Demand Responsive Transport


Demand Responsive Transport (DRT) are a flexible form of shared transport and infrastructure where the day-to-day service provision is shaped by the demand of the users. DRT does not follow a fixed timetable or route. Instead the most efficient route is calculated in response to user requests. DRT has characteristics of both buses and taxis but can take the form of a broad range of vehicular transport solutions; from familiar ‘dial-a-ride’ services typically booked by phone, to more recent dynamic applications that allow journeys to be booked through an application, adjusting the route to accommodate new pickup requests in almost real-time.

DRT is not a new concept. In developing countries, informal ‘paratransit’ systems are a significant form of urban transport. In developed countries, DRT had previously been employed as a rural community transport solution, used where conventional services do not exist, often due to their financial instability and requirement for heavy subsidisation. In response to the massive uptake of phone-based demand responsive solutions (e.g. Uber, Lyft, DiDi), cities are beginning to investigate how they can utilize DRT to improve shared use of public infrastructure, and the attractiveness and cost-efficiency of their public transport services.

Low frequency, low patronage transport services, which generally occur in sparsely populated areas or are due to under-developed transport service networks, must be heavily subsidised to maintain their service. These characteristics serve to encourage uptake of privately-owned cars for journeys. Due to the financial burden on local authorities, these services are at risk of being withdrawn. This would exacerbate the lack of accessibility to these areas with only those who can use privately-owned cars able to access the area, and in-turn increasing the physical road infrastructure load.

DRT can be used to solve an array of mobility related issues. They can act as first- and last-mile passenger and freight solutions particularly when combined with electrical infrastructure, as electric vehicles are suitable for short routes (see also the Transition to Electric Vehicle Transport Networks and Electric Charging infrastructure use cases). They can replace poor performing low frequency, low patronage services by shuttling users to the wider public transport network.

One prospective technological advance that is expected to greatly impact DRT are autonomous vehicles (AVs). With the introduction of AVs, user fares are anticipated to sharply decline as driver costs currently make up approximately 50% of DRT operational expenditure[1].


Improving efficiency and reducing costs:

  • Replace low patronage routes with a dynamic service that responds to demand, which can reduce the overall costs of service delivery
  • Reduce road deterioration and maintenance costs by increasing the number of economical vehicles on the road in place of heavier low patronage vehicles
  • Reduce need for additional capital investment as use of existing road infrastructure can be optimized

Enhancing economic, social and environmental value:

  • Reduce emissions by replacing inefficient fixed services with flexible, adaptable services that optimize service use
  • Encourage mode shift from private car usage with convenient shared mobility options that are tailored to the user’s specific needs



Legislation and regulation: Strategic planning must be undertaken to decide on the provision of projects that will solve infrastructure gaps and future development requirements (modifying the use of parking, stops, stations, grid; monetising public infrastructure usage for private providers). Implement outcome-based regulation for the delivery of DRT services with a view to serve the integration policy and development/optimisation of mass transit infrastructure.

Transition of workforce capabilities: Governments need strategic network planning, commercial and project management capabilities to understand and address the infrastructure gaps (physical, electrical and digital) to integrate such solutions with the provision of existing transport and energy services and develop the commercial framework to support the transition to outcomes-based delivery.


Implementation risk

Risk: To ensure its viability, a DRT service must maintain its market penetration to be seen by the user as a ‘go to’ option instead of a temporary service. The user must be able to rely on the service. Long wait times or periods without service can lead users to favour alternate services.

Mitigation: To mitigate, the service offering should be flexible enough to be scaled up in response to growth in demand and to avoid spreading the fleet too thinly over a geographical area. The scaling up of the fleet will enable better optimization of vehicles, enabling users travelling the same direction to be allocated to a single vehicle. This will enable the high vehicle occupancy necessary to make the service cost effective.

Social risk

Risk: The public understanding of DRT is currently limited. Users have different expectations of the service or no knowledge at all.Current examples of DRT are typically implemented as niche services. They do not take into consideration integration with other modes in the wider transport network.

Mitigation: This integration with other modes creates a unique value offering for public and private stakeholders. Therefore, is it important that legislation does not hinder growth for innovations in this space. Governments should lead conversations with stakeholders to encourage collaboration for the public good. Communication and consultation with the public about the service and its role for the community can be an effective way of capturing user expectations and encouraging uptake.

Safety and (Cyber)security risk

Risk: Dynamic demand management relies on connected technologies to capture the relevant demand data. Therefore, risks related to data privacy and data sharing exist.

Mitigation: Those risks can be addressed through appropriate regulations on data privacy which ensure a secure system that protects user data privacy.



Example: MOIA Hamburg

Implementation: A co-operation between the city of Hamburg and Volkswagen, which now has a fleet of 450 EVs and approximately 7,000 stopping points. MOIA now also operates in Hanover.

Cost: An average ride costs between €6 and €7 per person.

Timeframe: The service has been operating since 2018. A major extension of the operating area and fleet was announced for March 2020.

Example: Kutsuplus, Helsinki

Implementation: Research started at Aalto University led to a pilot project that started in 2012.

Cost: Kutsuplus was working towards being an economically feasible service. The average fare was €5 (~US$5.50) with an estimated two-thirds of their costs related to drivers’ wages. Despite positive developments the small-scale operation needed substantial subsidies.

Timeframe: Launched in October 2012, Katsuplus ultimately ceased operations at the end of 2015, as it was deemed the cost to taxpayers was too high.The popular service was hindered by the investment cost required to scale up operations in order to optimize trips across the fleet.

Example: Beeline, Singapore

Implementation: Beeline was developed by the Infocomm Development Authority and Land Transport Authority. It was an application that enabled users to pre-book rides on express routes operated by private bus operators. The project aimed to explore how transport networks could be made to adapt to changing commuter demand.

Cost: The average fee per ride is between S$5 and S$6. Driver costs account for a significant amount of the operational costs.

Timeframe: Launched in 2015, with operations ultimately ceasing in January 2020. Like with the above examples, achieving the required fleet optimization requires a significant scale up in fleet size and geographical service offering. The LTA instead decided to redirect resources to their core transport offering in 2020, citing the high technology costs associated with the Beeline service. 

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