Dynamic optimal congestion pricing in multi-region urban networks by application of a Multi-Layer-Neural network. (January 2022)

• Record Type: Journal Article
• Authors: Genser, Alexander; Kouvelas, Anastasios
• Is Part Of: Transportation research. Volume 134(2022)
• Publication Date: 2022-01
• Subjects: Multi-region-network modeling -- Dynamic optimal pricing -- Dynamic system optimum -- Linear rolling horizon optimization -- Machine learning -- Deep neural networks; Transportation -- Periodicals; Transportation -- Technological innovations -- Periodicals; 388.011
• DOI: 10.1016/j.trc.2021.103485
• Languages: English
• ISSNs: 0968-090X

Abstract

Abstract: Traffic management by applying congestion pricing is a measure for mitigating congestion in protected city corridors. As a promising tool, pricing improves the level of service in a network and reduces travel delays. However, previous advancements in pricing research that are responsive to the prevailing regional traffic conditions did not consider real-time applications and the effect on users' route choices. This work uses real-time dynamic pricing's influence and predicts pricing functions to aim for a system optimal traffic distribution. The framework models a large-scale network where every region is considered homogeneous, allowing for the Macroscopic Fundamental Diagram (MFD) application. We compute Dynamic System Optimum (DSO) and Dynamic Route Choice (DRC) of the macroscopic model by formulating a linear optimization problem and utilizing the Dijkstra algorithm and a Multinomial Logit model (MNL), respectively. The equilibria allow us to find an optimal pricing methodology by training Multi-Layer-Neural (MLN) network models. We test our framework on a case study in Zurich, Switzerland, and showcase that (a) our neural network model learns the complex user behavior and (b) allows predicting optimal pricing functions. Results show a significant performance improvement when operating a transportation network in the DSO and highlight how dynamic pricing influences the user's route choice behavior towards the system optimal equilibrium. Highlights: Effectiveness assessment of dynamic pricing in large-scale urban networks by computation of transportation equilibria. Derivation of the Dynamic System Optimum by solving a linear optimal route guidance problem. Training of neural network models to learn the users route choice behavior. Utilization of neural network models to predict generalized costs and derive optimal pricing functions.