Towards sustainable dynamic traffic management

Dynamic traffic management measures are potentially powerful measures to not only improve network efficiency but also to reduce externalities of traffic (i.e. emissions of substances and noise and safety). In current practice the deployment of these measures (i.e. strategies) focuses on improving efficiency on a local level in which behavioral responses are not taken into account in the assessment and mainly concerns the evaluation of a few predefined strategies. However, these strategies should be based on their network performance, because of spatial correlations, considering all possible strategies, which can be done by optimizing the objectives on network level. Previously no research has been done on how these objectives relate and what strategies can be effective, taking traffic dynamics and route choice behavior into account. For this purpose the optimization problem is formulated as a multi-objective network design problem. A framework is developed, connecting the Streamline dynamic traffic assignment model with externality models for emissions (ARTEMIS), noise (RMV and AR-INTERIMCM) and an accident risk based model for safety. To solve the optimization problem, various solution approaches are developed and compared, incorporating response surface methods within genetic algorithms to accelerate the solution approach. Applications shows that the objectives efficiency, air quality (NOx emissions) and climate (greenhouse gas emissions) are aligned, and are opposed to traffic safety and noise. Because the objectives are conflicting, there is not one single solution that optimizes all objectives simultaneously, an optimization results in finding Pareto optimal solutions. To choose the best compromise solution, a compensation principle is needed. Pruning and ranking methods have been applied, which may be useful to circumvent the possible difficulties in analyzing the large Pareto optimal set in the decision making process. Using cost benefit analysis shows that efficiency is the dominant objective. Other multi criteria decision making methods are potentially more useful as a basis for an interactive decision support tool. Analyzing the Pareto optimal solutions further shows that metering of traffic on smart locations can be an effective strategy to reduce externalities and that lowering the speed limit not necessarily reduces externalities.