Investigation of the Extent of Field Data Required for Reliable Calibration and Validation of Large Scale Traffic Simulation Models: A Case Study

Abstract

Availability, accuracy and relevance of field data are essential for developing a reliable simulation model. Large scale simulation models in particular require data from many sources and in great detail. Considering the sheer size of many simulation models used in practice, collecting all the required data is both costly and time-consuming, and in many cases even impossible. Therefore a trade-off is usually made in terms of the amount of data collected or the number of selected data collection locations used for the calibration and validation process. The fundamental question addressed in this paper is the following: what is the marginal gain in using an additional type of field data for the calibration and validation process? Using a case study where the calibration and validation of a test network is performed under different scenarios of available data types, and the results are compared in hindsight. The results indicate that only traffic flow and travel time data would suffice for the calibration and validation process, and that the marginal benefit of acquiring additional data such as, queue length, in this case study, is likely to be insignificant.