Calibrating the microsimulation traffic models can be defined as a black-box optimisation problem with some non-concave objective functions. In this regard, the stochastic optimisationalgorithms are suitable choices ...
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Calibrating the microsimulation traffic models can be defined as a black-box optimisation problem with some non-concave objective functions. In this regard, the stochastic optimisationalgorithms are suitable choices to explore the search space and prevent getting stuck in local optimums. However, considering only the traffic attributes-related objectives may fail to calibrate the model in terms of safety. Therefore, by defining two different objectives, a two-fold calibration approach is proposed such that the simulation model reproduces the real-world transportation network more accurately, both in terms of safety and operation. Moreover, the performance of two different approaches to solve this multi-objective optimisation problem are evaluated. It is shown that by aggregating the objectives in one single formula (i.e. a priori methods), the information exchange among solutions is not captured, which may lead to non-optimal solutions. While this limitation is overcome by a posteriori methods since different objectives can be optimised separately and simultaneously. In this regard, the performance of posteriori-based multi-objective particleswarmoptimisation (MOPSO) algorithm in calibrating VISSIM is compared with some priori-basedoptimisationalgorithms (e.g. PSO, genetic algorithm, and whale optimisationalgorithm). The results show that posteriori-based MOPSO leads to a more accurate solution set in terms of both objectives.
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