Public Funding for Trapeze Group (UK) Limited

The ChoiceRail project is developing a new generation of multimodal journey planners that combine real-time and road, rail and parking data to give true end-to end journey planning. It incorporates a highly innovative journey planning algorithm capable of finding diverse routes that balance different considerations such as distance, time, cost, reliability and congestion. Its design is informed by research studies into user behaviour for making travel decisions and user interface requirements. The resulting journey plans can be used both by web , mobile and in-car (SatNav) applications. In particular it aims to support Park and Ride and ‘Kiss & Ride’ options. The goal of the project is to make it much easier for the travelling public to find the best rail journeys taking into account road options to access differnt stations and the real-time conditions.

Real-time travel alerts are one of the most compelling ways of informing people how to make the best travel choices, and can also be used to encourage modal or route shifts to reduce congestion. Providing highly specific, relevant, personally tailored information at the point of decision empowers the traveller to make the best assessment of their current options. However, to be effective, travel alerts have to be almost effortless to use. Current systems typically require users to configure their travel patterns in advance, requiring at least some investment of time. Furthermore, in real life there is often considerable complexity and variability in behavioural patterns of personal travel, which is not captured by one simple configuration. The goal of this project is to make travel alerts both self-configuring and richer, based on automated observation of the actual behaviours of each traveller. By continuously tracking travellers' location, and using other data sources that reveal location or intent, such as journey planning queries or calendar entries, a personal travel map can be built up, with regular journeys and patterns of variation inferred by the system. This data can then be used to dynamically configure subscriptions and switch them on and off to match the actual behaviour of the user on a particular day. SpamJam is innovative in bringing together advances in the capabilities of mobile devices with the latest generation of alert engines to create a learning, adaptive system that can dynamically manage travel alerts. Project partners are Kizoom, O2, Imperial College, Dynamical Systems Research and Tactical Systems Designers.

Awaiting Public Summary