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Urban Transportation Optimization: A Multi-Model Simulation-Based Optimization Approach
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2016-01-01
[PDF-421.14 KB]
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Edition:Final Report
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Abstract:Consider a subnetwork within a larger urban network (e.g., an arterial within a city, a city within a region) where local changes to the supply of the subnetwork are being considered (e.g., changes in the network design or in the traffic control). Transportation agencies often resort to the use of traffic simulators in order to evaluate the impacts of these changes both locally (i.e., within the subnetwork) as well as globally (i.e. at the larger-scale). Transportation agencies often have multiple simulators that cover the subnetwork of interest. Nonetheless, these multiple simulators may differ in their modeling assumptions (e.g., macroscopic versus microscopic), in their reliability (e.g., quality of their calibration) as well as in their modeling scale (e.g., city-scale model versus regional model). Transportation experts will consider the pro's and con's of each model, and will ultimately choose one model to rely on in order to determine and study in detail the subnetwork changes. The choice of a model is not an easy task. A larger scale model may, for instance, capture more accurately the local-global interactions; yet may do so at a greater computational cost. This project proposed a simulation-based optimization (SO) framework that allows for the combined use of multiple simulation models. It aims to achieve a suitable trade-off between obtaining accurate local performance estimates and the associated computational costs. The SO method is suitable to address continuous and generally constrained transportation problems, where the objective function is estimated via simulation, whereas the constraints are available in closed-form and are differentiable
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