BACKGROUND Device-collected data from GPS and accelerometers for identifying active travel behaviors have dramatically changed research methods in transportation planning and public health. Automated algorithms have helped researchers to process large datasets with likely fewer errors than found in other collection methods (e.g., self-report travel diary). In this study, we compared travel modes identified by a commonly used automated algorithm (PALMS) that integrates GPS and accelerometer data with those obtained from travel diary estimates. METHODS Sixty participants, who made 2100 trips during seven consecutive days of data collection, were selected from among the baseline sample of a project examining the travel behavior impact of a new light rail system in the greater Seattle, WA (USA) area. GPS point level analyses were first conducted to compare trip/place and travel mode detection results using contingency tables. Trip level analyses were then performed to investigate the effect of proportions of time overlap between travel logs and device-collected data on agreement rates. Global performance (with all subjects' data combined) and subject-level performance of the algorithm were compared at the trip level. RESULTS At the GPS point level, the overall agreement rate of travel mode detection was 77.4% between PALMS and the travel diary. The agreement rate for vehicular trip detection (84.5%) was higher than for bicycling (53.5%) and walking (58.2%). At the trip level, the global performance and subject-level performance of the PALMS algorithm were 46.4% and 42.4%, respectively. Vehicular trip detection showed highest agreement rates in all analyses. Study participants' primary travel mode and car ownership were significantly related to the subject-level mode agreement rates. CONCLUSIONS The PALMS algorithm showed moderate identification power at the GPS point level. However, trip level analyses found lower agreement rates between PALMS and travel diary data, especially for active transportation. Testing different PALMS parameter settings may serve to improve the detection of active travel and help expand PALMS's applicability in geographically different urbanized areas with a variety of travel modes.

中文翻译：

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捕捉小规模旅行行为：个人活动位置测量系统（PALMS）与旅行日记之间的比较分析。

背景技术从GPS和加速度计收集的用于识别主动出行行为的设备数据极大地改变了交通规划和公共卫生领域的研究方法。自动化算法已帮助研究人员处理大型数据集，而错误率可能比其他收集方法（例如，自我报告旅行日记）中发现的错误少。在这项研究中，我们比较了由常用自动算法（PALMS）识别的出行方式，该算法将GPS和加速度计数据与从出行日记估计中获得的数据进行了集成。方法从连续60天连续7天收集数据的参与者中，选择了60名参与者，该项目是从一个项目基线样本中选出的，该项目研究了一个新轻轨系统对华盛顿州大西雅图地区的行车行为的影响。首先进行GPS点水平分析，以使用列联表比较旅行/地点和旅行模式的检测结果。然后进行旅行级别分析，以调查旅行记录和设备收集的数据之间的时间重叠比例对协议率的影响。在行程级别比较了算法的总体性能（结合了所有受试者的数据）和受试者级别的性能。结果在GPS点级别上，PALMS与旅行日记之间的旅行模式检测总体同意率为77.4％。车辆出行检测的同意率（84.5％）高于骑自行车（53.5％）和步行的同意率（58.2％）。在旅行级别，PALMS算法的整体性能和主题级性能分别为46.4％和42.4％。在所有分析中，车辆旅行检测显示出最高的一致率。研究参与者的主要出行方式和汽车拥有量与受试者水平的出行协议率显着相关。结论PALMS算法在GPS点级别显示出中等识别能力。但是，出行水平分析发现PALMS和旅行日记数据之间的协议率较低，尤其是对于主动运输而言。测试不同的PALMS参数设置可能有助于改善对主动出行的检测，并有助于扩展PALMS在具有各种出行方式的地理上不同的城市化地区的适用性。结论PALMS算法在GPS点级别显示出中等识别能力。但是，出行水平分析发现PALMS和旅行日记数据之间的协议率较低，尤其是对于主动运输而言。测试不同的PALMS参数设置可能有助于改善对主动出行的检测，并有助于扩展PALMS在具有各种出行方式的地理上不同的城市化地区的适用性。结论PALMS算法在GPS点级别显示出中等识别能力。但是，出行水平分析发现PALMS和旅行日记数据之间的协议率较低，尤其是对于主动运输而言。测试不同的PALMS参数设置可能有助于改善对主动出行的检测，并有助于扩展PALMS在具有各种出行方式的地理上不同的城市化地区的适用性。