Real-time personalized toll optimization based on traffic predictions

Road pricing is a traffic congestion management strategy that alters traffic demand and raises funds for transportation supply improvements. Compared to static pricing and reactive dynamic pricing, proactive dynamic pricing is most effective in achieving traffic management objectives, as the toll is based on traffic predictions that incorporate real-time information. We investigate a proactive toll pricing framework where the toll is optimized in real time based on traffic predictions generated by a dynamic traffic assignment (DTA) system. Toll optimization performance relies on accurate predictions, which is backed by the online calibration of the DTA system. We develop enhanced online calibration methodologies featuring a heuristic technique to calibrate supply parameters and improve the prediction accuracy of traffic speed. We test online calibration using real data from a real network consisting of managed lanes and general-purpose lanes.

Name thesis:degree_name

Doctoral

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Civil and Environmental Engineering

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2019

Author dc:creator

• Zhang, Yundi.

Advisor dc:contributor.advisor

• Moshe E. Ben-Akiva and Arun Akkinepally.

• Civil and Environmental Engineering.

Statement dc:rights

• MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/124189

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/124189