ITS Monday: Edition 30, 2026

Fare enough: A quantitative spatial transport model for transit pricing

A new academic paper, co-authored by Isaac Mann and David Levinson. The abstract:

Standard transport appraisal treats fare regulation in isolation, ignoring how price signals reshape urban geography. We bridge regulatory practice and quantitative spatial economics by constructing a Quantitative Spatial Transport Model (QSTM) for Greater Sydney, endogenising household and firm locations in response to transport shocks.

Our framework extends the canonical quantitative spatial model by endogenising commuter mode choice and internalising network externalities through road congestion and the Mohring effect. Evaluating three public transport pricing interventions, including zero-fare, flat-fare, and fare-cap policies, we find that free transit is welfare-suboptimal, driven largely by operator revenue losses. In our setting, distance-based fares provide a spatial price signal that helps internalise resource costs and discourage sprawl, a signal that uniform flat fares mute. The model demonstrates that allocative efficiency requires a higher user contribution, revealing that weekly fare caps function as implicit subsidies for long-distance commuters.

Higher fares trigger a spatial reorganisation where residents centralise to minimise commuting costs and firms decentralise to access labour. Ultimately, efficient distance-based pricing promotes a compact residential form, encourages polycentric employment, and reduces operator resource costs.

Related iMOVE project:

• Implications of large-scale price reductions in public transport fares

If we can track a steak, we can track a battery

From the Institute of Transport and Logistics Studies’ ‘Thinking outside the box’ series’, authored by Professor John Rose and Dr Andrea Pellegrini. “Electric vehicle batteries carry hidden histories that shape their performance, value, and longevity; tracking them like other valuable assets could change how we understand and use them.”

Related iMOVE articles:

• Electric Vehicles: Info, Projects & Resources
• The Conductor Series: The electrification of transport

Related iMOVE projects:

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

Electric U-turn: ‘Everyone knows someone with an EV’

“More than 100,000 battery-electric cars have joined Australian roads in six months, but experts warn the boom may not be self-sustaining and its future could depend on incentives, taxes, and developments in the Middle East.”

Lea-Anne’s ebike has given her freedom at 67. Now some older Queenslanders may be forced to stop riding

“New nation-first laws require every ebike rider to have a driver’s licence. Some older people may be banned entirely, limiting their movement.”

Strategic analysis meets the black box in public transport network design

A new academic paper, co-authored by Valentina Gómez, Sergio Jara-Díaz, and Andrés Fielbaum.

The abstract:

Public transport network design lies at the intersection of strategic theory and algorithmic complexity. While strategic models offer general insights using simplified representations, and algorithmic approaches solve large-scale problems via heuristics, these two perspectives have largely evolved in isolation.

This paper bridges the gap by enhancing a state-of-the-art genetic algorithm with a theoretically grounded decision rule: the Divisibility Index introduced by Gómez et al. (2026), a strategic quantity that determines whether a public transport line should be split into two. This index is integrated into a genetic algorithm, potentially splitting the candidate lines in every iteration when convenient.

This enhanced method is tested on a stylised city under four distinct demand scenarios, each favouring a different network topology: hub-and-spoke, feeder-trunk, direct-based, and exclusive-based. In all cases, this seemingly minor (yet strategic) tweak leads to significant performance gains: the modified algorithm consistently finds better solutions in a single iteration than the original version does in fourteen. Notably, the resulting networks match expected topologies for each demand pattern. Similar results arise in adapted real-city representations. Those results highlight the potential of combining strategic public transport analysis and complex algorithms − not only to improve solution quality and speed, but also to reveal new strategic insights.