A mechanistic model of work zone capacity

Lane closures in work zones for highway reconstruction reduce traffic operational capacity and negatively affect traffic flow. Estimating work zone capacity under lane closure operations becomes a critically important issue for establishing construction plans, traffic control strategies, and transportation management plans in highway reconstruction. This study first investigated the work zone capacities empirically through several case studies and identified the factors affecting a work zone's capacity. Merging behavior in work zones is then studied and was found to cause a loss in effective time for queue discharge. Merging patterns, merging location distribution, and merging times were investigated and quantified. Based on the understanding of merging behavior in work zones, a mechanistic model of work zone capacity was developed. The model estimates work zone capacity with given lane closure configurations and traffic conditions(i.e., proportion of heavy vehicles, proportion of merging patterns, average merging time, and queue discharging flow rate) based on merging frequency, average merging time and the normal queue discharge capacity under the given traffic conditions. Model validation using the empirical data shows that the developed model reliably estimated work zone capacities along with the different lane closure configurations of the multilane highways. This model uses a few parameters and is easy to calibrate. It provides the essential input: work zone capacity, to traffic analysis models such as the Demand-Capacity model and macroscopic traffic simulation models for predicting traffic delay in work zone traffic analysis. Also, it can be a measure of performance to calibrate a merging behavior model of work zones in microscopic traffic simulation. An application tool of the work zone capacity model can be a module in traffic and construction analysis tools that enable transportation planners to develop and compare various construction and lane closure plans, and select the best one that minimizes construction and road user costs and enhances driver safety.