Evaluation of travel time for different type of vehicles in traffic simulation

Microscopic simulation models have been widely used in both transportation operations and management analyses. This is because simulation is safer, less expensive and faster than field implementation and testing. However, for the simulation model the most important issues when running traffic simulation models are distinguished calibration inputs among model inputs (e.g., headway, desired speed, driving behavior etc.). Such inputs that explain driver behaviors and vehicle characteristics are difficult to collect from the field. Therefore, the simulation model user needs to fine-tune all inputs related to driving behavior and vehicle characteristics by comparing and adjusting some absolute measures and this overall process is called calibration. Traffic simulation is an indispensable instrument for transport planners and traffic engineers. VISSIM is a microscopic, behavior-based multi-purpose traffic simulation model designed to analyze and optimize the traffic flows. It offers a wide variety of urban and highway applications and integrates both public and private transportation. Complex traffic conditions are visualized in high level of detail supported by realistic traffic models. This study attempts to fill the gap in knowledge by providing further understanding of the traffic calibration resulting from two intersections and the associated field data. Two consecutive intersections with 4 legs were selected in Malatya for calibration. Data includes travel time, desired speed, hourly volume and headway. Traffic count data was collected during morning periods starting from 7:00 am to 9:00 am and video streaming was analyzed at the YTU ITS Lab. Signal timing and travel time data between the intersections was also collected in addition to headway. Headway(s) data was statistically analyzed to determine whether different lanes had the same headway. The t-test hypothesis was applied to detect the gap between simulated and field results. The results of this study show that different lanes had the same headway(s). Travel time data was also statistically analyzed to find out whether different vehicle types had the same travel time. The results of this study show that different vehicle types had different travel times. Therefore, different vehicle types should have different speed parameters including desired speed, acceleration and deceleration. Calibration was necessary to minimize the errors between the model and field results. Parameters such as CCO, headway(s), and headway (m) were used during the calibration where one parameter was varied independently and taken as variable while the other two parameters were taken as stable until reasonable results were found by applying RMSE (Root mean square error). This same process was repeated for other parameters.