SCC-PG: A multidisciplinary approach to assessing city-wide near misses between vehicles and vulnerable road users in Reno-Sparks, Nevada

NSF的智能互联社区项目将采用一种新颖的多学科方法，通过社区参与来检测，绘制和分析当脆弱的道路使用者（如骑自行车者或行人）与汽车之间发生碰撞时发生的“未遂”事件。在美国，弱势道路使用者受伤和死亡率的上升是社会关注的一个领域，并导致公众对更多地步行或骑自行车犹豫不决。这些趋势对全国范围内正在进行的努力提出了挑战，这些努力旨在通过增加步行，骑自行车和过境使用的方式转变，使道路对所有人都更安全，并减少交通部门的排放。迄今为止，解决弱势道路使用者安全问题的数据驱动解决方案往往依赖于官方的碰撞数据，但这些数据本身并不能全面反映弱势道路使用者的安全体验。能够更广泛地记录未遂事件，以及它们的频率和位置如何与官方报告的碰撞数据进行比较，对于为以安全为导向的交通规划战略提供信息至关重要。为了解决这一问题，该项目将与内华达州大里诺和斯帕克斯的社区合作者合作，整合地理学、交通工程和城市规划方面的方法和技术创新。应用于光探测和测距（LiDAR）传感器收集的数据的分类技术的最新进展，提供了检测涉及弱势道路使用者的未遂事件的能力。该项目将在整个大里诺和斯帕克斯部署一个便携式传感器网络。传感器位置将通过对分发给社区中经常步行或骑自行车的人的基于网络的调查的响应来告知，该调查将促使他们确定易受伤害的道路使用者安全问题的具体位置。将在这些地点收集数据一周。将对现场收集的数据应用新出现的未遂事故检测方法，并将未遂事故的频率和类型与官方坠机数据进行比较。一个社区焦点小组将审查这些传感器检测到的未遂事件，并提供反馈，以改进事件识别方法。一个地理设计讲习班将制定一项合作计划，优先考虑未来评估脆弱道路使用者安全的地点，并确定潜在的对策。这些努力将有助于指导正在进行的整合传感器网络的努力，如果有效扩展，可以提高实时检测未遂事件的能力，从而更好地为规划工作提供信息，以提高道路使用者的安全性。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

This NSF Smart and Connected Communities project will employ a novel and multidisciplinary approach informed by community participation to detect, map, and analyze “near-miss” events that occur when a collision between a vulnerable road user, such as a bicyclist or pedestrian, and an automobile is narrowly avoided. Rising injury and fatality rates in the United States for vulnerable road users is an area of societal concern, and contribute to public hesitancy to walk or bicycle more. These trends challenge ongoing efforts nationwide that aim to both make roads safer for all and reduce transportation sector emissions through a modal shift to increased walking, bicycling, and transit use. To date, data-driven solutions to address issues related to vulnerable road user safety often rely on official crash data, but these data cannot alone comprehensively represent the safety experiences of vulnerable road users. The ability to more broadly record near-miss events, and how their frequency and locations compare to officially reported crash data, is essential to informing safety-oriented transportation planning strategies. To address this topic, this project will integrate approaches and technological innovations from geography, traffic engineering, and urban planning, in partnership with community collaborators in greater Reno and Sparks, Nevada.Recent advancement in classification techniques applied to data collected from Light Detection and Ranging, or LiDAR, sensors provides an ability to detect near-miss events involving vulnerable road users. This project will deploy a portable network of such sensors at locations throughout greater Reno and Sparks. Sensor locations will be informed by responses to a web-based survey distributed to those who frequently walk or bicycle in the community that will prompt them to identify specific locations of vulnerable road user safety concern. Data will be collected at these locations for one week. Emerging near-miss detection methods will be applied to the field-collected data, and frequency and type of near misses will be compared against official crash data. A community focus group will review near miss events detected by these sensors and provide feedback to improve event identification methods. A Geodesign workshop will produce a collaborative plan that will prioritize locations for future assessment of vulnerable road user safety, and identify potential countermeasures. These efforts will help guide ongoing efforts to integrate a sensor network that if effectively scaled, could improve the ability to detect near-miss events in real-time, which in turn can better inform planning efforts to improve road user safety.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.