Abstract
This study proposes a methodology for the calibration of microscopic traffic flow simulation models by enabling simultaneous selection of traffic links and associated parameters. The analyst selects any number and combination of links and model parameters for calibration. Most calibration methods consider the entire network and use ad hoc approaches without enabling a specific selection of location and associated parameters. In practice, only a subset of links and parameters is used for calibration based on several factors such as expert knowledge of the system or constraints imposed by local governance. In this study, the calibration problem for the simultaneous selection of links and parameters was formulated using a mathematical programming approach. The proposed methodology is capable of calibrating model parameters considering multiple time periods and performance measures simultaneously. Traffic volume and speed are the performance measures used in this study, and the methodology is developed without considering the characteristics of a specific traffic flow model. A genetic algorithm was implemented to find a solution to the proposed mathematical program. In the experiments, two traffic models were calibrated: the first set of experiments included selection of links only, while all associated parameters were considered for calibration. The second set of experiments considered simultaneous selection of links and parameters. The implications of these experiments indicate that the models were calibrated successfully subject to selection of a minimum number of links. As expected, the more links and parameters that are used for calibration, the more time it takes to find a solution, but the overall results are better. All parameter values were reasonable and within constraints after successful calibration.
Highlights
Microscopic traffic flow simulation is increasingly being used to analyze complex scenarios for a broad range of objectives
E calibration approach provided by the Federal Highway Administration (FHWA) in Traffic Analysis
Cobos et al [20] found that when a memetic algorithm (MA) was adapted, using Solis and Wets local search chains (MA-SWChains), the results provided better and faster convergence compared to both Simultaneous perturbation stochastic approximation (SPSA) and MA
Summary
Microscopic traffic flow simulation is increasingly being used to analyze complex scenarios for a broad range of objectives. Toolbox Volume IV suggests a sequential process of calibrating the capacity at key bottlenecks, traffic volumes, and system performance [2] Using this approach, model parameters are adjusted by modifying global parameters first, link parameters, and route choice parameters. Various genetic algorithms (GAs) have been proposed to calibrate microscopic simulation models [4, 5, 8,9,10,11,12,13,14] with successful results and relatively faster convergence. State-of-the-art methods take into consideration sets of links and parameters for calibration without providing flexibility for selecting or constraining the search space in terms of where and what to use to fine-tune the traffic flow simulation model. Normalization allowed multiple performance measures to be considered simultaneously [3]
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