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

机译:移动电话信号强度的局部动态时间翘曲跟踪车辆轨迹及其在旅行时间估算中的潜力

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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.
机译:跟踪车辆具有许多应用,尤其是交通工程,包括旅行时间/速度,流量密度和原始目的地矩阵的估计。在本文中,我们提出了移动电话信号强度测量的局部对准,以跟踪车辆的运动,并证明其在道路段的行进时间估计的应用。我们使用局部对齐而不是传统上使用的全局对齐,以允许车辆改变道路。更具体地,我们使用本地动态时间翘曲(LDTW)将车辆中携带的手机的信号强度迹线对齐,以便为相关的道路段收集的参考迹线。来自移动电话的信号强度轨迹包括从服务小区接收的信号的强度和形成多变量时间序列的六个相邻小区。我们在这些多维时间序列上执行对齐,因为它们提供比最强单元的单变量时间序列更好的位置特异性,用于基于现有的基于对齐的方法。驱动器测试数据的实验表明,基于LDTW的算法相对于地面真理(GPS迹线)产生了较低的定位误差,而不是比较方法。 LDTW在移动服务提供商提供的现实世界呼叫迹线上的应用,产生了通过手动编号识别车辆的手动编号识别的平均误差11%的平均误差,并且与通过车辆的手动编号识别所识别的旅行时间相关的平均误差。

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