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Details of Grant 

EPSRC Reference: EP/X020835/1
Title: A scaled and sustainable demand responsive transport service
Principal Investigator: M'Hallah, Professor R R
Other Investigators:
Bennell, Professor J Currie, Dr CSM Calastri, Dr C
Researcher Co-Investigators:
Project Partners:
Busreinvented.com Ctr for Urban Sci and Progress London Leeds City Council
Mott Macdonald West Yorkshire Combined Authority
Department: Engineering
Organisation: Kings College London
Scheme: Standard Research
Starts: 04 September 2023 Ends: 03 September 2026 Value (£): 932,145
EPSRC Research Topic Classifications:
Mathematical Aspects of OR Transport Ops & Management
EPSRC Industrial Sector Classifications:
Transport Systems and Vehicles
Related Grants:
Panel History:
Panel DatePanel NameOutcome
08 Feb 2023 Engineering Prioritisation Panel Meeting 8 and 9 February 2023 Announced
Summary on Grant Application Form
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.

Key Findings
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Potential use in non-academic contexts
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