Bishop Michael O, Dawson Jeffrey D, Merickel Jennifer, Rizzo Matthew
Department of Biostatistics, University of Iowa College of Public Health, 145 N. Riverside Drive, Iowa City, IA 52242.
Department of Neurological Sciences, University of Nebraska Medical Center, Mind and Brain Health Labs, Omaha, NE 68198.
Proc Am Stat Assoc. 2018;2018:2420-2427.
In on-road driving behavior studies, vehicle acceleration is sampled at high frequencies and then reduced to meaningful metrics over short driving segments. We examined road test data from 65 subjects driving over a common route, as well as driving in naturalistic situations using their own vehicle. We isolated 24-second segments, then reduced the accelerometer data via two methods: 1) standard deviation (SD) within a segment, and 2) re-centering parameter from a time series model previously developed for driving simulator data. We analyzed the data via random effects models to ascertain the intraclass correlations (ICC's) of the metrics. With and without adjusting for speed, the ICC of SD within a segment tended to be much greater than the ICC of the re-centering parameter for the segment (range: 0-30% vs. 0-1%). Also, ICC's from the naturalistic driving data tended to be greater than the fixed-route data (range: 0-27% vs. 0-9%), which could reflect individuals exhibiting their more usual driving behavior in naturalistic environments. Findings illustrate the challenges of identifying meaningful driving metrics and comparing these across different epochs, road segments and research platforms.
在道路驾驶行为研究中，车辆加速度以高频进行采样，然后在短驾驶路段内简化为有意义的指标。我们检查了65名受试者在一条常见路线上驾驶以及使用自己的车辆在自然驾驶场景中的道路测试数据。我们分离出24秒的片段，然后通过两种方法简化加速度计数据：1）片段内的标准差（SD），以及2）来自先前为驾驶模拟器数据开发的时间序列模型的重新中心化参数。我们通过随机效应模型分析数据，以确定指标的组内相关性（ICC）。无论是否调整速度，片段内SD的ICC往往远大于该片段重新中心化参数的ICC（范围：0 - 30%对0 - 1%）。此外，自然驾驶数据的ICC往往大于固定路线数据的ICC（范围：0 - 27%对0 - 9%），这可能反映出个体在自然环境中表现出更平常的驾驶行为。研究结果说明了识别有意义的驾驶指标以及在不同时间段、路段和研究平台之间进行比较的挑战。
