This paper introduces a numerical variable speed limit (VSL) control method on a motorway, modeled by the system of partial differential equations (PDEs) of a non- equilibrium continuum traffic model. The method consists of a macroscopic simulation (i.e., numerical solution of the system of PDEs of the continuum model), introduction of the solution-based cost function and numerical optimization with a differential evolution algorithm (DE). Due to the numerical solution scheme, the method enables application of a wide range of continuum traffic models without prior discretization of PDEs. In this way, the method overcomes the limitations of the basic continuum models and represents a step towards more accurate traffic modelling in control strategies. In this paper, we determine optimal variable speed limits with the DE algorithm on a motorway section modeled by the modified switching curve model, which is a non-equilibrium continuum model consistent with the three-phase traffic flow theory. The effectiveness of the determined variable speed limits is validated using microsimulations of the test section, which show promising reductions of queue lengths and number of stops.

In many European countries and also in Slovenia, the highway network was rapidly built in order to reduce congestion and to increase the level of traffic safety on congested sections of the road network, thus enabling a higher level of service and accelerating polycentric development. Unfortunately, traffic demand is growing over all limits, be it tourist car traffic or transit-heavy vehicle traffic. Thus, countries are forced to actively manage road freight traffic, which is present all year round. Accordingly, in Slovenia, permanent and timed restrictions were introduced for trucks regarding overtaking on highways. Overtaking is prohibited during the day but trucks are allowed to change lanes at night. It should be noted, however, that there may be circumstances that can restrict the normal travel of heavy vehicles in all lanes in one way or another, whether at night or during the day. We would like to convince highway traffic managers that weather-responsive adaptive traffic control could be more efficient when weather conditions are considered. This article presents an approach to simulate traffic flow on a short section of a two-lane unidirectional carriageway under various weather conditions. Using two scenarios for lane traffic control, i.e., with and without a truck overtaking ban, as examples, we show that knowledge of the traffic characteristics of each lane in different weather conditions is important for decision-making and for the timeliness of traffic management. We found that under certain traffic and weather conditions, prohibiting vehicles from overtaking with limited speed limits on four-lane divided highways or proper traffic lane control has a positive effect on the traffic fluency or available conditional capacity of the highway. To some extent, this confirms that the decision of the operator of the Slovenian highway system regarding the driving regime for heavy vehicles was correct. Through our research, we found that dynamic bans can be more effective when we include the dynamics of traffic demand, and environmental and weather conditions.

To meet the collective goal of providing an efficient, sustainable, and safe transportation system, motorway managers are oriented toward establishing an optimal traffic management system (TMS). However, there are no straightforward guidelines through which to deploy the optimal integration scenario for traffic control centers (TCCs). An important component of TMSs is traffic control decisions, implying that human resources represent a critical feature of a TMS. In this study, we introduce human resource management (HRM) into the process of selecting the most appropriate integration scenario, which is achieved by developing a model to determine the number of employees. We formulated the problem as the mixed integer nonlinear programming problem based on the analysis of traffic incidents and road infrastructure data, considering also the employee voice and some HRM specificities in incident situations. Combining these findings with a SWOT analysis of the possible integration scenarios in Slovenia, we propose the most suitable scenario, in which TMS is organized as a cloud service. Moreover, this study predicts TCCs’ space requirements and identifies threats of neglecting HRM in TMS design. This paper can serve as a guideline and a baseline of expertise for motorway managers to create an effective and sustainable TMS in their region.

This paper presents a cost–benefit analysis (CBA) of proposed high-capacity cycling corridors in the wider Ljubljana area, representing the first such evaluation in Slovenia, where comparable infrastructure has not yet been implemented. The low baseline cycling demand presents methodological challenges for accurately forecasting user response. To address this, a discrete mode choice model calibrated with regional data was applied to estimate modal shifts from private car and public transport to cycling, based primarily on anticipated travel time savings. Long-term projections incorporate behavioral responses to improved infrastructure, such as enhanced comfort, continuity, and perceived safety. The analysis accounts for capital investment, land acquisition, maintenance, and renewal costs, while quantifying external benefits including public health improvements, reduced fuel consumption, lower emissions, and travel time savings. Conservative assumptions were applied throughout, and a detailed sensitivity analysis was conducted to test the robustness of results under alternative cost and uptake scenarios. Findings indicate that the investment shows positive economic performance under the baseline and in most tested scenarios, with greater sensitivity to demand-side parameters than to cost variations. These results highlight the importance of integrating infrastructure planning with complementary policy measures to stimulate early adoption and maximize societal benefits. The study proposes a framework that could be transferable for the appraisal of transformative cycling infrastructure and contributes to the growing body of evidence on active mobility in emerging markets, particularly within Central and Eastern Europe.

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.