Optimizing mixed traffic environments with shared and private autonomous vehicles: An equilibrium analysis of entrance permit and tradable credit strategies

Abstract

The importance of relieving central business district (CBD) congestion while fulfilling network user demand in the morning daily commute has prompted much attention to shared mobility. The deployment of ridesharing paired with autonomous vehicles is expected to bring about a paradigm shift in traffic network dynamics by eliminating the considerable reliance on solo-passenger vehicle usage. In this study, we propose and evaluate two strategies: (1) a CBD entrance permit and (2) temporal capacity allocation with tradable credit. To evaluate the effectiveness of the proposed strategies, we consider four travel modes, including Transit (T), Shared Autonomous Vehicle (SAV), Autonomous Vehicle (AV), and Conventional Vehicle (CV), and account for various factors, such as costs associated with walking, autonomous vehicle self-driving, ridesharing, travel time, and schedule delay. These strategies aim to encourage commuters to adopt sustainable transit or shared mobility options, taking into account different scenarios that consider the challenges and advantages of ridesharing alongside traditional transit systems. The findings indicate that implementing temporal capacity allocation for ridesharing with tradable credit is more advantageous compared to the CBD entrance permit, particularly when the disparity in the fixed additional cost of using SAVs and AVs is minimal. However, both strategies rely on accurately estimating the extra cost incurred by commuters when opting for ridesharing services. Besides, introducing tradable award schemes for ridesharing and transit can improve the efficiency of the system. This study highlights the importance of using new methods and strategies in regulating the travel behavior of commuters with the emergence of autonomous vehicles and shared mobility options to determine solutions for optimizing the system cost. The findings of this study provide valuable insights for transportation planners and policymakers to develop effective strategies for reducing traffic congestion in CBDs.