Managing network congestion with tradable credit scheme: a trip-based MFD approach

09/15/2020 ∙ by Renming Liu, et al. ∙ 0

This study investigates the efficiency and effectiveness of area-based tradable credit scheme (TCS) based on the trip-based Macroscopic Fundamental Diagram model for the morning commute problem. In the proposed tradable credit scheme, the regulator distributes initial credits to all travelers and designs a time-varying and trip length specific credit tariff. Credits are traded between travelers and the regulator, and the credit price is determined by the demand and supply of credits. The heterogeneity of travelers is considered in terms of desired arrival time, trip length and departure-time choice preferences. The TCS is incorporated into a day-to-day modelling framework to examine the travelers' learning process and the evolution of network and credit market properties. The existence of the equilibrium solution and the uniqueness of the credit price at equilibrium state are established analytically. Furthermore, a open-source simulation framework is developed to showcase the analytical properties of the proposed TCS and compare it with alternative control strategies in terms of mobility, network and social welfare performance. Bayesian optimization is then adopted to optimize the credit toll scheme. The numerical results demonstrate that the proposed TCS outperforms the no-control case and matches the performance of the time-of-day pricing strategy, while maintaining its revenue-neutral nature.



There are no comments yet.


page 1

page 2

page 3

page 4

This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.