A Two-Layer Network Dynamic Congestion Pricing Based on Macroscopic Fundamental Diagram

Abstract

Dynamic congestion pricing has attracted increasing attentions during the recent years. Nevertheless, limited research has been conducted to address the dynamic tolling scheme at the network level, such as to cooperatively manage two alternative networks with heterogeneous properties, e.g., the two-layer network consisting of both expressway and arterial network in the urban areas. Recently, the macroscopic fundamental diagram (MFD) developed by both field experiments and simulation tests illustrates a unimodal low-scatter relationship between the mean flow and density network widely, providing the network traffic state is roughly homogeneous. It reveals traffic flow properties at an aggregated level and sheds light on dynamic traffic management of a large network. This paper proposes a bilevel programming toll model, incorporating MFD to solve the unbalanced flow distribution problem within the two-layer transportation networks. The upper level model aims at minimizing the total travel time, while the lower level focuses on the MFD-based traffic assignment, which extends the link-based traffic assignment to network wide level. Genetic algorithm (GA) and the method of successive average were adopted for solving the proposed model, on which an online experimental platform was established using VISSIM, MATLAB, and Visual Studio software packages. The results of numerical studies demonstrate that the total travel time is decreased by imposing the dynamic toll, while the total travel time savings significantly outweigh the toll paid. Consequently, the proposed dynamic toll scheme is believed to be effective from both traffic and economic points of view.

Model-Based Dynamic Toll Pricing: An Overview

“…- [35] general NW bi-level opt. UE UE - [36] two-layer NW bi-level opt. macro (MFD) UE micro (VISSIM) […”

“…In [35], a bi-level dynamic second-best toll pricing model is solved through a relaxation scheme consisting in the conversion of the bi-level formulation into a single level nonlinear programming problem, which is then solved iteratively. In [36], the genetic algorithm and the method of successive average are used to solve a two-layer network dynamic congestion pricing problem. In [37], a hybrid self-adaptive gradient projection (SAGP) and artificial bee colony (ABC) algorithm are employed to solve a bi-level dynamic congestion tolling problem, with the SAGP solving the lower level and ABC for the upper one.…”

“…The Macroscopic fundamental diagram, also called Network Fundamental Diagram, introduced by [102] to model networks traffic dynamics on an aggregate level by considering relations between space-mean traffic flow and density in urban regions, is adopted in [18,19,36,50,51].…”

Impact of Accurate Detection of Freeway Traffic Conditions on the Dynamic Pricing: A Case Study of I-95 Express Lanes

“…Moreover, Yang et al [ 40 ] proposed a mathematical model which can be calibrated with ease to solve the dynamic pricing problem for distance-based toll collection systems. Some studies utilized the network (aka Macroscopic) fundamental diagram to develop area-based pricing schemes [ 41 , 42 , 43 ].…”

Identifying the combined effect of shared autonomous vehicles and congestion pricing on regional job accessibility

“…(Zhong, Xiao, Bushell, & Sun, 2017). The purpose of road congestion pricing is to adjust the traffic flow on the crowded roads during peak hours, so as to reduce road traffic congestion (Pigou, 1920;Yang & Huang, 1998;Zhang & Yang, 2004;de Palma & Lindsey, 2011;Meng, Liu, & Wang, 2012;Wei & Sun, 2018). The introduction of SAVs has brought new challenges to the studies of traditional road congestion pricing.…”