It is critical to implement accurate short-term traffic forecasting in traffic management and control applications. This paper proposes a hybrid forecasting method based on neural networks combined with the K-nearest neighbor (K-NN) method for short-term traffic flow forecasting. The procedure of training a neural network model using existing traffic input-output data, i.e., training data, is indispensable for fine-tuning the prediction model. Based on this point, the K-NN method was employed to reconstruct the training data for neural network models while considering the similarity of traffic flow patterns. This was done through collecting the specific state vectors that were closest to the current state vectors from the historical database to enhance the relationship between the inputs and outputs for the neural network models. In this study, we selected four different neural network models, i.e., back-propagation (BP) neural network, radial basis function (RBF) neural network, generalized regression (GR) neural network, and Elman neural network, all of which have been widely applied for short-term traffic forecasting. Using real world traffic data, the  experimental results primarily show that the BP and GR neural networks combined with the K-NN method have better prediction performance, and both are sensitive to the size of the training data. Secondly, the forecast accuracies of the RBF and Elman neural networks combined with the K-NN method both remain fairly stable with the increasing size of the training data. In summary, the proposed hybrid forecasting  approach outperforms the conventional forecasting models, facilitating the implementation of short-term  traffic forecasting in traffic management and control applications.

In this paper, the relation of the velocity of a vehicle in the slip mode to the parameters of the tire marks on the road surface is examined. During traffic accident reconstructions, the initial velocity of a sideslipping vehicle is established according to the tire mark trajectory radius, and calculations highly depend on the directly measured parameters of the tire marks, in particular cases known as yaw marks. In this work, a developed and experimentally validated 14-degree-of-freedom mathematical model of a vehicle is used for an investigation of the relation between velocity and trajectories. The dependence of initial vehicle velocity on tire yaw mark length and trajectory radius was found as a characteristic relation. Hence, after approximation of the permanent slipping part by a polynomial, the parameters of the latter were related to vehicle velocity. The dependences were established by specific experimental tests and computer-aided simulation of the developed model.

Safety is the key point of railway transportation, and railway traffic accident prediction is the main content of safety management. There are complex nonlinear relationships between an accident and its relevant indexes. For this reason, triangular gray relational analysis (TGRA) is used for obtaining the indexes related to the accident and the deep auto-encoder (DAE) for finding out the complex relationships between them and then predicting the accident. In addition, a nonlinear weight changing particle swarm optimization algorithm, which has better convergence and global searching ability, is proposed to obtain better DAE structure and parameters, including the number of hidden layers, the number of neurons at each hidden layer and learning rates. The model was used to forecast railway traffic accidents at Shenyang Railway Bureau, Guangzhou Railway Corporation, and Nanchang Railway Bureau. The results of the experiments show that the proposed model achieves the best performance for predicting railway traffic accidents.

A major problem connected with planning the organization of trains on a railway network is the optimization of the scheme of movement, which determines the routing and the number of trains. In this paper, an integrated approach of fuzzy linear programming method and multi-criteria analysis including three steps is proposed. In the first step, we defined the schemes of transportation of intercity trains and optimized each scheme in terms of direct operating costs by taking into account the uncertainty of passenger flows and utilization of train capacity using the fuzzy linear programming method. In the second step we determined the additional technological criteria to assess the variant schemes. The Fuzzy AHP method was applied to determine the weights of criteria. Using the results obtained from Fuzzy AHP, we prioritized the variant schemes of transportation by applying the PROMETHEE method. The third step presents the optimal choice of transportation of trains on a railway network based on minimum ratio of normalized costs and normalized PROMETHEE net outranking flow. In this step, the model uses the results obtained in the first and second steps. The practicability of the integrated approach is demonstrated
through the case study of Bulgaria’s railway network, and nine schemes were investigated. The model results and the real situation were compared. It was found out that the optimal scheme of intercity train transportation improves the service and reduces direct operating costs.

Drinking-driving behaviors are important causes of road traffic injuries, which are serious threats to the lives and property of traffic participants. Therefore, reducing the occurrences of drinking-driving behaviors has become an important problem of traffic safety research. Forty-eight male drivers and six female drivers who could drink moderate alcohol were chosen as participants. The drivers’ physiological data, operation behavior data, car running data, and driving environment data were collected by designing various virtual traffic scenes and organizing drivers to conduct driving simulation experiments. The original variables were analyzed by the Principal Component Analysis (PCA), and seven principal components were extracted as the input vector of the Radial Basis Function (RBF) neural network. The principal component data was used to train and verify the RBF neural network. The Levenberg-Marquardt (LM) algorithm was chosen to train the parameters of the neural network and build a drinking-driving recognition model based on PCA and RBF  neural network to realize an accurate recognition of drinking-driving behaviors. The test results showed that the drinking-driving recognition model based on PCA and RBF neural network could identify drinking drivers accurately during driving process with a recognition accuracy of 92.01%, and the operation efficiency of the model was high. The research can provide useful reference for prevention and treatment of drinking and  driving and traffic safety maintenance.

