Two‐fold calibration approach for microscopic traffic simulation models. Issue 10 (9th July 2019)

• Record Type: Journal Article
• Authors: Karimi, Mohammad; Alecsandru, Ciprian
• Is Part Of: IET intelligent transport systems. Volume 13:Issue 10(2019)
• Page Start: 1507
• Page End: 1517
• Publication Date: 2019-07-09
• Subjects: genetic algorithms -- traffic engineering computing -- particle swarm optimisation -- road traffic control; two‐fold calibration approach -- microscopic traffic simulation models -- microsimulation traffic models -- black‐box optimisation problem -- nonconcave objective functions -- stochastic optimisation algorithms -- search space -- local optimums -- simulation model -- real‐world transportation network -- nonoptimal solutions -- posteriori‐based multiobjective particle swarm optimisation algorithm -- whale optimisation algorithm -- traffic attribute‐related objectives -- MOPSO algorithm -- VISSIM; Intelligent transportation systems -- Periodicals; Electronics in transportation -- Periodicals; 388.31205
• DOI: 10.1049/iet-its.2018.5369
• Languages: English
• ISSNs: 1751-956X

Abstract

Abstract : 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 optimisation algorithms 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 particle swarm optimisation (MOPSO) algorithm in calibrating VISSIM is compared with some priori‐based optimisation algorithms (e.g. PSO, genetic algorithm, and whale optimisation algorithm). The results show that posteriori‐based MOPSO leads to a more accurate solution set in terms of both objectives.