Analytics for Demand Responsive Online Intelligent Transport (ADROIT)

Sustainable transport is central to sustainable development, providing access to work, vital services, leisure, friends and family while enabling safe mobility at a reduced environmental impact. Providing affordable and flexible transport can reduce inequalities and empower those less able to access work and urban life. Effective transport is central to building a fairer society and low-carbon transport is central to a sustainable future. 

While many are fortunate to have access to a car, 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 are responsible for 60.7% of total CO2 emissions from road transport. However, as a society, we are attached to the convenience and comfort of car ownership.  

The project seeks to understand and build models for services that reduce CO2 emissions while mitigating societal loss. Moreover, it will support linking poorly served geographies and alleviating the challenges of the elderly and disabled to afford mobility.

This research proposes the development of the mathematical tools needed to deliver sustainable, flexible, shared mobility. The service is a hybrid of a bus and a taxi, where rides can be requested in a similar way to a taxi, while the vehicle may be servicing other customers simultaneously along the route. Specifically, this is known as a Demand Responsive Transport Service (DRTS).
 

Impact


We anticipate that the project will: 

•    Provide insights into behaviours and attitudes towards shared mobility and the service experience needed to incentivise car owners to use a DRT service 

•    Develop effective scheduling and routing optimisation algorithms that can run in real-time and provide efficient schedules across a mixed fleet of vehicles. 

•    Advance methodologies around integrating dynamic pricing into routing and scheduling services. 

•    Deliver a large-scale simulation that can be used to test a wide range of user scenarios and support implementation decisions and understanding of investment scenarios and volume of demand required to make the service sustainable. 

•    Inform transportation policy with respect to initiatives designed to reduce congestion and pollution in cities. For example, how to price clean air tariffs and parking charges.

Project website

https://business.leeds.ac.uk/directories0/dir-record/research-projects/2175/analytics-for-demand-responsive-online-intelligent-transport-adroit