QTIP: Quick simulation-based adaptation of Traffic model per Incident Parameters
This work addresses a critical need for reliable traffic prediction during disruptions, which is important for traffic management and safety, though it is incremental as it builds on existing simulation and prediction methods.
The authors tackled the problem of traffic prediction models failing during sudden disruptions like road incidents by introducing QTIP, a simulation-based framework that adapts models in real-time using incident parameters from vehicle signals, resulting in improved prediction accuracy in the first critical minutes of incidents.
Current data-driven traffic prediction models are usually trained with large datasets, e.g. several months of speeds and flows. Such models provide very good fit for ordinary road conditions, but often fail just when they are most needed: when traffic suffers a sudden and significant disruption, such as a road incident. In this work, we describe QTIP: a simulation-based framework for quasi-instantaneous adaptation of prediction models upon traffic disruption. In a nutshell, QTIP performs real-time simulations of the affected road for multiple scenarios, analyzes the results, and suggests a change to an ordinary prediction model accordingly. QTIP constructs the simulated scenarios per properties of the incident, as conveyed by immediate distress signals from affected vehicles. Such real-time signals are provided by In-Vehicle Monitor Systems, which are becoming increasingly prevalent world-wide. We experiment QTIP in a case study of a Danish motorway, and the results show that QTIP can improve traffic prediction in the first critical minutes of road incidents.