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：高分辨率公交车辆定位（“心跳”）数据是许多公交机构相对较新的数据源。从表面上看，心跳数据能提供记录中公交车辆行程所有运行细节的丰富信息，涵盖从位置轨迹到速度与加速度剖面。以往研究主要集中于通过车辆状态将总行程出行时间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。本研究深入探讨了从心跳数据重构完整、连续且平滑的公交车辆轨迹这一任务，该轨迹可提取公交车在行程中任意时刻的运行信息。仅利用心跳数据的纬度、经度和时间戳字段，作者证明可通过局部回归结合单调三次样条插值方法重构出连续、平滑且单调的车辆轨迹。所得轨迹可用于评估公交运行性能，并识别交通信号灯、人行横道及公交车站等基础设施附近公交延误的具体位置。