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For operators running Demand-Responsive Transit (DRT), the ambition has always been clear: to deliver flexible, accessible public transport in areas where fixed routes are inefficient or non-existent. Yet, as recent reports highlight, many schemes continue to struggle with “under use” and unsustainable cost recovery (BBC News).
For DRT to move beyond short-term pilot status and become a core part of the public transport ecosystem, operators, in partnership with councils and technology providers, must engineer financial viability from the ground up. That means shifting DRT from a high-cost, socially motivated service into a data-driven, commercially efficient one.
At Liiftango, we see financial sustainability not as an abstract goal but as a design principle - one that must guide service configuration, marketing, and daily operations.
In this article we explore:
- Mitigate Risk Through Algorithmic Precision (Simulation)
- Drive Demand Through Community Engagement (Marketing)
- Maximise Efficiency in Real Time (Operational Factors)
From Cost Centre to Sustainable Model
While most public transport services operate with some level of subsidy, the goal for DRT operators should always be to maximise cost coverage - the ratio between fare revenue and operational costs.
Traditional bus services often achieve 15–40% coverage; many DRT pilots have reported figures closer to 5–15% (Arthur D. Little). Closing that gap requires more than incremental improvements - it demands a system-level approach that maximises vehicle efficiency, service relevance, and ridership density.
Below are three key levers DRT operators can pull to shift the financial balance.
1. Mitigate Risk Through Algorithmic Precision (Simulation)
The biggest driver of underperformance in DRT is service design misalignment - deploying too many vehicles, in the wrong zones, at the wrong times. For operators, this translates directly into high OpEx and low utilisation.
Simulation tools offer a low-risk way to get the service right before launch. By modelling demand scenarios, fleet sizes, and routing logic, operators can identify the precise conditions under which a service will meet both mobility needs and financial thresholds.
“Simulations help you test and refine service designs to get the most out of your service. They let you adjust fleets, schedules, and time settings to see how changes affect performance without disrupting real users. This gives you a solid foundation for making smart, data-driven decisions about service delivery and future changes.” — Dr Ben Kaufman, Head of New Mobilities, Liftango
Operational Insight — Lolland, Denmark:
A simulation study in rural Denmark showed that a well-optimised DRT service became more cost-efficient than fixed bus routes once demand density dropped below a defined threshold. Identifying that tipping point provided clear, data-backed justification for redeploying services where DRT performs best, especially when using time windows correctly.
(MDPI: “Sustainability” Journal)
Operational Insight — Porto, Portugal:
In Porto, simulations integrating mobile data and routing algorithms reduced stops by 135% and total distance travelled by 81% while maintaining full coverage. For operators, this means fewer empty kilometres and reduced OpEx - a direct path to improved cost efficiency.
(MDPI: “Sustainability” Journal)
Key takeaway:
Pre-deployment simulation gives operators the insight to design for success, not trial-and-error. It ensures pooling efficiency, cost control, and public confidence from day one.
2. Drive Demand Through Community Engagement (Marketing)
Even a perfectly optimised service will fail if potential users don’t understand or trust it. For operators, sustainable ridership is built on proactive, ongoing engagement - not one-time promotion.
Segmented Outreach and Local Partnerships
Operators can strengthen uptake by collaborating with councils and local organisations to co-design outreach. Segmenting audiences (commuters, students, seniors, carers) allows messaging that speaks to specific needs and lifestyles - a strategy proven effective by the UK’s
Transport Choices Segmentation model.
Case Example — Suffolk, UK:
The Connecting Communities model shows how working with community transport operators creates deeper local trust and higher awareness. By blending flexible fare structures and locally tailored promotion, Suffolk’s model has achieved consistent ridership in low-density areas where conventional bus services failed - GOV.UK: DRT Local Authority Toolkit
Omnichannel Accessibility:
Many DRT users are not digital natives. Ensuring call-centre booking alongside app-based access broadens reach and maximises potential revenue. For operators, inclusion is not just a social good - it’s a financial strategy.
Integration and Visibility:
Integration into regional transport apps and fare systems makes DRT appear as part of the established network - reducing friction and improving trust. Clear vehicle branding and visibility at major bus and rail hubs help accelerate adoption and repeat use.
Key takeaway:
Demand doesn’t emerge organically. It must be cultivated through visibility, trust, and relevance - all of which directly impact utilisation and farebox recovery.
3. Maximise Efficiency in Real Time (Operational Factors)
Even after launch, profitability depends on active optimisation. The most successful DRT operators treat service parameters as living variables - continuously tuned to balance efficiency and passenger satisfaction.
Feeder Service Integration
Positioning DRT as a feeder to mainline transit hubs often yields the best returns. In several UK counties, 15–20% of DRT trips link to rail stations - creating value for both passengers and local transport authorities.
Dynamic Service Tuning
Using virtual stops instead of door-to-door collection typically increases pooling rates and reduces detours. Operators who regularly tune wait times and walking distances can boost ride density without degrading service quality.
Key takeaway:
Financially sustainable DRT operations are those that never “set and forget.” Operators who dynamically adapt parameters based on live performance data consistently achieve higher cost recovery.
Conclusion
For operators and councils alike, the financial fear surrounding DRT is rooted in outdated models. Modern, tech-enabled DRT - designed with simulation, driven by data, and supported by active community engagement is fundamentally changing that equation.
By aligning operational design, marketing, and real-time performance management, DRT operators can break the subsidy cycle and create services that are both socially valuable and financially resilient.
At Liiftango, we partner with operators and local authorities to deliver the data-driven insights and operational tools that make this transformation possible - from simulation and service design to optimisation in the field.
