Completed Engineering Clean Energy

Mobility On-Demand Laboratory Environment (MODLE)

In plain English

AI plain-English summary

A new transport service will combine the convenience of a private taxi with the cost savings of a shared bus, using real-time data to route vehicles where people actually need to go. This matters because cars remain the default choice for many short journeys, especially in congested urban areas where parking is scarce and public transport does not cover the "last mile" between a train station and a final destination. Existing bus services run fixed routes and timetables, which often do not match where and when people want to travel. MODLE fills that gap by offering on-demand, point-to-point shared rides that adapt to changing demand throughout the day. If the service succeeds, it could reduce car dependency in city centres, easing congestion and cutting emissions without forcing people onto rigid bus schedules. The project also tests new business models to keep fares competitive and the service financially sustainable over the long term. For commuters, this could mean a reliable, affordable alternative to driving—one that complements existing bus and rail networks rather than competing with them. The work builds directly on a detailed feasibility study conducted in 2014.

View original technical description
MODLE (The Mobility on Demand Laboratory Environment) will develop, test, and refine a new transport service that combines the convenience of point-to-point journeys with the environmental and cost benefits of shared use. Think of it as a taxi-bus, intelligently routed by real-time and predicted demand. Ultimately the service will usurp the car as the convenient choice in areas dogged by congestion and intense competition for parking space. And, in so doing, it will provide an attractive last-mile solution that complements and augments the use of existing scheduled public transport services, for example bus or rail. The service will also explore the use of new business models to ensure that it is competitively priced at point of use and sustainable in the long- term. The project builds on a detailed feasibility study conducted in 2014.

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Original classification

Collaborative R&D

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