A scaled and sustainable demand responsive transport service
Lead Research Organisation:
KING'S COLLEGE LONDON
Department Name: Engineering
Abstract
Private mobility has a high carbon footprint due to the manufacturing, use, storage and disposal of vehicles. Private cars spend 96% of their time idle and were responsible for 60.7% of total CO2 emissions from road transport. To reduce CO2 emissions while mitigating societal loss, linking poorly served geographies and alleviating the challenges of elderly and disabled to afford mobility, this research proposes the development of the mathematical tools needed to deliver sustainable, shared mobility, specifically a Demand Responsive Transport Service (DRTS).
We will design novel algorithms that optimise the routing and scheduling integrated with dynamic pricing of DRTS. Solving these large-scale hard combinatorial optimisation problems, in real time, will enable a transformation of DRTS, part of the emerging sector of scaled shared transport solutions, encouraging increased take up of shared mobility. DRTS allows passengers to book a door-to-door service requesting pick up or drop off times, much like a taxi, but sharing a vehicle with other passengers that may be collected or dropped off along the route. Similar services, such as Dial-a-Ride, exist to meet specific needs but they are reduced in scope and heavily subsidized by local councils and the Department for Transport. They lack route planning flexibility and cannot manage high demand. At scale, with optimized dynamic pricing and routing, realistic demand forecasts, informed accurate behavioural models, and incentivised by policies that enhance their acceptance and induce voluntary behaviour changes, DRTS would be financially viable and more sustainable than private car use. The original transformative science in the form of efficient, complex optimization algorithms, and the rich understanding of preferences and attitudes towards shared mobility developed in this project will help enable DRTS to be both efficient and cost-effective; thus, promoting shared mobility and significantly reducing CO2 emission of local travel.
This project will integrate three important scientific components to deliver an attractive, flexible, low-carbon DRTS.
1) An effective efficient scheduling and routing optimisation algorithm for a fleet of vehicles of different types that can provide instant accept/reject decisions on journey requests. In order to do this effectively, the algorithm needs to anticipate potential future demand and be continuously globally optimising schedules across the fleet in the background.
2) New revenue management formulations that allow the prices of journeys to be changed dynamically, with prices dependent on journey length and service quality; thus, supporting the financial sustainability of the service.
3) A rich understanding of customer behaviour and preferences, which will be obtained by running surveys and focus groups and using the data collected to build choice models, describing how potential passengers make decisions. These models will support service design and motivate behaviour changes.
Combining these three components of work comprehensively addresses the practical challenge and advances an exciting new interdisciplinary research area for shared green transportation. The algorithmic approach also has the potential to be adapted to electric and autonomous vehicles in the future.
We will design novel algorithms that optimise the routing and scheduling integrated with dynamic pricing of DRTS. Solving these large-scale hard combinatorial optimisation problems, in real time, will enable a transformation of DRTS, part of the emerging sector of scaled shared transport solutions, encouraging increased take up of shared mobility. DRTS allows passengers to book a door-to-door service requesting pick up or drop off times, much like a taxi, but sharing a vehicle with other passengers that may be collected or dropped off along the route. Similar services, such as Dial-a-Ride, exist to meet specific needs but they are reduced in scope and heavily subsidized by local councils and the Department for Transport. They lack route planning flexibility and cannot manage high demand. At scale, with optimized dynamic pricing and routing, realistic demand forecasts, informed accurate behavioural models, and incentivised by policies that enhance their acceptance and induce voluntary behaviour changes, DRTS would be financially viable and more sustainable than private car use. The original transformative science in the form of efficient, complex optimization algorithms, and the rich understanding of preferences and attitudes towards shared mobility developed in this project will help enable DRTS to be both efficient and cost-effective; thus, promoting shared mobility and significantly reducing CO2 emission of local travel.
This project will integrate three important scientific components to deliver an attractive, flexible, low-carbon DRTS.
1) An effective efficient scheduling and routing optimisation algorithm for a fleet of vehicles of different types that can provide instant accept/reject decisions on journey requests. In order to do this effectively, the algorithm needs to anticipate potential future demand and be continuously globally optimising schedules across the fleet in the background.
2) New revenue management formulations that allow the prices of journeys to be changed dynamically, with prices dependent on journey length and service quality; thus, supporting the financial sustainability of the service.
3) A rich understanding of customer behaviour and preferences, which will be obtained by running surveys and focus groups and using the data collected to build choice models, describing how potential passengers make decisions. These models will support service design and motivate behaviour changes.
Combining these three components of work comprehensively addresses the practical challenge and advances an exciting new interdisciplinary research area for shared green transportation. The algorithmic approach also has the potential to be adapted to electric and autonomous vehicles in the future.
Publications
Laalaoui Y
(2024)
Enhancing the best-first-search F with incremental search and restarts for large-scale single machine scheduling with release dates and deadlines
in Annals of Operations Research
| Description | Key findings are being summarized for dissemination as a state of practice paper and as a policy brief. Demand Responsive Transport (DRT) has seen a resurgence as a part of the UK's public transport strategy, yet its performance remains highly variable and not fully understood. We developed a three-pillar analytical framework (technology and operations, user behaviour and economics, and governance and policy) to integrate fragmented evidence. Drawing on a comprehensive review of the academic literature, practitioner interviews, industry reports, and international and UK case studies, we synthesised the state of the art and the state of practice to analyse how operational design choices, behavioural responses and regulatory structures interact to shape DRT outcomes. Our findings show that different aspects or functions or DRT should not be considered in isolation, and that its viability depends on the alignment of routing and scheduling logic, user expectations around waiting, detour and price, and the governance frameworks determining funding, integration with fixed public transport, and regulatory constraints. This alignment is key to the success of the service. Successful schemes are often anchored to a hub and serve as a feeder mode, and have clear social or spatial objectives. The paper identifies cross-pillar research gaps and proposes directions for future work on operations, behavioural modelling, and regulation to support equitable, efficient and context-appropriate DRT. |
| Exploitation Route | We are writing up results on the technical work for publication in peer-reviewed journals and are investigating opportunities for future funding to take this work forward. |
| Sectors | Transport |
| Description | We engaged the different stakeholders in car sharing within the UK at two workshops. This gave local councils the opportunity to listen to the challenges and barriers to entry faced by commercial companies operating car sharing. It also helped us to scope the real problems within car sharing. For example, much of the current research in this area concentrates on one-way hires in which cars are picked up and dropped off at different locations, whereas in the UK almost all car sharing companies operate a back-to-base model. The results of the workshops have been compiled into a policy paper that has now been published and made available. We are also in early meetings with a car share operator about implementing some of the methodological findings from the project. The project has led to new MSc and PhD studentships at King's College London, University of Southampton and Lancaster University on the topic of car sharing. |
| First Year Of Impact | 2024 |
| Sector | Transport |
| Impact Types | Policy & public services |
| Description | Exploring the future of demand responsive transport |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Professional Practitioners |
| Results and Impact | This one-day workshop informed the scope and specification of the project. Through engagement with a breadth of stakeholders, the workshop explored current best practices, captured learning from previous and current DRT services, identified research priorities, and established key measures of success. The workshop objectives: · To devise a problem specification for large-scale demand responsive transport · To determine key metrics for the project · To gain an informed understanding of the state of shared services access across the UK · To learn from the experiences of existing services or previous pilot schemes |
| Year(s) Of Engagement Activity | 2023 |