Calibration of Driving Behavior Models using Derivative-Free Optimization and Video Data for Montreal Highways

Traffic simulation software is commonly used for traffic impact assessment in transportation projects, allowing the selection of adequate solutions to existing complex problems without having to test them in the field. Such software is highly adaptable to different road conditions and driving behaviours by letting engineers modify a large number of parameters. However, this flexibility requires the model to be calibrated for each application in different regions, conditions and settings. Calibration requires data, which can be time-consuming and costly to collect. The authors propose a calibration procedure for the driving behavior models, which describe how vehicles interact with each other. These calibrated behaviors should be generic for the region regardless of the specific site geometry and the proposed procedure seeks to allow this generalisation by allowing simultaneous simulations on many networks. To achieve this calibration, a state-of-the-art derivative free optimization algorithm, the mesh-adaptive direct-search algorithm, is used to fit simulations to real world microscopic data acquired via automated video analysis. The authors propose an implementation of this procedure for the Wiedemann 99 model in the VISSIM traffic micro-simulation software in a case study for the City of Montreal using data collected on a major Montreal highway.

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