Supplementary data for the paper 'Identifying lane changes automatically using the GPS sensors of portable devices'

Mobile applications that provide GPS-based route navigation advice or driver diagnostics (e.g., driving speed, driving style) are gaining popularity. However, these applications currently do not have knowledge of whether the driver is performing a lane change. Having such information may prove valuable to individual drivers (e.g., to provide more specific navigation instructions) or road authorities (e.g., knowledge of lane change hotspots may inform road design). The present study aimed to assess the accuracy of lane change recognition algorithms that rely solely on mobile GPS sensor input. Three trips on Dutch highways, totaling 158 km of driving, were performed while carrying two smartphones (Huawei P20, Samsung Galaxy S9), a GPS-equipped GoPro Max, and a USB GPS receiver (GlobalSat BU343-s4). The timestamps of 215 lane changes, acting as the ground truth, were manually extracted from the forward-facing GoPro camera footage. After connecting the GPS trajectories to the road using Mapbox Map Matching API, lane changes were identified based on the exceedance of a lateral translation threshold in set time windows. The number of true positives, false positives, true negatives, and false negatives with respect to the ground truth were tabulated, and the overall accuracy of the lane-change classification was found to be 90%. The method appears promising for highway engineering and traffic behavior research that use floating car data, but there may be limited applicability to real-time advisory systems due to the occasional occurrence of false positives.

提供基于GPS的路线导航建议或驾驶员诊断（例如行驶速度、驾驶风格）的移动应用正日益普及。然而，当前这类应用无法知晓驾驶员是否正在进行变道操作。掌握此类信息对个体驾驶员（例如提供更精准的导航指令）或道路管理部门（例如通过变道热点数据优化道路设计）均具有重要价值。本研究旨在评估仅依赖移动GPS传感器输入的变道识别算法的准确性。研究人员在荷兰高速公路上完成了三段行程，总行驶里程达158公里，测试过程中搭载了两部智能手机（华为P20、三星Galaxy S9）、一台配备GPS的GoPro Max运动相机，以及一款USB GPS接收器（GlobalSat BU343-s4）。以215次变道的时间戳作为基准真值（ground truth），研究人员从前置GoPro相机的录像中手动提取了这些数据。随后通过Mapbox地图匹配API将GPS轨迹与道路路网进行匹配，并基于设定时间窗口内的横向位移阈值是否被突破来识别变道行为。最终统计了相对于基准真值的真阳性、假阳性、真阴性与假阴性样本数量，结果显示变道分类的整体准确率达90%。该方法在使用浮动车数据的高速公路工程与交通行为研究中展现出应用潜力，但由于偶尔会出现假阳性结果，其在实时咨询系统中的适用性可能有限。