The fundamental diagram links average speed to density or traffic flow. An analytic form of this diagram, with its comprehensive and predictive power, is required in a number of problems. This paper argues, however, that, in some assessment studies, such a form is an unnecessary constraint resulting in a loss of accuracy. A non-analytical fundamental diagram which best fits the empirical data and respects the relationships between traffic variables is developed in this paper. In order to obtain an unbiased fundamental diagram, separating congested and non-congested observations is necessary. When defining congestion in parallel with a safety constraint, the density separating congestion and non-congestion appears as a decreasing function of the flow and not as a single critical density value. This function is here identified and used. Two calibration techniques – a shortest path algorithm and a quadratic optimization with linear constraints – are presented, tested, compared and validated.

The transportation sector accounts for nearly 19% of total energy consumption in Turkey, where energy demand increases rapidly depending on the economic and human population growth and the increasing number of motor vehicles. Hence, the estimation of future energy demand is of great importance to design, plan and use the transportation systems more efficiently, for which a reliable quantitative estimation is of primary concern. However, the estimation of transport energy demand is a complex task, since various model parameters are interacting with each other. In this study, artificial neural networks were used to estimate the energy demand in transportation sector in Turkey. Gross domestic product, oil prices, population, vehicle-km, ton-km and passenger-km were selected as parameters by considering the data for the period from 1975 to 2016. Seven models in total were created and analyzed. The best yielding model with the parameters of oil price, population and motor vehicle-km was determined to have the lowest error and the highest R² values. This model was selected to estimate transport energy demand for the years 2020, 2023, 2025 and 2030.

Applying cognitive radio in the railway communication systems is a cutting-edge research area. The rapid motion of the train makes the spectrum access of the railway wireless environment instable. To address the issue, first we formulate the spectrum management of railway cognitive radio as a distributed sequential decision problem. Then, based on the available environmental information, we propose a multi-cognitive-base-station cascade collaboration algorithm by using naive Bayesian learning and agent theory. Finally, our experiment results reveal that the model can improve the performance of spectrum access. This cognitive-base-station multi-agent system scheme comprehensively solves the problem of low efficiency in the dynamic access of the railway cognitive radio. The article is also a typical case of artificial intelligence applied in the field of the smart city.

The accuracy and reliability in predicting short-term traffic flow is important. The K-nearest neighbors (K-NN) approach has been widely used as a nonparametric model for traffic flow prediction. However, the reliability of the K-NN model results is unknown and the uncertainty of traffic flow point prediction needs to be quantified. To this end, we extended the K-NN approach by constructing the prediction interval associated with the point prediction. Recognizing the stochastic nature of traffic, time interval used to measure traffic flow rate is remarkably influential. In this paper, extensive tests have also been conducted after aggregating real traffic flow data into time intervals, ranging from 3 minutes to 30 minutes. The results show that the performance of traffic flow prediction can be improved when the time interval increases. More importantly, when the time interval is shorter than 10 minutes, K-NN can generate higher accuracy of the point prediction than the selected benchmark model. This finding suggests the K-NN model may be more appropriate for traffic flow point and interval prediction at a shorter time interval.

Davatelji univerzalnih usluga imaju obvezu pružanja skupine minimalno potrebnih poštanskih usluga - poznatih kao obveza univerzalne usluge. Kako bi se osigurala obveza univerzalne usluge, često se uspostavljaju regulatorne mjere (kriteriji) koje davatelji usluga moraju ispuniti. U ovom radu provodi se geografska analiza navedenih kriterija, uz primjenu postojećeg regulatornog okvira u Republici Hrvatskoj. Na temelju gravitacijskog modela, prikazana je dostupnost poštanskih usluga. Cilj provedenog istraživanja jest istražiti primjenu gravitacijskog modela za određivanje dostupnosti poštanskih usluga, s posebnim naglaskom na ruralna područja. Prema našim spoznajama, u prethodnim istraživanjima ova metoda nije korištena kako bi se odredila dostupnost poštanskih usluga. Rezultati primijenjenog modela mogli bi se koristiti u budućem planiranju kriterija gustoće pristupnih točaka koristeći različite modove transporta.

