School of Qilu Transportation, Shandong University, Jinan, 250061, China.
School of Traffic and Transportation, Beijing Jiaotong University, Beijing, 100044, China.
Accid Anal Prev. 2020 Mar;137:105438. doi: 10.1016/j.aap.2020.105438. Epub 2020 Jan 28.
Skateboarding is being an emerging travel model, especially for young travelers. The conflict between skateboarders and the other road users has raised safety concerns for traffic engineers. Safety evaluation about skateboarder-related conflicts has not been well performed due to the low skateboarder-related crashes and the limited historical crash data. Near-crashes have been considered as surrogate data for skateboard-related safety evaluation. This paper developed a procedure to extract skateboarder-associated near-crashes automatically with the roadside Light Detection and Ranging (LiDAR). A new indicator: distance-deceleration-time profile (DDTP) which combined time, space, and deceleration information was introduced for skateboarder-pedestrian near-crash identification. The DDTP was developed for the roadside LiDAR data specially. The case studies showed that the proposed method can extract skateboarder-pedestrian safety-critical events with high accuracy. The proposed method can be also used for skateboarder-vehicle and skateboarder-bicycle near-crash identification.
滑板运动是一种新兴的出行方式，尤其受到年轻旅行者的青睐。滑板者与其他道路使用者之间的冲突引起了交通工程师对安全问题的关注。由于滑板相关碰撞事故的发生率较低，且可用的历史碰撞数据有限，因此对滑板相关冲突的安全评估还不够完善。近碰撞事故已被视为滑板相关安全性评估的替代数据。本文开发了一种使用路边激光雷达（LiDAR）自动提取滑板相关近碰撞事故的程序。引入了一个新的指标：距离-减速度-时间分布（DDTP），该指标结合了时间、空间和减速度信息，用于识别滑板者-行人间的近碰撞事故。DDTP 是专门为路边 LiDAR 数据开发的。案例研究表明，该方法可以高精度地提取滑板者-行人间的安全关键事件。该方法还可用于识别滑板者-车辆和滑板者-自行车间的近碰撞事故。
