Traffic management and traffic information are essential in urban areas, and require a good knowledge 2 about both the current and the future traffic state. Both parametric and non-parametric traffic state 3 prediction techniques have previously been developed, with different advantages and shortcomings. 4While non-parametric prediction has shown good results for predicting the traffic state during recurrent 5 traffic conditions, parametric traffic state prediction can be used during non-recurring traffic conditions 6 such as incidents and events. Hybrid approaches, combining the two prediction paradigms have 7 previously been proposed by using non-parametric methods for predicting boundary conditions used in a 8 parametric method. In this paper we instead combine parametric and non-parametric traffic state 9 prediction techniques through assimilation in an Ensemble Kalman filter. As non-parametric prediction 10 method a neural network method is adopted, and the parametric prediction is carried out using a cell 11 transmission model with velocity as state. The results show that our hybrid approach can improve travel 12 time prediction of journeys planned to commence 15 to 30 minutes into the future, using a prediction 13 horizon of up to 50 minutes ahead in time to allow the journey to be completed. 14 15
TRB 2016 Annual MeetingPaper revised from original submittal.Allström, Ekström, Gundlegård, Ringdahl, Rydergren, Bayen, PatireTraffic management and traffic information is essential in urban areas. This requires a good knowledge 2 about both the current and the future traffic state. Today, the road infrastructure in urban areas is 3 commonly equipped with different type of sensors, capturing speed, flows and travel time data. To use 4 this data for traffic state estimation is a well-studied area and involves both data filtering techniques and 5 traffic simulation models. Examples of filtering approaches for traffic state estimation can for instance be 6 found in (1), (2), (3) and (4). Traffic simulation approaches are presented in (5), (6), (7), (8) and (9). With 7 the deployment of traffic sensors of various kinds it has become more important to combine these two 8approaches (10), (11) There exist several examples of parametric models which can be applied for the purpose of traffic 25 state prediction. Such examples include the microscopic simulation approach in (5) and (9), the 26 mesoscopic simulation approach in (6) and the macroscopic traffic flow models in (7) and (8). Common 27 for all these models is that they include parameters with a predetermined structure. Still, these parameters 28 need to be calibrated according to empirical data. Parametric models all inherit the property of only 29 describing traffic phenomena which follows from the predetermined relationship between model 30 parameters. Also, they rely on boundary conditions, such as traffic demand, which need to be predicted 31 for the entire prediction horizon. 32One way of approaching the shortcomings of both non-parametric and parametric m...