Replication of freeway work zone capacity values in a microscopic simulation model

Evaluating the traffic impacts of work zones is vital for any transportation agency for planning and scheduling work activity. Traffic impacts can be accurately estimated using microscopic simulation models due to their ability to simulate individual vehicles and their interactions that can have a strong impact on various performance measures such as capacity, queue length, and travel delays. One challenge in using these simulation models is obtaining the desired work zone capacity values which tend to vary from one state to another. Thus, the default parameter values in the model which are suitable for normal traffic conditions are unsuitable for work zone conditions let alone for conditions specific to particular states. A few studies have been conducted on parameter selection to obtain the desired capacity values. However, none of these studies have provided a convenient look-up table (or a chart) for the parameter values that will replicate the field observed capacities. Without such provision it has not been possible for state agencies to utilize many of the research recommendations. This research provides the practitioner a simple method for choosing appropriate values of driving behavior parameters in the VISSIM micro-simulation model to match the desired field capacity for work zones operating in a typical early merge system. Besides the car-following and lane changing driver behavior parameters this research also recommends the appropriate truck characteristics for use while modeling freeways in U.S. The default truck characteristics that are commonly used in VISSIM do not reflect the typical U.S. truck fleet characteristics. For example, the default length of trucks in VISSIM is 33.51 feet. In U.S, truck lengths vary from 30 feet to as long as 80 feet. This distinction is especially critical given the significant impact that trucks have on work zone capacity and queue lengths.;The two most significant car-following parameters and one lane changing parameter were selected and varied to obtain different work zone capacity values. CC1 is the desired time headway, CC2 is the longitudinal following threshold during a following process, and SRF is the safety distance reduction factor representative of lane changing aggressiveness. Additionally, for each recommended set of driving behavior parameters, lane distribution of the closed lane at different points upstream of the taper was collected. It was verified that the recommended parameter values not only produce the desired capacities but also create traffic conditions consistent with traffic flow theory. To apply this method, a transportation agency with the knowledge of lane distribution at specific points upstream from the work zone chooses a unique set of driving behavior parameters from the table to match the observed field capacity. Finally, multivariate regression models were developed to analyze relationship between the selected driver behavior parameters with work zone capacity, truck percentage, and lane distribution.