Incorporating Mixed Automated Vehicle Traffic in Capacity Analysis and System Planning Decisions

It is predicted that half of the vehicles sold and 40% of vehicle travel could be autonomous in the 2040s (Litman 2017). However, how the presence of connected and autonomous vehicles (CAV) impact highway capacity and network system performance remain unclear. Without this knowledge, it is hard to understand and quantify the implication of the disruptive CAV technologies on the existing traffic operations. Also, it would be difficult for relevant agencies (e.g., MPOs and state DOTs) to make appropriate long-term planning for preparing the infrastructure systems for emerging mixed CAV traffic. This proposal aims to explore methods for tackling these challenges and demonstrating their applicability via real-world case studies. The research team first will develop a comprehensive analytical approach to quantify highway capacity in mixed traffic environments. This approach considers CAV technology uncertainties considering different headway distributions and vehicle platooning configurations. A generalized capacity function for mixed CAV traffic for the full spectra of traffic density, CAV penetration rates, vehicle types, platooning configurations and highway segment types will be explored. The proposed model shall have a simple and practical form for easy applications by relative stakeholders. Next, the team will demonstrate applications of the proposed mixed traffic analysis approach to a transportation system with a case study on the Tampa Bay Regional Planning Model (http://www.tbrta.com/). The proposed model will be evaluated by AECOM, Florida District 7 Department of Transportation and Plan Hillsborough for assisting their planning decisions. Further, the team will also investigate how the evolution of traffic patterns from regular vehicles to CAV mixed traffics will impact the spatial patterns of job accessibility for the general population and the disparities across different socio-economic groups. The findings could offer policy implications related to transportation planning and urban design on job access for low-access areas.