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

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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.