The aim of this paper is to describe the use of a general methodology tailored to the evaluation of micro-scale meteorological models applied to flow and dispersion simulations in urban areas. This methodology, developed within COST 732, has been tested through a large modelling exercise involving many groups across Europe. The major test case used is the Mock Urban Setting Test (MUST) experiment representing an idealised urban area. It is emphasised that a full model evaluation is problem-dependent and requires several activities including a statistical validation that requires a careful choice of the metrics for the comparison with measurements.

Overloaded vehicles pose a real threat to road safety and significantly contribute to the degradation of the road surface. High-Speed Weigh in Motion (HS-WIM) stations are the commonly used method of eliminating them from traffic. In Poland, HS-WIM stations operate in pre-selection mode, sending information to services about the potential exceedance of acceptable standards by a specific vehicle. The article presents the results of the data analysis from selected HS-WIM stations operating on the national road network in Poland indicating significant limitations of the effectiveness of the whole system. The main reason for this may be that carriers use the knowledge about the HS-WIM stations location and working time to avoid inspections. The results presented in the paper indicate, among other things, that in some locations the share of vehicles overloaded with traffic increases significantly outside the working hours of the controlling services. For Light Commercial Vehicle, the share of overloaded vehicles in this group is also significant. Also, the paper indicates that the effectiveness of the procedure for determining vehicle overload has been limited due to errors in classification.

This paper presents the analyses of the signals recorded by the main sensors of a WIM test station in the cases of abnormal runs (i.e., runs with the changes of trajectory or the dynamics of vehicle motion). The research involved strain gauges which are used for measuring the weight of vehicles, inductive loops, as well as piezoelectric sensors used, inter alia, to detect twin wheels and to determine where a vehicle passes through a station. Since the designers intend the station to be able to implement the direct enforcement function, the selection of runs deviating from the normative ones constitutes an important issue for the assessment of the measurement reliability. The study considered the location of the trajectory of the runs, the dynamics (acceleration/braking) and the trajectory changes. The change in the amplitude and the value of the signal recorded by the strain gauges as a function of the location (position) of the contact between sensor and tires is a noteworthy observation which indicates the need to monitor this parameter in automatic WIM systems. Other tests also demonstrated the influence of the analysed driving parameters on the recorded results. However, by equipping the WIM station with a set of duplicate strain gauges, the measurement errors of the gross weight and axle loads are normally within the accuracy limits of class A(5) stations. Only in the case of accelerating/decelerating, does the error in measuring the load of a single axle reach several per cent.

The challenge of maintaining the required level of mobility and air quality in cities can be met by deploying an appropriate management system in which the immediate vicinity of roads is monitored to identify potential pollution hotspots. This paper presents an integrated low-cost system which can be used to study the impact of traffic related emission on air quality at intersections. The system was used for three months in 2017 at five locations covering intersections in the centre of a mid-sized city. Depending on the location, pollution hotspots with high PM_2.5 and PM₁₀ concentrations occurred 5–10% of the time. It was shown that despite the close mutual proximity of the locations, traffic and the immediate surroundings lead to significant variation in air quality. At locations with adverse ventilation conditions a tendency towards more frequent occurrences of moderate and sufficient air quality was observed than at other locations (even those with more traffic). Based on the results, a practical extension of the system was also proposed by formulating a model for the prediction of PM_2.5 concentration using a neural network. Information on transit times, meteorological data and the background level of PM₁₀ concentration were used as model input parameters.