High-resolution location (``heartbeat'') data of transit fleet vehicles is a relatively new data source for many transit agencies. On its surface, the heartbeat data can provide a wealth of information about all operational details of a recorded transit vehicle trip, from its location trajectory to its speed and acceleration profiles. Previous studies have mainly focused on decomposing the total trip travel time into different components by vehicle state and then extracting measures of delays to draw conclusions on the performance of a transit route. This study delves into the task of reconstructing a complete, continuous and smooth transit vehicle trajectory from the heartbeat data that allows for the extraction of operational information of a bus at any point in time into its trip. Using only the latitude, longitude, and timestamp fields of the heartbeat data, the authors demonstrate that a continuous, smooth, and monotonic vehicle trajectory can be reconstructed using local regression in combination with monotonic cubic spline interpolation. The resultant trajectory can be used to evaluate transit performance and identify locations of bus delay near infrastructure such as traffic signals, pedestrian crossings, and bus stops.

翻译：高分辨率定位（“心跳”）数据是许多公交机构相对较新的数据源。从表面上看，心跳数据可以提供有关记录公交车辆行程所有运营细节的丰富信息，包括其位置轨迹、速度和加速度曲线。以往研究主要侧重于将总行程时间按车辆状态分解为不同组成部分，进而提取延误指标以评估公交线路的运行性能。本研究深入探讨了如何从心跳数据中重构完整、连续且平滑的公交车辆轨迹，从而能够提取车辆行程中任意时刻的运营信息。仅利用心跳数据中的纬度、经度和时间戳字段，作者证明了通过局部回归结合单调三次样条插值，可以重构出连续、平滑且单调的车辆轨迹。生成的轨迹可用于评估公交运行性能，并识别交通信号灯、人行横道和公交车站等基础设施附近发生公交延误的位置。