Stochastic Modeling of Motorway Work Zone Capacity from Field Data in Slovenia

Abstract

As Slovenia’s motorway infrastructure matures and approaches the end of its design life, maintenance-related work zones are becoming increasingly frequent, making the estimation of their capacity a crucial issue for effective traffic management. In addition to maintenance activities, the planned expansion of the motorway network with additional lanes is expected to introduce numerous large-scale construction work zones in the coming years. Accurate capacity assessment is essential for minimizing congestion, ensuring safety, and supporting maintenance scheduling and construction planning on heavily used motorway sections that experience temporary lane closures and geometric restrictions. In current practice, the Highway Capacity Manual and most empirical models provide deterministic capacity estimates, whereas stochastic approaches—particularly those based on the Weibull distribution—have been successfully applied to describe capacity reliability on basic freeway segments. However, such probabilistic concepts have not yet been systematically applied to motorway work zones, where lane reductions and geometric constraints substantially alter traffic dynamics. This study applies a stochastic modeling framework that integrates survival-based capacity sampling with Weibull distribution fitting to estimate motorway work zone capacity using detailed field data from the Slovenian motorway network. The analysis was performed on several work zone configurations for which sufficient prebreakdown flow observations were available to calibrate Weibull capacity distributions. The resulting distribution parameters provide probabilistic measures of breakdown likelihood and allow comparison of reliability levels between work zone configurations under different geometric and traffic conditions. Results show significant capacity reductions in work zones involving lane narrowing and crossovers, while the Weibull-based framework effectively captures the variability and reliability of capacity across different configurations. The study demonstrates the practical applicability of stochastic capacity modeling for motorway work zones and its value for data-driven planning, reliability assessment, and management of maintenance and expansion activities in mature motorway networks.