In road safety, the process of organizing road infrastructure
network data into homogenous entities is called segmentation.
Segmenting a road network is considered the
first and most important step in developing a safety performance
function (SPF). This article aims to study the benefit
of a newly developed network segmentation method which is based on the generation of accident groups applying K-means clustering approach. K-means algorithm has been used to identify the structure of homogeneous accident groups. According to the main assumption of the proposed clustering method, the risk of accidents is strongly influenced by the spatial interdependence and traffic attributes of the accidents. The performance of K-means clustering was compared with four other segmentation methods applying constant average annual daily traffic segments, constant length segments, related curvature characteristics and a multivariable method suggested by the Highway Safety Manual (HSM). The SPF was used to evaluate the performance of the five segmentation methods in predicting accident frequency. K-means clustering-based segmentation method has been proved to be more flexible and accurate than the other models in identifying homogeneous infrastructure segments with similar safety characteristics.

E-tržišta su postala bitni dio e-trgovine. U istraživanju razvijena je decentralizirana platforma za e-tržište temeljena na agentima. Cilj istraživanja je poboljšati kvalitetu usluge opskrbnog lanca dopuštajući agentima i  tvrtkama da procjenjuju kvalitetu usluga svojih partnera kroz povijest transakcija. Slijedom toga,zbog činjenice da se u svakom čvoru opskrbnog lanca kontinuirano i autonomnoodlučuje o nabavi, poboljšava se kvaliteta usluga u cijelom lancu opskrbe. U članku empirijski se procjenjuje model procjene kvalitete usluga opskrbnog lanca.

Operation of means of transport is one of major sources of environmental impact. The goal of this article was to analyse the greenhouse gas emissions and to assess the impact of operation of means of road transport in Poland on human health using the life cycle assessment technique based on an analysis of emission of dust and gas pollutants. Road transport was assessed by taking the following means of transport into account: passenger cars, other cars with weight of up to 3,500 kg, lorries, buses, motorcycles, mopeds and tractors. The analysis covered various dust and gas pollutants, including the emission of CO₂, CO, N₂O, CH₄, NO_x, NMVOC, PM and SO₂. Using the IMPACT 2002+ life cycle impact assessment method, transport was assessed in a breakdown into the following impact categories: greenhouse gas emission and damage to human health, including damage caused by organic and inorganic compounds. It has been evidenced that the highest emissions of dust and gas pollutants are caused by passenger cars, which is mainly due to the number of vehicles of this type traversing Polish roads. The main cause of climate changes due to road transport is CO₂ emission, while NO_x emission is the main factor determining individual categories of damage to human health. The negative environmental impact is primarily related to the operation of combustion engine vehicles. Diesel oil and petrol are currently the main fuels used in Polish transport. In order to reduce their impact on the environment one should intensify the efforts aimed at increasing the share of alternative fuels in transport.

According to the Convention for the Safety of Life at Sea and International Convention on Maritime Search and Rescue, saving human lives at sea is the duty of all signatory states. This paper analyzes and gives an overview of previous research activities in search and rescue system at sea and how the use of unmanned aerial vehicles (UAV) can improve search and rescue actions at sea. Research activities include development of the search system and placement of resources that are used in search and rescue actions (ships, planes etc.). Previous research is mainly related to minimizing response time when accidents at sea are detected in relation to search and rescue missions. Implementation of unmanned aerial vehicles into the search and rescue system enables improvement of these actions due to earlier detection and verification of accidents at sea and prevents unnecessary search and rescue units engagement in cases when an accident did not occur. The results of previous research point to the fact that future research should aim to explore the synthesis of unmanned aerial vehicles with the existing search and rescue system at sea in Croatia.

Mileage data collected via surveys based on self-estimation, reports from garages and other sources which use estimations are rough estimates and differ greatly from the actual mileage. Vehicle mileage is a major factor in emission calculations and needs to be as accurate as possible to obtain reliable emission models. Odometer readings are collected annually at the periodic technical inspection in Croatia. Average mileage data were analyzed for vehicles up to 20 years of age in 2017. Vehicles were classified by curb weight and fuel type. Such classification proved to follow driver behavior and the intended purpose of the vehicle. For each vehicle class the model was applied using vehicle age and population size as inputs for calculating average mileage. Real data show that vehicles in Croatia considerably exceed the estimated mileage in the years following the first registration of the vehicle and that they cannot be compared to data collected in other studies based on estimations. The difference lies in the covered mileage after vehicles reach ten years of age. The outcome of this study has resulted in a model for calculating average vehicle mileage. The model is suitable for use in various analyses for Croatia or for countries with similar driving habits and economic status now and for years to come.