Equilibrium Analysis in Urban Traffic: Impacts of Electric, Autonomous, and Shared Vehicles

Abstract

The incorporation of advanced technologies—such as Artificial Intelligence (AI), electric and autonomous vehicles (AVs), and ridesharing systems—into transportation networks have the potential to enhance daily commuting experiences significantly. By leveraging these technological advancements and incorporating smart mobility practices, transportation networks can be further optimized to align with contemporary living. However, despite the evident advantages these innovations offer, especially to transit-dependent users, there is growing apprehension. Concerns have arisen that without new and proper policies and planning, these technologies might exacerbate transportation equity disparities. Moreover, the financial implications and the novelty of these technologies serve as barriers to achieving widespread adoption. Once these technologies become an integral part of the network, they introduce a new set of challenges. As a result, it becomes imperative to investigate the implications they have on system costs and ensuing traffic complications.

Addressing these significant challenges necessitates holistic system optimizations and novel approaches to ensure a seamless, efficient, and equitable operation of the transportation system, considering the dynamic nature of traffic demand and congestion patterns. This requires developing strategies that not only augment the benefits of the novel technologies but also pave the way for a sustainable and equitable urban transportation paradigm.

This Ph.D. dissertation introduces four main innovations: (1) A comprehensive examination of morning commute patterns is conducted, particularly in the context of a mixed traffic environment consisting of Transit (T), Electric Vehicle (EV), Shared Autonomous Vehicle (SAV), Autonomous Vehicle (AV), and Conventional Vehicle (CV)., with a focus on various influential factors such as autonomous vehicle self-driving costs and schedule delays. (2) Various novel strategies are introduced to mitigate congestion in central business districts (CBDs) in order to reduce private vehicle usage and encourage ridesharing, promote sustainable transit choices, and optimize the total system costs (TSC). (3) Different novel network configurations, such as various bottleneck capacity allocations and introducing HOV or en-route electric charging lanes, are applied to identify their effectiveness in changing mode and path choice. (4) Homogeneous and heterogeneous commuters are considered to capture insights from both general trends and diverse individual preferences, enabling the formulation of comprehensive and inclusive transportation strategies.