ITS Monday: Edition 40, 2025

EV ‘charging deserts’ in regional Australia are slowing the shift to clean transport

Professor Hussein Dia writing in The Conversation. “If you live in a big city, finding a charger for your electric vehicle (EV) isn’t hard. But drive a few hours in any direction and the story changes.

For most regional Australians, the nearest public charger is still a detour, not a stop on the way. And for anyone planning a long road trip, the chargers along the route fade for hundreds of kilometres at a time.”

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

A Physics-Informed Deep learning framework for traffic state estimation in signalized arterial roads

A new academic paper, co-authored by Wanuji Abewickrema, Mehmet Yildirimoglu, and Jiwon Kim. The abstract:

Efficient traffic management and effective signal planning can decrease intersection congestion and minimize delays. While Physics-Informed Deep Learning (PIDL) has shown promise in highway contexts, its application to urban arterial roads remains underexplored. This paper presents a structurally enhanced PIDL framework designed for traffic state estimation (TSE) on signalized arterials using partially observed high-resolution traffic data.

The proposed model integrates a diffusively corrected PDE to capture driver behavior and incorporates spatial, signal-phase, and congestion-aware reweighting mechanisms into the loss function to improve learning around queue-prone regions. Three FD models–3-parameter LWR, Greenberg, and Newell–are evaluated across two realistic traffic conditions using simulated loop detector and probe vehicle data. A hybrid collocation sampling strategy and FD parameter estimation are employed within the learning architecture.

Results demonstrate the model’s robustness across varying probe penetration rates, data sparsity, and congestion regimes.

Notably, the proposed PIDL consistently outperforms its state-of-the-art counterpart in both estimation accuracy and queue length prediction. These findings highlight the proposed framework’s suitability for real-time arterial traffic operations and signal optimization.

Related iMOVE article:

• Traffic Congestion: Info, Projects & Resources

Slower speeds make safer streets | Infrastructure Victoria

Nice job wit this short video from Infrastructure Victoria. “Infrastructure Victoria recommends reducing speed limits to 30km/h on local streets, starting in places that children often visit, including around schools, playgrounds, childcare centres and kindergartens. ”

Reducing traffic with “carrots”: A review of the evidence

A new academic paper, co-authored by Maria Börjesson and Jonas Eliasson. The abstract:

Reducing traffic volumes is one way to reduce carbon emissions from the transport sector. Since increasing driving costs is often met with public resistance, high hopes are often pinned on the possibility to reduce traffic volumes by non-coercive policy measures, or “carrots”. Such measures include improvements of alternative modes, strategies that affect urban forms, and “soft measures” that aim to affect behaviour by providing information or changing norms and attitudes.

This paper reviews the empirical evidence regarding such measures, focusing on their potential to reduce aggregate road traffic volumes in a national perspective. While such measures can yield significant other benefits, and may also reduce traffic volumes locally, our general conclusion is that their effects on aggregate traffic volumes appear small, especially from a climate policy perspective where emissions need to be cut radically and rapidly.

While they are often motivated for several other reasons, overestimating their effects on aggregate traffic volumes may cause complacency, misallocations of scarce public resources and backlashes against climate policy.