ITS Monday: Edition 34, 2026

Can building more roads make traffic worse? Here’s the mathematics behind it

Milad Haghani, Associate Professor and Principal Fellow in Urban Risk and Resilience, at the University of Melbourne, writing for The Conversation.

“Tackling traffic congestion is one of the biggest challenges facing large cities around the world. When it comes to reducing congestion, one solution almost instinctively comes to mind: build more roads or widen existing ones.

But the science of transport economics has shown for decades that increasing road capacity does not necessarily translate into smoother traffic. In fact, under certain conditions, it can do the opposite.

Here’s how it works.”

Related iMOVE articles:

• Traffic congestion: Info, Projects & Resources

How should a kilometre charge be structured? Evidence from a revenue-neutral choice experiment

This new academic paper is downloadable from the link. It is co-authored by Michiel C.J. Bliemera and Matthew J. Beck, both of the Institute of Transport and Logistics Studies at the University of Sydney.

The abstract:

Fuel excise revenue is falling as vehicles become more fuel efficient and electric, and governments are considering replacing fuel excise and vehicle taxes with a charge per kilometre.

We report findings from a stated choice experiment among 1,245 adults in New South Wales, Australia, in which respondents choose between alternative kilometre charging structures with the same cost to an average driver as the existing charges. The existing charges are compared with kilometre charges whose rates may vary by time of day and between the first 5,000 and additional kilometres per year, and which may or may not include tolls.

Multinomial logit and latent class models show that respondents prefer a flat rate to peak/off-peak variation, prefer paying tolls separately, and reject a higher rate for the first 5,000 kilometres. We identify four classes: respondents who prefer the existing charges (14%); respondents who welcome differentiated rates, mostly under 55 and more often men (24%); a large group, dominant among older respondents, that prefers a simple kilometer charge (43%); and a group without clear preferences (18%).

Weighted to the voting population, 7079% of voters are predicted to prefer any of the 56 designed kilometre tariffs over existing charges. Almost two-thirds of drivers would pay less under a flat kilometre charge, but we find no evidence that these drivers are more likely to choose it. A flat rate with tolls paid separately is preferred to every differentiated structure, except a 30% discount on the first 5,000 kilometres, which is weakly preferred to it.

These results suggest that a reform is most likely to gain support if it starts with a flat kilometer charge, keeps tolls separate, and introduces time differentiation, if at all, at a later stage.

Who is inclined to embrace green options in on-demand mobility?

A new academic paper, vo-authored by Andrea Pellegrini and Andres Fielbaum, as above both are from the Institute of Transport and Logistics Studies, at the University of Sydney.

The abatract:

Shared on-demand mobility can be made more environmentally friendly, but this involves trade-offs in comfort and cost: electric vehicles may require higher fares due to charging time, while walking segments can reduce vehicle-kilometres-travelled at the expense of door-to-door convenience.

This study examines travellers’ willingness to choose green shared mobility options using an integrated choice and latent variable model estimated from a discrete choice experiment conducted in Sydney, Australia. Respondents chose among three shared mobility options differing in walking requirements, travel times, emissions, and price, and answered attitudinal questions capturing car safety, pro-walk orientation, time sensitivity, and variety-seeking behaviour. Willingness-to-pay values are computed for five distinct user profiles.

Results indicate that (i) users are generally willing to pay to reduce emissions; (ii) personalised options yield greater emission reductions than uniform policies; and (iii) regular public transport users show higher willingness to walk, highlighting potential synergies between on-demand mobility and transit.

Related iMOVE article:

• Sustainable Transportation: Info, Projects & Resources

Related iMOVE project:

• Policy paths encouraging behaviour shift for sustainable mobility

Learning from common knowledge: A multi-agent cooperative ramp merging framework for decentralized autonomous vehicles

And last article this week is another new academic paper, co-authored by Ang Ji, Zhennan Ma, Hongtai Yang, Asad J. Khattak, and David Levinson.

The abstract:

This paper proposes a multi-agent reinforcement learning (MARL) framework that combines an offline shared pool of information (SPI) with hypernetworks to enable cooperative on-ramp merging among high-level autonomous vehicles (AVs), leveraging common knowledge for spontaneous cooperation.

First, we discuss the construction method of the common knowledge base–SPI from two approaches: model- and data-driven methods. Then, we design an on-board fine-tuning algorithm that enables each agent to refine its policy from local observations without any communication protocols. Based on the above advances, we develop a hybrid SPI-based on-ramp merging strategy driven by the physical model and human driving data.

Finally, the proposed framework is evaluated in simulated peak- and off-peak traffic scenarios calibrated by the Zen trajectory dataset. The results demonstrate improved traffic efficiency outcomes and fewer collision occurrences than several baselines under the evaluated conditions. The findings reveal possible research directions for future work and provide insights for the application of the decentralized-training-decentralized-execution framework in autonomous driving.

Related iMOVE articles:

• Autonomous Driving: Info, Projects & Resources
• Autonomous Driving Technology: Info, Projects & Resources

Related iMOVE projects:

• CAVs and the environment: A cleaner future?
• CAVs and Australians: Attitudes, perceptions, preferences
• Safely deploying automated vehicles on Australian roads