University of Texas at Arlington, USA.
University of Texas at Arlington, Department of Civil Engineering, Box 19308, Arlington, TX, USA.
Accid Anal Prev. 2019 Jul;128:32-39. doi: 10.1016/j.aap.2019.03.014. Epub 2019 Apr 5.
Most agencies and decision-makers rely on crash and crash severity (property damage only, injury or fatality) data to assess transportation safety; however, in the context of public health where perceptions of safety may influence the willingness to adopt active transportation modes (e.g. bicycling and walking), pedestrian-motor vehicle and other similar conflicts types may define a better performance measure for safety assessment. In the field of transportation safety, an absolute conflict occurs when two parties' paths cross and one of the parties must undertake an evasive maneuver (e.g. change direction or stop) to avoid a crash. Other less severe conflicts where paths cross but no evasive maneuver is required may also impact public perceptions of safety especially for vulnerable modes. Most of the existing literature focuses on vehicle conflicts. While in the past several years, more research has investigated bicycle and pedestrian conflicts, most of this has focused on the intersection environment. A comprehensive analysis of conflicts appears critical. The major objective of this study is two fold: 1) Development of an innovative and cost effective conflict data collection technique to better understand the conflicts (and their severity) involving vulnerable road users (e.g. bicycle/pedestrian, bicycle/motor vehicle, and pedestrian/motor vehicle) and their severity. 2) Test the effectiveness and practicality of the approach taken and its associated crowd sourced data collection. In an endeavor to undertake these objectives, the researchers developed an android-based crowd-sourced data collection app. The crowd-source data collected using the app is compared with traditional fatality data for hot spot analysis. At the end, the app users provide feedback about the overall competency of the app interface and the performance of its features to the app developers. If widely adopted, the app will enable communities to create their own data collection efforts to identify dangerous sites within their neighborhoods. Agencies will have a valuable data source at low-cost to help inform their decision making related to bicycle and pedestrian education, encouragement, enforcement, programs, policies, and infrastructure design and planning.
大多数机构和决策者依赖于碰撞和碰撞严重程度（仅财产损失、受伤或死亡）数据来评估交通安全性；然而，在公共健康领域，对安全性的看法可能会影响采用积极交通方式（例如骑自行车和步行）的意愿，行人-机动车和其他类似冲突类型可能为安全评估定义更好的绩效衡量标准。在交通安全领域，当两个当事方的路径交叉且其中一个当事方必须采取回避机动（例如改变方向或停车）以避免碰撞时，就会发生绝对冲突。其他路径交叉但不需要回避机动的不太严重的冲突也可能会影响公众对安全的看法，尤其是对弱势模式。大多数现有文献都集中在车辆冲突上。虽然在过去几年中，更多的研究调查了自行车和行人冲突，但其中大部分研究都集中在交叉口环境上。对冲突进行全面分析似乎至关重要。本研究的主要目标有两个：1）开发一种创新且具有成本效益的冲突数据收集技术，以更好地了解涉及弱势道路使用者（例如自行车/行人、自行车/机动车和行人/机动车）的冲突（及其严重程度）及其严重程度。2）测试所采用方法及其相关众源数据收集的有效性和实用性。为了实现这些目标，研究人员开发了一种基于 Android 的众源数据收集应用程序。使用该应用程序收集的众源数据与传统的热点分析死亡数据进行比较。最后，应用程序用户向应用程序开发人员提供有关应用程序界面整体能力和其功能性能的反馈。如果得到广泛采用，该应用程序将使社区能够开展自己的数据收集工作，以识别其社区内的危险地点。各机构将以低成本获得有价值的数据来源，以帮助其做出与自行车和行人教育、鼓励、执法、计划、政策以及基础设施设计和规划相关的决策。
