Effectiveness of the overtaking ban for heavy vehicles on the four-lane divided highway in different weather conditions

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