ITS Monday: Edition 39, 2025

Robot air taxis, ferries, fast buses, express trains all part of Moreton Bay council’s vision for 2032

Queensland’s Moreton Bay Council has released its ‘City of Tomorrow’ strategy, and with the 2032 Olympic Games fast approaching there is some big ideas around transport. There’s a link to the strategy document in the article.

Keep an eye out for upcoming iMOVE projects, as there are one or two Olympics-related projects on the way.

Related iMOVE article:

• Drone Transport: Info, Projects & Resources

Don’t plug in your plug-in hybrid? Here’s how the extra emissions add up

A new study of hundreds of thousands of plug-in hybrid electric vehicles (PHEVs) shows most users do not regularly recharge and the vehicles emit almost as much as a petrol car. Australian PHEV users not regularly recharging could add millions of tonnes of planet-heating pollution by 2050, exclusive modelling shows.

Related iMOVE article:

• Electric Vehicles: Info, Projects & Resources

Related iMOVE projects:

• An afterlife ecosystem for electric vehicle batteries
• Leading the charge in bi-directional charging
• Utrecht to Australia: Unlocking scalable, low-cost V2G
• Being a V2G trailblazer: Lessons for mass market adoption

Young people are increasingly being killed or injured on e-bikes. It’s time for governments to act

A new piece in The Conversation, by Milad Haghani, Associate Professor and Principal Fellow in Urban Risk and Resilience, The University of Melbourne. “Australia also has no formal mechanism for recording e-bike fatalities – itself a significant data gap. But the trend is hard to ignore: e-bike crashes involving young riders appear to be an escalating risk.”

Related iMOVE article:

• Active Transport: Info, Projects & Resources

Related iMOVE projects:

• Safer cycling infrastructure: Design and policy
• Modelling cycling investments in regional areas
• OneDock: Supercharging e-micromobility

Coffs Harbour active transport plan open for feedback

“The City of Coffs Harbour is seeking community feedback on its draft Active Transport Plan, which sets out a vision for making it easier to walk, ride, roll and scoot across the region. The draft Active Transport Plan replaces the region’s Pedestrian Access and Mobility Plan and Bike Plan (2014–2019) and will shape active transport in Coffs Harbour for the next decade.”

Related iMOVE projects:

• Impacts & community benefits of a regional active travel network
• Improving Darebin’s streets: The community speaks

Case study: Identifying challenges for Milton Keynes cyclists and providing an evidence-base to improve safety and accessibility across the network

This case study of a project in the UK comes to us from See.Sense, partner with iMOVE on a few projects. “Milton Keynes is one of the UK’s best-known “new towns”, built with innovation at its core. It is especially recognised for its commitment to active travel through the Redway network—over 200 miles of shared-use paths designed to keep people walking, wheeling, cycling, and scooting safely separated from traffic.”

Related iMOVE projects:

• Smart bike lights, data, and improved cyclist safety
• AI-powered data dashboard for cycling safety and planning

A vision for trustworthy, fair, and efficient socio-technical control using karma economies

Another new academic paper, co-authored by Ezzat Elokda, Andrea Censi, Emilio Frazzoli, Florian Dörfler, and Saverio Bolognani.

The abstract:

Control systems will play a pivotal role in addressing societal-scale challenges as they drive the development of sustainable future smart cities. At the heart of these challenges is the trustworthy, fair, and efficient allocation of scarce public resources, including renewable energy, transportation, data, computation, etc..

Historical evidence suggests that monetary control – the prototypical mechanism for managing resource scarcity – is not always well-accepted in socio-technical resource contexts. In this vision article, we advocate for karma economies as an emerging non-monetary mechanism for socio-technical control. Karma leverages the repetitive nature of many socio-technical resources to jointly attain trustworthy, fair, and efficient allocations; by budgeting resource consumption over time and letting resource users “play against their future selves.” To motivate karma, we review related concepts in economics through a control systems lens, and make a case for (a) shifting the viewpoint of resource allocations from single-shot and static to repeated and dynamic games; and (b) adopting Long-run Nash welfare (LNW) as the formalization of “fairness and efficiency” in socio-technical contexts.

We show that in many dynamic resource settings, karma Nash equilibria maximize LNW. Moreover, we discuss implications for a future smart city built on multi-karma economies: by choosing whether to combine different socio-technical resources, e.g., electricity and transportation, in a single karma economy, or separate into resource-specific economies, karma provides new flexibility to design the scope of fairness and efficiency.

Statistical regression-powered optimization methods for path-based congestion pricing at scale

A new academic paper, co-authored by Mingye Luan, Taha Hossein Rashidi, S. Travis Waller, and David Rey.

The abstract:

This study addresses the problem of managing traffic in urban transportation networks via path-based congestion pricing. Path-based congestion pricing policies consist of rewarding or tolling users for their path selections with the objective of mitigating system-wide congestion effects. The design of optimal path-based congestion pricing policies is notoriously difficult due to the large number of paths existing in transportation networks. Hence, both the problems of identifying candidate paths for pricing and that of computing optimal path-based congestion pricing policies are challenging.

This study addresses these challenges by presenting novel statistical regression-powered optimization methods based on machine learning. We consider a bilevel optimization problem where a network planner aims to minimize congestion by providing path-based reward credits to commuters who are modeled as selfish agents minimizing a generalized cost function. We develop a statistical regression-powered heuristic approach to solve this path-based congestion pricing problem at scale.

Our methodology integrates machine learning and optimization techniques to generate representative path sets and to model the relationship between congestion effects and path-based credit allocation. Sampling procedures and feature selection are used to synthesize a training data for a statistical regression model of congestion. Multiple supervised learning approaches are explored. The trained models are embedded in a surrogate optimization problem to determine path credits. An algorithm is designed to find feasible and efficient solutions to the bilevel optimization problem. Numerical experiments are conducted on small to large size networks.

A comprehensive comparison is conducted between statistical regression-powered optimization methods, an exact branch-and-bound algorithm, and a model-based heuristic. The results demonstrate the efficiency and the computational scalability of the proposed statistical regression-powered optimization methods for solving problem instances based on large-scale networks.

Study finds active work commutes lower cancer risk

This study is from the Univerity of Auckland, New Zealand. “While earlier studies had established exercise reduced the risk of a number of cancers, they hadn’t specifically looked at how people travel to work, with active transport a sure way to meet health guidelines for exercise.”