Comparative Evaluation of Road Pricing Schemes: A Simulation Approach (Australian Perspective)

Author:

Munir Tariq1ORCID,Dia Hussein1ORCID,Shafiei Sajjad2,Ghaderi Hadi3

Affiliation:

1. Department of Civil and Construction Engineering, Swinburne University of Technology, Melbourne, VIC 3122, Australia

2. Department of Computer Science and Software Engineering, Swinburne University of Technology, Melbourne, VIC 3122, Australia

3. Department of Management and Marketing, Swinburne University of Technology, Melbourne, VIC 3122, Australia

Abstract

Road network pricing and congestion charging continue to be debated as efficient instruments to address traffic congestion and emissions. For cities where the schemes have not been implemented yet, the impacts of these schemes are typically evaluated using transport simulation models to understand the impacts and design effective solutions before the schemes are deployed. This paper considers a simulation approach for the city of Melbourne in Australia to investigate the potential impacts of road network pricing on reducing private vehicle travel, road congestion, and vehicle emissions. The study uses a dynamic traffic simulation model developed for Melbourne using the AIMSUN modeling tool, which was extended for modeling road user pricing and congestion charging, including considerations and formulations of distance-based, delay-based, joint-distance-and-delay-based, and cordon-based schemes under low-cost, medium-cost, and high-cost regimes. The study’s contributions also include an extension of the modeling framework to include public transport options to allow for providing travelers with the option of choosing an alternative mode of transport if they do not wish to pay. A mesoscopic stochastic route choice modeling approach was adopted to examine the impact of road pricing inside a nominated charging zone within the network. The results showed it would be possible to achieve a reduction of 11% in vehicle count, a 20% reduction in travel time, a 13% reduction in emissions, and a 3% increase in travel speed within the proposed pricing zone under a high-cost pricing scenario. The results also showed a significant reduction in emissions resulting from shifting drivers who are not willing to pay the congestion charge to public transport. When 20% of car drivers shifted to public transport, carbon emissions were reduced by up to 30% and network performance improved by 45%, compared to the baseline scenario without pricing. The findings of this research provide important directions for policymakers in deciding on the type and scope of charging schemes to use and how these could reshape transportation taxation systems by moving away from taxes on vehicles through registration fees and towards user-pay taxations where travelers pay for the amount of travel they do or the pollution and emissions they are responsible for.

Funder

Swinburne University

iMOVE CRC

Cooperative Research Centre’s

Publisher

MDPI AG

Subject

Management, Monitoring, Policy and Law,Renewable Energy, Sustainability and the Environment,Geography, Planning and Development,Building and Construction

Reference48 articles.

1. Munir, T., Dia, H., and Ghaderi, H. (2021). A Systematic Review of the Role of Road Network Pricing in Shaping Sustainable Cities: Lessons Learned and Opportunities for a Post-Pandemic World. Sustainability, 13.

2. Mode shift impacts of optimal time-dependent congestion pricing in large networks: A simulation-based case study in the greater Toronto area;Hasnine;Case Stud. Transp. Policy,2019

3. Understanding public response to a congestion charge: A random-effects ordered logit approach;Zheng;Transp. Res. Part A Policy Pract.,2014

4. Infrastructure Victoria (2020, April 15). Good Move Fixing Transport Congestion: Infrastructure Victoria. Available online: https://www.infrastructurevictoria.com.au/wp-content/uploads/2020/03/Good-Move-fixing-transport-congestion-Infrastructure-Victoria.pdf.

5. Optimal distance- and time-dependent area-based pricing with the Network Fundamental Diagram;Gu;Transp. Res. Part C Emerg. Technol.,2018

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