2019
DOI: 10.1177/0361198119850135
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Dynamic Toll Pricing using Dynamic Traffic Assignment System with Online Calibration

Abstract: The paper presents a toll pricing methodology using a dynamic traffic assignment (DTA) system. This methodology relies on the DTA system’s capability to understand and predict traffic conditions, thus enhanced online calibration methodologies are applied to the DTA system, featuring a heuristic technique to calibrate supply parameters online. Improved offline calibration techniques are developed to apply toll pricing in a real network consisting of managed lanes and general purpose lanes. The online calibratio… Show more

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Cited by 12 publications
(9 citation statements)
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“…Minimizing total delay was the goal of the dynamic pricing schemes proposed by [ 48 , 49 ]. Other flexible pricing approaches were introduced in [ 50 , 51 , 52 ] to minimize delay, maximize revenue, or achieve levels of specified combinations of delay and revenue.…”
Section: Literature Reviewmentioning
confidence: 99%
“…Minimizing total delay was the goal of the dynamic pricing schemes proposed by [ 48 , 49 ]. Other flexible pricing approaches were introduced in [ 50 , 51 , 52 ] to minimize delay, maximize revenue, or achieve levels of specified combinations of delay and revenue.…”
Section: Literature Reviewmentioning
confidence: 99%
“…They applied the tolling methodology on a synthetic corridor network with two gantries where the tolls need to be optimized. Zhang et al ( 16 ) also study a a managed-lane setting and demonstrate the impact of online calibration within a predictive toll optimization framework. More recently Hashemi and Abdelghany.…”
Section: Literature Reviewmentioning
confidence: 99%
“…First, it has the potential to facilitate more accurate prediction, which is vital to directing real-time operation. Second, it could direct personalized tolling policies ( 28 , 29 ) in similar ways to the personalized travel incentives discussed in the literature ( 30 , 31 ).…”
Section: Literature Reviewmentioning
confidence: 99%