Calibration and Validation of Microscopic Traffic Flow Models

Abstract

Microscopic simulation models are becoming increasingly important tools in modeling transport systems. A large number of models are used in many countries. The most difficult stage in the development and use of such models is the calibration and validation of the microscopic submodels such as the car-following and gap acceptance models. This difficulty results from the lack of suitable methods for adapting models to empirical data. Recent progress has been made in calibrating a number of microscopic traffic flow models. Ten very different models have been tested with data collected via Differential Global Positioning System-equipped cars on a test track in Japan. To calibrate the models, the data of the leading car are fed into the model under consideration, and the model is used to compute the headway time series of the following car. The deviations between the measured and the simulated headways are then used to calibrate and validate the models. The calibration results agree with earlier studies as there are errors of 12% to 17% for all models, and no model can be denoted to be the best. The differences between individual drivers are larger than the differences between different models. The validation process gives acceptable errors from 17% to 22%. But for special data sets with validation errors up to 60%, the calibration process has reached what is known as "overfitting": because of the adaptation to a particular situation, the models are not capable of generalizing to other situations.