With the advent of urban sprawl, managing congestion is at the heart of major cities’ operational challenges. The prediction of travel time variation on congested corridors in urban and peri-urban networks is critical to inform travellers of traffic conditions and assist them in their trip planning. Travel time predictions are also extremely relevant to forecast and assess network performance and anticipate congestion episodes through the use of variable message signs. Further road capacity reductions caused by incidents, road occupancy projects (e.g. maintenance operations) or special events may also impact travel time and should be accounted for in the travel time forecasting process.
The focus of Application 1 was to develop a real-time travel time prediction software to provide an effective and robust framework for predicting travel time during peak and off-peak periods at a corridor-level. The developed travel time prediction software innovates on the available forecasting tools by allowing dynamic road capacity variations to be tracked in real-time and providing adapted travel time estimations. This is possible thanks to an innovative modelling framework which incorporates traffic flow dynamics within the travel time forecasting process instead on relying exclusively on historical data. This project is envisioned to contribute towards a framework for intelligent transportation systems.