Daily Commuting Equilibrium Analysis During Transition Phase from Manual Cars to Autonomous Vehicles

Abstract: Deployment of autonomous vehicles in a traffic network can be a root cause of several changes like travel patterns, mode choice, total travel kilometres, and congestion. This may result in many issues during transport planning, such as urban congestion tolls; one of the key tools used traditionally to manage mobility in cities by incentivizing optimum travel choices. So, this research adopted Vickery’s bottleneck model for assessing the generalized travel cost for dynamic traffic patterns during morning and evening commutes in the presence of autonomous vehicles. Three scenarios (scenario 1: only manual cars, scenario 2: transition phase, and scenario 3: 100% autonomous vehicles) are generated for calculating the user’s total travel cost of the system during equilibrium both for evening and morning commutes, respectively. To achieve the system optimum, this research adopted the time-dependent congestion toll and social parking pricing strategy. Furthermore, the formulated model is applied to a numerical study having an assumed network link. Results clarified from the numerical study that the user’s total travel cost significantly decreases when a system has autonomous vehicles in the future. So, congestion on the network decreases, which leads to less congestion toll and social parking pricing during the transition phase to a fully AV environment.