Calibration of microsimulation traffic model using neural network approach

• To apply traffic simulation model in local conditions, it is necessary to calibrate the model.
• A new calibration method by using computer software was analyzed..
• A neural network is applicable in the process of calibration of the micro-simulation model.
• VISSIM micro-simulation model is used for calibration done at the example of urban roundabouts..
• The calibration method is applicable in the VISSIM as well as in other micro-simulation models.

This paper presents the results of research on the applicability of neural networks in the process of computer calibration of a microsimulation traffic model. VISSIM microsimulation model is used for calibration done at the example of roundabouts in an urban area. The calibration method is based on the prediction of a neural network for one traffic indicator, i.e. for the traveling time between measuring points. Besides the traveling time, the calibration process further/also involves a comparison between the modeled and measured queue parameters at the entrance to the intersection. The process of validation includes an analysis of traveling time and queue parameters on new sets of data gathered both at the modeled and at a new roundabout. A comparison of the traffic indicators measured in the field and those simulated with the calibrated and uncalibrated microsimulation traffic model provides an insight into the performance of the calibration procedure.