Using In-Vehicle Data to Enhance Real Time Journey Time Prediction

Case Study

This innovative project developed a connected vehicle data feed emulator which combined with existing connected infrastructure data feeds to assess how this data can enhance the prediction of journey times in real time. This was a DfT sponsored project under a T-TRIG grant, working in partnership with Arup

While there is an awareness of the concept of connected vehicles and the potential for consuming in-vehicle data sources, there is a lack of currently available standardised data. As a result, potential consumers have limited knowledge of exactly what in-vehicle data attributes would be available and what financial, operational, environmental and safety benefits this data could unlock in terms of enhanced ITS solutions and applications.

In response, this project delivered the following:

· Developed a connected ITS (C-ITS) data emulator that simulates incoming data feeds from in-vehicle sources and combines them with existing real world data feeds from ITS infrastructure, for example Highways England Motorway Incident Detection and Automatic Signalling (MIDAS) data. This comprised a working emulator feeding a web dashboard application used to support further analysis and visualisation of the benefits of in-vehicle data.

· Investigated the potential for using in-vehicle data to enhance real time journey time prediction algorithms.  These enhanced predictions were then visualised on a web dashboard application.

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