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.
A conceptual modeling framework is proposed, and mathematical submodels for route choice on motorways and urban networks are derived. The models convey the most relevant aspects that play a role in route choice, including learning, risk attitude under uncertainty, habit, and the impacts of advanced travel information service on route choice and learning. To gain insight into the relative importance of the different aspects and processes of route choice behavior, which support the proposed conceptual framework, the models were estimated with data from two experiments carried out with a so-called interactive travel simulator. The latter is a new research laboratory that combines the advantages of both stated preference and revealed preference research. Many relevant contributions on the aforementioned aspects that play a role in route choice can be found in the literature, but a simultaneous consideration of all is lacking. On the basis of these contributions from the literature, a conceptual framework that integrates these aspects was developed. The results from the laboratory experiments indicate that people perform best under the most elaborate information scenario and that habit and inertia together with en route information play a major role in route choice. Learning about route attributes is especially important during the first days but then plays a smaller role than the provided information and the developed habit. Finally, the way information is presented has a great impact on route choice.
In the Netherlands, a Field Operational Test was conducted in 2006 to assess the impact of two Advanced Driver Assistance Systems (ADAS), namely Adaptive Cruise Control (ACC) and Lane Departure Warning (LDW) systems. The research goal was to estimate the effects of these systems on road capacity, safety and emissions. In this paper we focus on the interaction between driver and ACC system using the data from this FOT. It is found that drivers choose headway settings according to their manual driving behavior. Moreover, they often keep the system deactivated under dense traffic conditions. It is also observed from the data that the system, once de-activated, either automatically or manually, needs some time to become again active. These findings imply that, even with 100% cars on the road equipped with ACC, manual driving behavior will still be a determinant factor.
In this study we analyse the impact of congestion in dynamic origin-destination (OD) estimation. This problem is typically expressed using a bi-level formulation. When solving this problem the relationship between OD flows and link flows is linearised. In this article the effect of using two types of linear relationship on the estimation process is analysed. It is shown that one type of linearisation implicitly assumes separability of the link flows, which can lead to biased results when dealing with congested networks. Advantages and disadvantages of adopting non-separable relationships are discussed. Another important source of error attributable to congestion dynamics is the presence of local minima in the objective function. It is illustrated that these local minima are the result of an incorrect interpretation of the information from the detectors. The theoretical findings are cast into a new methodology, which is successfully tested in a proof of concept.
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