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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:通过移动电话信号强度的局部动态时间弯曲跟踪车辆的轨迹及其在行驶时间估计中的潜力

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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,尤其是在交通工程中,包括估计行进时间/速度,交通密度和始发地目的地矩阵。在本文中,我们提出了移动电话信号强度测量的局部对准,以跟踪车辆的运动,并展示了其在路段行驶时间估计中的应用。我们使用局部路线代替传统使用的全局路线,以允许车辆改变道路。更具体地说,我们使用本地动态时间规整(LDTW)将车辆中携带的电话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轨迹包括从服务小区和六个邻居小区接收的信号强度,这些信号形成了多元时间序列。我们在这些多维时间序列上执行对齐,因为它们比现有的基于对齐方法中使用的最强单元格的单变量时间序列提供更好的位置特异性。对路测数据进行的实验表明,与比较方法相比,基于LDTW的算法相对于地面真相(GPS轨迹)产生的定位误差较小。 LDTW在移动服务提供商提供给我们的现实世界呼叫跟踪上的应用产生了行驶时间估计值,其平均误差为11%,并且与通过手动识别车牌识别出的行驶时间有关。

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