The success of any business depends fundamentally on the possibility of balancing (symmetry) needs and their satisfaction, that is, the ability to properly define a set of success indicators. It is necessary to continuously monitor and measure the indicators that have the greatest impact on the achievement of previously set goals. Regarding transportation companies, the rationalization of transportation activities and processes plays an important role in ensuring business efficiency. Therefore, in this paper, a model for evaluating performance indicators has been developed and implemented in three different countries: Bosnia and Herzegovina, Libya and Serbia. The model consists of five phases, of which the greatest contribution is the development of a novel rough additive ratio assessment (ARAS) approach for evaluating measured performance indicators in transportation companies. The evaluation was carried out in the territories of the aforementioned countries in a total of nine companies that were evaluated on the basis of 20 performance indicators. The results obtained were verified throughout a three-phase procedure of a sensitivity analysis. The significance of the performance indicators was simulated throughout the formation of 10 scenarios in the sensitivity analysis. In addition, the following approaches were applied: rough WASPAS (weighted aggregated sum product assessment), rough SAW (simple additive weighting), rough MABAC (multi-attributive border approximation area comparison) and rough EDAS (evaluation based on distance from average solution), which showed high correlation of ranks by applying Spearman's correlation coefficient (SCC).

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According to models commonly used in practice, the capacity of roundabouts largely depends on the value of critical headway. The value of critical headway depends on the characteristics of vehicles, driving conditions, and geometric characteristics of intersections, but also on driver behaviour. Driver behaviour is the result of many factors that depend on the influence of the local environment, driver habits, mentality, etc. Accordingly, to calculate the capacity of roundabouts within the op-erational and planning analyses of roundabouts more accurately, it is necessary to use data that correspond to local conditions. In this paper, the critical headway was estimated at five urban single-lane roundabouts using five methods: Harders’, Logit, Raff’s, Wu’s, and the max-imum likelihood method. In order to determine which of the stated methods provides the most realistic estimate of critical headway, a comparison of field capacity values with theoretical capacity values was performed. Based on the comparative analysis performed in MATLAB, as well as the calculation of percentage prediction error, it was found that the Harders' method provides the most accurate estimate of critical headway at observed round-abouts in two cities in Bosnia and Herzegovina. Due to the similarity in the design of roundabouts and driver be-haviour, the results obtained in this paper can be applied in the surrounding countries, i.e., Southeast Europe

According to the models which are commonly used in practice, the capacity of roundabouts depends on the value of critical headway. Critical headway size depends on numerous objective factors, primarily on geometric characteristics of the intersection. However, research so far have shown that beside characteristics of the intersection, value of critical headway depends on many other factors, primarily on driver behavior. Driver behavior is the result of the action of numerous factors that depend on the influence of local environment, habits, mentality, etc. For that very reason, determination of the value of this traffic flow parameter in local research is recommended in many models. Within this paper, the results related to the value of critical headway determined by research which are conducted in Bosnia and Herzegovina are presented. The presented value of critical headway can be used for objective determination of roundabout capacity in our region.

Access or access point usually presents approaching roadway constructed directly along the driveway of the main road through whom vehicles are entering on or exiting from private property, but also it implies commercial approaching and access roads. Increased access-point density connected on the main road affects the disorder of functional dependence of fundamental parameters of traffic flow. An increase of access-point density on the main road has the effect of decrease of capacity and speed of traffic flow, but also increase of travel time. This paper is the outcome of research on several roadway segments in Bosnia and Herzegovina, and results are presenting the distribution of access points in a function of section length. Key results are related to access-point density, i.e. number of access points on both sides of two-lane highways divided by the length of the roadway segment. Depending on access-point density, decrease of free flow speed appears on mentioned sections which value goes from 2,35 km/h to 21,53 km/h and that is significant dispersion determined free flow speeds on given sections. In this paper is analyzed unplanned and uncontrolled construction of a large number of access points along the driveway of two-lane highway, which does not attract significant attention in our country and neighborhood. The main goal of this paper is to determine decrease of speed on segment of representative road network depending on access-point density and highlight the importance and necessity of increased control of access points.

Passenger car equivalents (PCE) present a very important parameter for capacity calcula on and road service level as well as a planning segment of road capacity. There are many ways of calculating PCE and most of them are based on Greenshield's basic method. This paper studies the PCE calculaon methodology and condi ons under which it is applied. The fi rst part of the paper is about role of PCE in analyzing traffi c fl ow, and the rest of the paper is presen ng methodologies for computa on of PCE. Example of the latest method for determining PCE according to HCM-2010 is given in this paper. The goal of the research is presented by structural, parameter and func onal analysis of methods. Further research direc ons of PCE are shown as well.

It is known that traffic flow characteristics have significant influence at the capacity of all functional segments of the road and street network. One of the most important traffic flow parameters which affect the capacity of roundabouts is follow-up headway at minor approaches of roundabouts. This traffic flow parameter, like the most others, depends on driver behaviour, i.e. local traffic conditions. This paper presents the research results related to follow-up headway at three roundabouts obtained by the photographic method. This data collection method is chosen because its application completely eliminates the impact of research at behaviour of traffic participants. After the research, a representative sample is formed and its processing and analysis led to conclusions about the value of follow-up headway at roundabouts which can be applied in standard procedures for capacity calculation at roundabouts in midsize cities of our region.

Due to the impossibility of directly measuring of critical headway, numerous methods and procedures have been developed for its estimation. This paper uses the maximum likelihood method for estimating the same at five roundabouts, and based on the obtained results and pairs of accepted and maximum rejected headways, several predictive models based on machine learning techniques were trained and tested. Therefore, the main goal of the research is to create a model for the prediction (classification) of the critical headway, which as inputs, i.e. independent variables use pairs - accepted and maximum rejected headways. The basic task of the model is to associate one of the previously estimated values of the critical headway with a given input pair of headways. The final predictive model is chosen from several offered alternatives based on the accuracy of the prediction. The results of training and testing of various models based on machine learning techniques in IBM SPSS Modeler software indicate that the highest prediction accuracy is shown by the C5 decision tree model (73.266%), which was trained and tested on an extended data set obtained by augmentation or data set augmentation (Data Augmentation - DA).