Dynamic Systems Modeling and Integrated Transportation Demand-and-Supply Management with a Polynomial Arrival Queue Model

Abstract

Dynamic systems modeling (DSM) provides an opportunity to systematically investigate traffic system dynamics at the network scale. However, simulation-based DSM approaches are often criticized for their heavy computational burden and low efficiency, as well as the difficulty in calibrating and validating their results. In this study, we propose an analytical modeling approach that integrates the analysis of both demand and supply sides using the polynomial arrival queue (PAQ) model. This model approximates the arrival rate by a polynomial function and the discharge rate by a constant, offering an efficient and accurate representation of traffic dynamics. Our proposed approach allows for the joint optimization of demand regulation policies and infrastructure capacity-building efforts, addressing the shortcomings of locally oriented congestion reduction strategies in isolation. This integrated approach is of great importance for transportation network modeling, management, and control. Overall, our study presents a novel and effective approach to transportation engineering, utilizing DSM and analytical modeling to optimize both demand and supply sides, ultimately improving transportation network performance.