Developing an Innovative Platform for Modeling Active Road User Interactions and Safety: Integration of Computer Vision, Agent-based, and Machine Learning Models

开发用于对主动道路用户交互和安全进行建模的创新平台：计算机视觉、基于代理和机器学习模型的集成

• 批准号: RGPIN-2019-06688
• 负责人: Hussein, Mohamed
• 金额: $ 2.26万
• 依托单位: McMaster University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=715732
• 关键词: Developing; Innovative; Platform; Modeling; Active

Promoting active modes of transportation, such as walking and biking, is receiving more emphasis in transportation engineering as they represent vital contributors to transportation sustainability and have several benefits to public health and the environment. Nevertheless, active travellers are the most vulnerable road users, as they are at a higher risk of being killed or severely injured due to road collisions. There is an urgent need to develop a solid understanding of active road user interaction behaviour and adopt innovative techniques to enhance their safety. As such, an innovative platform for analyzing and modeling active road user interactions and enhancing their safety is proposed in this research program. The proposed platform involves three interrelated focus areas. Focus Area I involves the acquisition of a variety of active road user datasets (mainly, video data collected at locations of interest and GPS data) and applying innovative analytical tools, including machine learning techniques and computer vision, to analyze the collected data. The analysis will investigate active road user interactions with each other, interactions with motorized vehicles (conflicts), and road user violation behaviour (jaywalking and temporal violations), and explore the implications of these behaviours on road safety. In Focus Area II, a novel agent-based micro-simulation model for modeling detailed road user interactions will be developed. The model relies on artificial intelligence principles and utilizes the agent-based modeling approach to simulate active road user interactions. The model will provide a powerful tool for addressing a variety of applications related to road user safety and assessing the level of service of active transportation facilities. The proposed model will enable the analysis of different design alternatives before making any changes to existing conditions and investigating scenarios that might be difficult to explore using actual data. Focus Area III involves developing a comprehensive framework for analyzing active road user safety through the advanced statistical modeling of collision and traffic conflict data on both the macro and the micro levels. In addition, the research will establish the statistical relationship between road user collisions and conflicts. The proposed research will create powerful analytical tools to support the design of safe transportation facilities for active road users of all ages and abilities. The proposed platform will aid transportation engineers and planners to promote and accommodate active transportation activities, which contributes to public health, safety, and reducing GHG emission. It will also enable to consider road user safety in the early planning stages. The adoption of the proposed platform is expected to save thousands of lives, either directly by improving road safety or indirectly by improving air quality and increasing the physical activity level of the population.

提倡积极的交通方式，如步行和骑自行车，在交通工程中受到越来越多的重视，因为它们是交通可持续性的重要贡献者，对公共卫生和环境有一些好处。然而，活跃的旅行者是最脆弱的道路使用者，因为他们因道路碰撞而死亡或严重受伤的风险更高。迫切需要深入了解道路使用者的主动互动行为，并采用创新技术来提高他们的安全。因此，本研究计划提出了一个创新的平台，用于分析和建模活跃的道路使用者互动并提高他们的安全性。拟议的平台涉及三个相互关联的重点领域。重点领域一涉及获取各种活跃的道路用户数据集（主要是在感兴趣的地点收集的视频数据和GPS数据），并应用创新的分析工具，包括机器学习技术和计算机视觉，来分析收集到的数据。该分析将调查道路使用者之间的主动互动、与机动车的互动（冲突）以及道路使用者的违规行为（乱穿马路和时间违规），并探讨这些行为对道路安全的影响。