Estimation of the Capacity of a Basic Freeway and Weaving Segment Under Traditional, Autonomous, and Connected Autonomous Vehicles, Using Oversaturated Traffic Condition Data

Autonomous vehicles (AVs) and connected autonomous vehicles (CAVs) will be the standard in transportation in the future. The use of such vehicles could minimize traffic oscillation and travel time and boost safety and mobility on freeways. An AV is a self-driving vehicle that can make decisions by itself in any situation. CAVs include all the characteristics of AVs and additional communication with other vehicles or the infrastructure (signal system). The use of AVs and CAVs will substantially increase motorway capacity in upcoming decades. Moreover, vehicle dynamics will change as technology and algorithms become more commonplace. In the short term, capacity may have a negative impact on talent; however, as the algorithms become more aggressive, the results will improve. Highway Capacity Manual (HCM) may need to be updated if freeway system capacity changes. As a result, the manual should focus on enhancing two freeway segments: the fundamental freeway portion and the weaving part (case study on U.S. 101 in Los Angeles, California). A microsimulation program developed by the Planung Transport Verkehr (PTV) in Karlsruhe, Germany, was used to calibrate and evaluate Wiedemann's behavioral car-following model (CFM). The Coexist project from Europe created three types of autonomous cars: AV-cautious, AV-normal, and AV all-knowing. CFMs are vital because they measure the distance between vehicles. This is crucial for capacity. The capacity of AV cautious vehicles is decreased at all levels and penetrations. When AV-cautious autonomy evolves into AV all-knowing autonomy, the capacity of the weaving section and the BFS may rise by 33% and 36%, respectively. This study provides a method for evaluating the capacity of freeways, which we estimate using AV levels and penetrations. Transportation planners and traffic engineers may utilize these capabilities to design better traffic planning and traffic-management technology in the future. For example, highway capacity will be restricted if the AV mix is largely AV-cautious. However, the solution is likely not to expand capacity but to find ways to manage traffic as new technology develops and moves to CAVs. This research aids in the planning and design of how to bring AVs and CAVs to market