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Predictive distance-based road pricing — Designing tolling zones through unsupervised learning by Lentzakis, Antonis F. (author);Seshadri, Ravi (author);Ben-Akiva, Moshe (author) is a Social Sciences article available to read on EtoBox.

What is Predictive distance-based road pricing — Designing tolling zones through unsupervised learning about?

Congestion pricing is a standard approach to mitigate traffic congestion in a number of urban networks around the world. The advancement of satellite technology has spurred interest in distance-based congestion pricing schemes, which obviate the need for fixed infrastructure such as gantries that are used in area- and cordon-based pricing. Moreover, distance-based pricing has the potential to more effectively manage traffic congestion. In the context of distance-based congestion pricing, we propose the use of sparse subspace clustering methods employing Elastic Net optimization (SSCEL) and Orthogonal Matching Pursuit (SSCOMP), as well as two hierarchical density-based clustering methods, (OPTICS, HDBSCAN*) for the derivation of tolling zones. These tolling zones are then used within a simulation-based framework for real-time predictive distance-based toll optimization to examine network congestion and performance of the tolling schemes. Within this framework, for a given definition of tolling zones, tolling function parameters are optimized in real-time using a simulation-based Dynamic Traffic Assignment (DTA) model. Guidance information generation is integrated into the predictive

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Author

Lentzakis, Antonis F. (author);Seshadri, Ravi (author);Ben-Akiva, Moshe (author)

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Social Sciences