State Estimation, Short Term Prediction and Virtual Patrolling Providing a Consistent and Common Picture for Traffic Management and Service Providers
Year: 2018
Type: conference-paper
Abstract: The two traffic management stakeholder groups of public road authorities and private mobility service providers both need information on and derived from the current and predicted traffic state to be able to assess and take appropriate, pro-active actions.Significant improvements of quality and availability of data offers the opportunity to provide such information.By combining data science and traffic modelling techniques, an application is developed consisting of current and short term traffic prediction (typically 10 minutes ahead) and a virtual patrol detecting congestion and incidents for urban and non-urban networks.Being able to detect incidents and predict congestion problems, providing a consistent and complete common picture, offers the opportunity for both stakeholders to cooperate, negotiate and act faster optimizing their objectives.This application is based on four key features; data fusion, real-time supply and demand calibration, fuzzy traffic state estimation and traffic flow theory.Case studies on the A10 orbital road of Amsterdam and on a secondary road network of Almere consisting of ten traffic lights, shows an accuracy of approximately 90% in both cases in terms of true positives and negatives in state estimations and predictions.
Authors: Luc Johannes Josephus Wismans, L.C.W. Suijs, Luuk Brederode, Henri Palm, P. van Beek
Institutions: Goudappel Coffeng, Delft University of Technology
Language: English
Subfield: Transportation
Field: Social Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W2998623340