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

这个NSF智能和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、绘制和分析当弱势道路使用者（如骑自行车的人或行人）与汽车发生碰撞时发生的“险些”事件。在美国，易受伤害的道路使用者的伤害和死亡率不断上升，这是一个社会关注的领域，并导致公众对步行或骑自行车犹豫不决。这些趋势挑战了全国范围内正在进行的努力，这些努力旨在通过增加步行、骑自行车和公交使用的模式转变，使所有人的道路更安全，并减少交通部门的排放。迄今为止，解决与弱势道路使用者安全相关问题的数据驱动解决方案通常依赖于官方碰撞数据，但仅凭这些数据无法全面代表弱势道路使用者的安全体验。更广泛地记录未遂事件的能力，以及它们的频率和位置与官方报告的碰撞数据的比较，对于告知以安全为导向的交通规划策略至关重要。为了解决这个问题，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、交通工程和城市规划方面的方法和技术创新。分类技术的最新进展应用于光探测和测距（LiDAR）传感器收集的数据，提供了检测涉及弱势道路使用者的未遂事件的能力。该项目将在大里诺和斯帕克斯地区部署这种便携式传感器网络。传感器的位置将通过对网络调查的回应来告知，这些调查将分发给经常在社区步行或骑自行车的人，这将促使他们确定容易受到道路使用者安全关注的具体位置。数据将在这些地点收集一周。新兴的近距离脱靶检测方法将应用于现场收集的数据，并将近距离脱靶的频率和类型与官方坠机数据进行比较。社区焦点小组将审查这些传感器检测到的近靶事件，并提供反馈以改进事件识别方法。地理设计研讨会将制定一项合作计划，确定未来道路使用者安全评估的优先位置，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工作，如果有效扩展，可以提高实时检测未遂事件的能力，从而可以更好地为改善道路使用者安全的规划工作提供信息。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.