In literature there are several approaches to eliminate shock waves on freeways by means of dynamic speed limits. Most of them incorporate control systems that have a high computational complexity or that contain parameters without direct physical interpretation, which may make the application in real life difficult. Here we present an approach called SPECIALIST that is based on shock wave theory, and that has parameters with clear physical meaning. The clear interpretation of the parameters leads to an intuitive and insightful formulation of the tuning guidelines. One of the most important features related to the parameter tuning is that the stability of the traffic flow can be ensured by selecting a proper maximum density that is allowed to occur in the speedcontrolled area. In addition, other parameters can be tuned for more robust behavior of the algorithm.We first present the theory of shock wave resolution, and next we develop a practical control algorithm based on this theory. A unique feature of the algorithm is that it first judges the solvability of a shock wave and only starts controlling the speed limits if the shock wave is classified as solvable.The algorithm is demonstrated with a simulation example, and it is shown that its performance is similar to existing approaches.

To gain insight into the behavior of drivers during congestion, and to develop and test theories and models that describe congested driving behavior, very detailed data are needed. A new data-collection system prototype is described for determining individual vehicle trajectories from sequences of digital aerial images. Software was developed to detect and track vehicles from image sequences. In addition to longitudinal and lateral position as a function of time, the system can determine vehicle length and width. Before vehicle detection and tracking can be achieved, the software handles correction for lens distortion, radiometric correction, and orthorectification of the image. The software was tested on data collected from a helicopter by a digital camera that gathered high-resolution monochrome images, covering 280 m of a Dutch motorway. From the test, it was concluded that the techniques for analyzing the digital images can be applied automatically without much problem. However, given the limited stability of the helicopter, only 210 m of the motorway could be used for vehicle detection and tracking. The resolution of the data collection was 22 cm. Weather conditions appear to have a significant influence on the reliability of the data: 98% of the vehicles could be detected and tracked automatically when conditions were good; this number dropped to 90% when the weather conditions worsened. Equipment for stabilizing the camera—gyroscopic mounting—and the use of color images can be applied to further improve the system.

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This paper considers multianticipative car-following behavior (i.e., driver behavior that includes responses to multiple vehicles ahead). Two well-known models incorporating multivehicle stimuli (Bexelius and Lenz) are recalled, and various modifications are proposed to improve their performance. With vehicle trajectories for a motorway collected from a helicopter and a newly developed approach to parameter identification, new empirical evidence of multianticipative car-following is provided with estimates of the driver-specific parameters of the considered multianticipative car-following models. In doing so, one can investigate the nature of multileader stimuli, including insights into the number of vehicles ahead to which drivers react and the kind of stimuli to which drivers respond. Large interdriver variability in multileader driving behavior is also presented. In the last part of the paper, implications of the research findings for microscopic modeling are discussed.