A Lane-Based Congestion Pricing Model for Expressway Toll-Lane Management

Shou Ren Hu, Chih Peng Chu, Ting Wei Wu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Road pricing is one of the methods for mitigating urban and/or intercity traffic congestion problems. This study proposes a lane-based road pricing model where the investigated expressway trunk is classified into two types of lanes: a premium lane and a regular lane(s). The premium lane serves road users with urgent needs and would be willing to pay a higher fee to avoid traffic congestion. The developed model applies the marginal cost pricing principle with an aim to maximize the total social welfare. The numerical analysis results indicate that the social welfare of the lane-based, first-best pricing scheme is higher than that of the no-toll scheme. In addition, the total social welfare is monotonically increasing as the lane-based tolling distance is increased. The findings of this study have implications for the government and/or expressway operators in preparing a desirable congestion management policy.

Original languageEnglish
Title of host publicationInternational Conference on Transportation and Development 2020
Subtitle of host publicationPlanning and Development - Selected Papers from the International Conference on Transportation and Development 2020
EditorsGuohui Zhang
PublisherAmerican Society of Civil Engineers (ASCE)
Pages332-344
Number of pages13
ISBN (Electronic)9780784483169
StatePublished - 2020
EventInternational Conference on Transportation and Development 2020: Planning and Development, ICTD 2020 - Seattle, United States
Duration: 26 May 202029 May 2020

Publication series

NameInternational Conference on Transportation and Development 2020: Planning and Development - Selected Papers from the International Conference on Transportation and Development 2020

Conference

ConferenceInternational Conference on Transportation and Development 2020: Planning and Development, ICTD 2020
CountryUnited States
CitySeattle
Period26/05/2029/05/20

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