更好的位置特异性。驾驶测试数据的实验表明,与比较方法相比,基于ldtw的算法相对于地面真值(GPS迹线)产生更低的定位误差。LDTW应用于移动服务提供商提供给我们的真实世界的呼叫轨迹,产生了平均误差为11%的旅行时间估计,并且与通过手动车牌识别计算的旅行时间有显著相关性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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