One of the most critical aspects of effective microscopic traffic simulation models is proper calibration for accurate replication of both supply and demand characteristics, as well as their interaction. Recent research has begun automating the calibration process by using intelligent transportation system data. This research, however, has targeted automobile traffic and has not generally included commercial motor vehicle (CMV) impacts. Because CMVs are a significant part of the traffic stream and tend to have a disproportionate effect on the transportation system, it is theorized that these vehicles should be included in the calibration process. The objective of this research, therefore, is (a) to outline calibration parameters and network properties available (including site-specific vehicle distributions with weigh-in-motion and automatic vehicle classification data) for a freeway simulation of both passenger cars and CMVs and (b) to apply these parameters and network properties on an urban freeway system in Texas by using the microscopic traffic simulation model CORSIM and an automated genetic algorithm calibration methodology. The results of the analysis indicate that the overall calibration measurably improves the ability of the model to replicate observed conditions. The results for this corridor, however, do not support the need to include maximum nonemergency deceleration rates in the calibration process. The inclusion of site-specific vehicle distributions, however, was observed to provide a more accurate representation of vehicle composition and overall network results. This methodology has not been explored previously and provides a strong base for evaluating the impacts of CMVs on the transportation network by the use of microscopic traffic simulation models.
ASJC Scopus subject areas
- Civil and Structural Engineering
- Mechanical Engineering