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Projects: Projects for Investigator
Reference Number	EP/X020835/1
Title	A scaled and sustainable demand responsive transport service
Status	Started
Energy Categories	Energy Efficiency(Transport) 5%; Not Energy Related 95%;
Research Types	Basic and strategic applied research 100%
Science and Technology Fields	SOCIAL SCIENCES (Economics and Econometrics) 10%; SOCIAL SCIENCES (Town and Country Planning) 5%; SOCIAL SCIENCES (Business and Management Studies) 25%; SOCIAL SCIENCES (Sociology) 10%; PHYSICAL SCIENCES AND MATHEMATICS (Statistics and Operational Research) 50%;
UKERC Cross Cutting Characterisation	Not Cross-cutting 100%
Principal Investigator	Professor R R M'Hallah Engineering King's College London
Award Type	Standard
Funding Source	EPSRC
Start Date	04 September 2023
End Date	03 September 2026
Duration	36 months
Total Grant Value	£932,145
Industrial Sectors	Transport Systems and Vehicles
Region	London
Programme	NC : Engineering
Investigators	Principal Investigator	Professor R R M'Hallah , Engineering, King's College London (99.997%)
	Other Investigator	Dr C Calastri , Institute for Transport Studies, University of Leeds (0.001%) Professor J Bennell , Leeds University Business School (LUBS), University of Leeds (0.001%) Dr CSM Currie , Sch of Mathematical Sciences, University of Southampton (0.001%)
	Industrial Collaborator	Project Contact , Mott Macdonald UK Ltd (0.000%) Project Contact , Leeds City Council (0.000%) Project Contact , West Yorkshire Combined Authority (0.000%) Project Contact , Busreinvented.com (0.000%) Project Contact , Ctr for Urban Sci and Progress London (0.000%)
Web Site
Objectives
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 motivatebehaviour 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	(none)
Final Report	(none)
Added to Database	05/04/23