Short-term prediction of traffic operations in traffic control centers

Currently the traffic managers base their decisions mainly on experience: they implicitly estimate the traffic state at hand and the expectation in the near future by means of several data sources. Examples of these data sources are the roadside cameras, loop detectors and road works information. In this way they select the right predefined scenario and associated measures to control the traffic operations.

However, in the future this method will not provide sufficient results. The decision making process will become more complex: more data will become available from different sources in different forms (e.g. travel time camera’s, individual speed data from in-car systems and weather forecasts). As the use of the road infrastructure and therefore the impact of (wrong) decisions of traffic managers become more intensive, the pressure on traffic managers to make the right decision will increase. An integrated traffic state estimation and prediction tool will help the traffic managers making sense of all the data that becomes available and improving the decisions made.

In this project, the needs of the traffic managers are identified and a prototype of such a tool that satisfies the needs of the traffic managers is developed. The basis of the prototype is a macroscopic traffic flow simulation that can adapt to real-time input of data from loop detectors. The prototype can be extended to include other data sources. The assimilation of the data and the simulation is done using an Ensemble Kalman Filter (EnKF), instead of the in scientific research common Extended Kalman Filter (EKF), as the EnKF can possibly provide better accuracy than the EKF. The prototype is tested using a case study of the Rotterdam highway network.


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