Model-based Short Term Predictor of Traffic States

Model-based Short Term Predictor of Traffic States


Leon Suijs, Goudappel Coffeng, Luc Wismans, Dat.mobility, Luuk Brederode, Dat.mobility


A model-based short term predictor has been developed for analyzing actual and near future traffic states detecting congestion and incidents. The predictor processes and fuses data from various sources using a macroscopic traffic propagation model.


Such as in many European countries, the use of urban and interurban roads is increasing in the Netherlands. As a result road networks are running out of capacity and congestion is increasing as well as the vehicle hours spent. This causes societal disbenefits which increases the urge to solve the problems. One of the solutions is to allow traffic managers to gain insight in actual traffic situation and near future events and to better inform car users during their trip. This can be done with road side units such as DRIPs (Dynamic Route Information Panel) and with smart information using in-car equipment consisting route information. For such more advanced information provision smart traffic data is required.

The quality and availability of traffic data has been significantly improved in the last few years. More and more, loop detector data and floating car data becomes available (real time). This offers opportunities for new tools for traffic flow analysis and prediction relevant for operational traffic management services. Being able to detect and predict incidents, e.g. queues, accidents and car breakdowns in an early stage, offers the opportunity to act faster and therefore reduce or even mitigate congestion problems compared to current practice. Supported by the projects CHARM (co-operation between Highways England (UK) and Rijkswaterstaat (NL)) and the iCentrale initiative (Dutch Program in which local, regional and national authorities work together with private parties on better traffic management) a model-based short term predictor has been developed and applied for several real life cases.

This short term predictor is based on four key features; data fusion, real-time estimation of the fundamental diagram, fuzzy traffic state estimation and traffic flow simulation. Traffic flow theory is used to aggregate and fuse data from various data sources (i.e. loop detector data, floating car data and traffic light data) into detailed traffic state estimations per minute for each 250 meter road segment. The basis for fusing and completion of data is a macroscopic traffic propagation model within OmniTRANS transport planning software which is also used for near future prediction purposes (up to 10 minutes). This model is fed by traffic state data (speeds and flows) from real-time measurements. From this data (every minute) actual traffic and actual road capacities for the modelled network are estimated. As road capacity varies for weather conditions, lightness, number of vehicles et cetera a self-adapting module is used to constantly estimate and update parameters which describe the fundamental diagram for road segments (such as free flow speed, capacity and speed at capacity). Every minute a model-run is performed resulting in actual and near futures traffic states. Subsequently a virtual patrol analyses the measured and modelled data using fuzzy logic to detect incidents on the road network and identify and predict congestion in the near future.

This model-based short term predictor has now been applied with success on the A10 orbital road of Amsterdam (NL) and will be applied on the M-ring of Birmingham (UK) and a secondary road network of Almere consisting of ten traffic lights in the coming months. Our paper and presentation will describe the approach used and the results of all these three cases.


Association for European Transport