![]() This article aims to provide a comparative analysis of the application of ![]() Fuzzy inference techniques proved their ability to solve uncertainties inĬontinuous-time models. Replicates the observed model, it is necessary to have a calibration process that applies the appropriateĬompensation values to the simulation model parameters to reduce the differences compared to the Furthermore, the measurement of traffic parametersĪlso introduces uncertainties through measurement errors. Modeling process is affected by uncertainties. Differences between the simulated and observed model are present because the One of the most used traffic models is the car-following model, whichĪims to control the movement of a vehicle based on the behavior of the vehicle ahead while ensuringĬollision avoidance. Of the traffic paradigm and helps researchers to estimate traffic behavior and identify appropriate Traffic modeling simplifies the understanding * Correspondence: transition to intelligent transportation systems (ITSs) is necessary to improve trafficįlow in urban areas and reduce traffic congestion. MiceaĬomputer and Information Technology Department, Politehnica University of Timisoara, 300223 Timisoara, Romania (M.-D.P.) (M.V.M.) M˘ad˘alin-Dorin Pop, Dan Pescaru * and Mihai V. Takagi–Sugeno Fuzzy Inference Systems in the Calibration of Continuous-Time Car-Following Models This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( /licenses/by/ Received: 20 September 2023 Revised: 22 October 2023 Accepted: 25 October 2023 Published: 28 October 2023 Takagi–Sugeno Fuzzy Inference Systems in the Calibration of Continuous-Time Car-Following Models.
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