Fares, Funding, and Future: A Passenger-Centric Perspective on Rail Subsidy

The recent figures from the Office of Rail and Road (ORR) show a promising trend: passenger revenue has risen 8% to £11.5 billion, contributing to a reduction in government subsidy to £11.9 billion. With passenger journeys approaching pre-pandemic levels (1.7 billion), this recovery is a vital step for the industry's financial health.

However, revenue still lags 12% behind pre-pandemic levels. As we look to the future, ensuring this momentum continues will require a nuanced approach to fare strategy rather than simple across-the-board increases.

Balancing Fares and Ridership

Public transport demand is highly sensitive to cost. My research suggests that fare levels are a primary driver of mode choice. While reducing subsidy is essential, relying primarily on fare increases can be challenging, as it requires careful calibration to avoid impacting ridership growth.

In my study "Bimodal transit design with heterogeneous demand elasticity under different fare structures", I observed that demand elasticity varies significantly across different groups. Commuters and leisure travelers respond differently to price signals. This indicates that broad fare adjustments might miss opportunities to maximize revenue from less price-sensitive segments while retaining those who are more cost-conscious.

The Role of Passenger Behavior

There is significant potential in designing passenger-centric strategies that respond to how travellers actually make decisions.

1. Understanding Traveller Preferences

In my recent work on Fare Optimization, I demonstrated that effective fare structures must account for the complex trade-offs passengers make between cost, time, and comfort. Strategies that recognize these behavioral nuances—rather than treating all passengers as identical—can significantly improve system efficiency and user satisfaction.

2. Behavioral Incentives

Fare policy can also support crowd management. In Modeling and optimizing a fare incentive strategy to manage queuing and crowding in mass transit systems, I analyzed a "surcharge-reward" scheme. Offering small incentives for travelling just outside the peak shoulder can help smooth demand curves. This benefits the passenger through reduced crowding and supports the operator by optimizing the use of existing capacity, potentially moderating the need for costly peak-infrastructure expansion.

A Multimodal Perspective

Ultimately, rail fares exist within a wider ecosystem. As explored in my study on "Joint optimization of bimodal transit networks", seamless integration between rail, bus, and active travel is key. Balancing rail fares with the cost of other modes ensures that our pricing strategies continue to support broader decarbonization goals.

Conclusion

The reduction in government subsidy is a positive fiscal indicator. Sustainably building on this progress lies in continued innovation in fare structure. By applying bi-level optimization techniques to our pricing strategies, we can seek a financial equilibrium that supports the operator while delivering value to the passenger.

References

• Jiang, Y., et al. (2025). Fare Optimization for the Demand Adaptive Paired-Line Hybrid Transit System. Transportation Research Part B.
• Jiang, Y., Szeto, W.Y., Long, J., Han, K. (2020). Modeling and optimizing a fare incentive strategy to manage queuing and crowding in mass transit systems. Transportation Research Part B: Methodological, 138, 247-271.
• Jiang, Y., et al. (2023). Bimodal transit design with heterogeneous demand elasticity under different fare structures.