Safe Transportation Research and Education Center, University of California, Berkeley 2614, Dwight Way #7374, Berkeley, CA 94720, USA.
Safe Transportation Research and Education Center, University of California, Berkeley, CA, USA.
Accid Anal Prev. 2019 Sep;130:99-107. doi: 10.1016/j.aap.2017.08.014. Epub 2017 Aug 25.
In this paper, the non-motorized traffic safety concerns in and around three university campuses are evaluated by comparing police-reported crash data with traffic safety information sourced from the campus communities themselves. The crowdsourced traffic safety data comprise of both self-reported crashes as well as perceived hazardous locations. The results of the crash data analysis reveal that police-reported crashes underrepresent non-motorized safety concerns in and around the campus regions. The spatial distribution of police-reported crashes shows that police-reported crashes are predominantly unavailable inside the main campus areas, and the off-campus crashes over-represent automobile involvement. In comparison, the self-reported crash results report a wide variety of off-campus collisions not involving automobiles, while also highlighting the issue of high crash concentrations along campus boundaries. An assessment of the perceived hazardous locations (PHLs) reveals that high concentrations of such observations at/near a given location have statistically significant association with both survey-reported crashes as well as future police-reported crashes. Moreover, the results indicate the presence of a saturation point in the relationship between crashes and PHLs wherein beyond a certain limit, an increasing number of traffic safety concerns may not necessarily correlate with a proportional increase in the number of crashes. These findings suggests that augmenting our existing knowledge of traffic safety through crowdsourcing techniques can potentially help in better estimating both existing as well as emerging traffic safety concerns.
本文通过将警方报告的事故数据与校园社区自身提供的交通安全信息进行比较，评估了三所大学校园及其周边地区的非机动交通安全问题。这些众包的交通安全数据既包括自我报告的事故，也包括感知到的危险地点。事故数据分析的结果表明，警方报告的事故数据未能充分反映校园区域及其周边地区的非机动交通安全问题。警方报告的事故的空间分布表明，校园内主要区域主要没有警方报告的事故，而校外事故则过多地涉及汽车。相比之下，自我报告的事故结果报告了许多校外碰撞事故，这些事故不涉及汽车，同时也突出了校园边界沿线事故集中的问题。对感知危险地点（PHL）的评估表明，在给定地点/附近存在大量此类观察结果与调查报告的事故以及未来警方报告的事故具有统计学上的显著关联。此外，结果表明，在事故和 PHL 之间的关系中存在一个饱和点，即在某个极限点之后，越来越多的交通安全问题不一定与事故数量的成比例增加相关。这些发现表明，通过众包技术来补充我们现有的交通安全知识，有可能有助于更好地估计现有的和新出现的交通安全问题。
