Replication of Work Zone Capacity Values in a Simulation Model

Abstract

Evaluating the traffic impacts of work zones is vital for any transportation agency to plan and schedule work activity. Traffic impacts can be estimated by using microscopic simulation models. One challenge in using these software models is obtaining the desired work zone capacity values, which tend to vary from state to state. Thus, the default parameter values in the model that are suitable for normal traffic conditions are unsuitable for work zone conditions, let alone for conditions specific to particular states. Although a few studies have been conducted on parameter selection to obtain desired capacity values, none of them have provided a convenient look-up table (or chart) for the parameter values that will replicate field-observed capacities. Without such provision it has not been possible for state agencies to use any of the research recommendations. This study provides the practitioner a simple method for choosing appropriate values of driving behavior parameters in the VISSIM microsimulation model to match the desired field capacity for work zones operating in a typical early-merge system. 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 the safety distance reduction factor is representative of lane-changing aggressiveness. It has been verified that the recommended parameter values not only produce the desired capacities but also create traffic conditions consistent with traffic flow theory.

Operational Assessment of Joint and Conventional Lane Merge Configurations for Freeway Work Zones

“…Since traffic composition and driver characteristics were controlled in both configurations, the detected differences between CLM and JLM would be attributed to the merging behavior, which is the main focus of this analysis. This is consistent with the general application of simulation models to what-if scenarios or the particular application to similar work-zone studies demonstrated by Beacher et al (2005a), Chatterjee et al (2009), Pesti et al (2008, Wei et al (2008), and Yang et al (2009), to name a few. Since JLM is in an experimental stage, the use of simulation provides an insight of its anticipated performance before implementation takes place.…”

“…As pointed out by Chatterjee et al (2009), the capacity of work zones may vary from one location to another. In VISSIM, the adopted car following algorithm is based on Wiedemann 99 model, which includes 10 parameters (CC0-CC9) that can be modified by the user during the calibration process.…”

“…The geometric characteristics and the data collection points for the CLM configuration in VISSIM were scaled and coded to the coordinates of the field study to get reliable results. Work-zone capacities measured in different states (Chatterjee et al, 2009) were found to be well below the capacity of basic freeway segments, estimated by the Highway Capacity Manual (2000). The observed range of work-zone capacities suggests values in the neighborhood of the queue discharge rate, as indicated by Benekohal, Kaja-Mohideen, and Chitturi (2004).…”

Comparing Three Lane Merging Schemes for Short-Term Work Zones: A Simulation Study

“…Previous literature showed that in VISSIM the parameters CC0, CC1, CC2, CC4/CC5, and SRF are suited for model calibration of a work zone. Chatterjee et al [15] argued that between the two parameters CC0 and CC1 that determine the safety distance d xsafe = CC0 + CC1 * v (that in turn determines ISRN Civil Engineering capacity) only CC1 has a significant effect on the safety distance. According to the same study, it was concluded from visual interpretation that CC4/CC5 pair less than 3 resulted in unstable car following process and values higher than 3.0 did not result in variation of the MOE.…”

Designing Dynamic Delivery Parking Spots in Urban Areas to Reduce Traffic Disruptions

“…This factor also depends on the number of remaining free lanes: the higher the number of lanes, the smaller the overall effect (i.e., the higher the ). Merging effects and the impact of lane changes have been studied in freeway environments, either with an endogenous model [43][44][45], simulation [46,47], or empirical data [48,49]. In this paper, as it will be later shown, the parameter can be easily calibrated with a simple simulation experiment performed in a controlled manner.…”