ABSTRACT

Transportation agencies report that millions of crashes are caused by poor road conditions every year, which makes the localisation of roadway anomalies extremely important. Common methods of road condition evaluation require special types of equipment that are usually expensive and time-consuming. Therefore, the use of smartphones has become a potential alternative. However, differences in the sensitivity of their inertial sensors and their sample rate can result in measurement inconsistencies. This study validated those inconsistencies by using three different types of smartphones to collect data from the traversal of both a paved and an unpaved road. Three calibration methods were used including the reference-mean, reference-maximum, and reference-road-type methods. Statistical testing under identical conditions of device mounting using the same vehicle revealed that the roughness indices derived from each device and road type are normally distributed with unequal means. Consequently, applying a calibration coefficient to equalise the means of the distributions of roughness indices produced from any device using the reference mean method resulted in consistent measurements for both road types.

摘要

交通运输机构报告称，每年有数百万起事故是由恶劣的道路条件造成的，这使得道路异常的定位极为重要。路况评估的常用方法需要特殊类型的设备，这些设备通常既昂贵又耗时。因此，使用智能手机已成为一种潜在的替代方案。但是，它们的惯性传感器的灵敏度和采样率的差异可能会导致测量不一致。这项研究通过使用三种不同类型的智能手机从铺设和未铺设道路的遍历中收集数据来验证这些不一致。使用了三种校准方法，包括参考平均值、参考最大值和参考道路类型方法。使用相同车辆在相同设备安装条件下进行的统计测试表明，源自每个设备和道路类型的粗糙度指数以不等方式正态分布。因此，应用校准系数来均衡使用参考均值法从任何设备产生的粗糙度指数分布的均值会导致两种道路类型的测量结果一致。