To scientifically and accurately evaluate the status of the development of green airports in China, evaluation methods of green, ecological airports are established in this paper. To address the shortcomings in subjective and objective weighting methods, we propose a combination weighting method based on Spearman’s rank correlation coefficient and evaluation grades based on interval approximation. At the same time, by taking into account resource conservation, environmental friendliness, operation efficiency, and people-oriented service, we propose an evaluation index system and an interval number for each index. Lastly, the theory is applied to five large airports in different regions of China. Analysis of the evaluation results shows that Shanghai Pudong International Airport (PVG) and Guangzhou Baiyun International Airport (CAN) have the highest scores for the resource conservation and environmental friendliness indexes, thus indicating that the development of a green ecological airport is closely related to its passenger transportation scale and economic strength. All considered airports showed the need for upgrading public  service facilities and constructing intelligent equipment. The method proposed in this paper is reasonable  and reliable; therefore, it can provide guidance for the evaluation and construction of green, ecological  airports.

In this paper, a methodology for creating and testing new proposed transport infrastructure is presented. It is based on microscopic traffic simulation of current and forecasted traffic demand and in-depth analysis of traffic flow. The most congested boulevard in Skopje has been chosen as a use case. Real-world traffic flow data was collected and used in the calibration and validation of a microscopic simulation model. Three possible configurations of new urban mobility infrastructure have been proposed and best one chosen using appropriately defined criteria. The proposed configurations were evaluated from the aspect of traffic performances, suitability for forecasted future traffic demand, and vehicle emissions. The obtained results prove the effectiveness of the presented methodology in reducing delays and vehicle emissions and significantly improving the level of service of the chosen use case.

Small cities with less than 200,000 inhabitants do not usually suffer from chronic congestion problems. However, private vehicles are used excessively, making it necessary to implement measures to encourage further use of public transport and pedestrian mobility to make it more sustainable. Bypasses improve level of service (LOS) by removing cars from the city center, leading to significant reductions in overall travel time. Most studies so far have been conducted in large cities suffering chronic congestion problems, so the aim of this research is to analyze the effects of bypasses in small and non-congested cities through the construction of a traffic model in Badajoz (Spain), starting with the allocation of the origin-destination travel matrix derived from surveys and traffic counts conducted at the southern and eastern accesses. The traffic model describes the mobility in potentially-capturable future southern traffic relationships and allows insights into different alternatives in the construction of a new high LOS road. This research concludes that small cities with no chronic congestion problems should plan bypasses as close as possible to the city, since they are the most economical, produce greater traffic capture, greater time savings, and eliminate the largest number of CO₂ emissions from the urban center. The more distant alternatives have a higher LOS, however, these are longer and more expensive solutions that also capture less traffic and thus eliminate less CO₂ emissions.

To apply the experimental data measured in a wind tunnel for a scaled aircraft to a free-flying model, conditions of dynamical similarity must be met or scaling procedures introduced. The scaling methods should correct the wind tunnel data regarding model support, wall interference, and lower Reynolds number. To include the necessary corrections, the current scaling techniques use computational fluid dynamics (CFD) in combination with measurements in cryogenic wind tunnels. There are a few methods that enable preliminary calculations of typical corrections considering specific measurement conditions and volume limitation of test section. The purpose of this paper is to present one possible approach to estimating corrections due to sting interference and difference in Reynolds number between the real airplane in cruise regime and its 1:100 model in the small wind tunnel AT-1. The analysis gives results for correction of axial and normal force coefficients. The results of this analysis indicate that the Reynolds number effects and the problem of installation of internal force balance are quite large. Therefore, the wind tunnel AT-1 has limited  usage for aerodynamic coefficient determination of transport airplanes, like Dash 8 Q400 analyzed in this paper.

The importance of the Port of Ploče lies in serving the majority of the Bosnian market. However, the Pan-European Corridor Vc provides access to a much wider market in Central and South-Eastern Europe. The purpose of this paper is to express views on the future development of the Corridor and its consequential impact on the Port. This was conducted by means of analysis, comparison, and synthesis of cargo flow data and the dynamics data of the Corridor Vc construction. It covers the relations between Bosnia and Herzegovina (BiH) and the Republic of Croatia, and the assessment of importance of the Corridor in those countries. Statistical indicators show the importance of the Corridor completion for the successful execution of port development plans. The analysis of the Corridor status points to the current prevailing circumstances in BiH that make its realization ultimately uncertain, especially its railway component. The findings show that the most significant obstacles for a successful realization of Port of Ploče development plans are not only within the BiH internal geo-political relationships but in the disputes between BiH and the Republic of Croatia (RH) as well. Consequently, it has been shown that the Port of Ploče is not able to define and carry out the necessary measures toward BiH on its own but necessarily with the participation of the RH Government. The analysis offers observations and recommendations for improving relations with BiH, which would significantly advance the completion of the Corridor in BiH. In this way, it would allow for a full establishment of the Port of Ploče on target markets.

The aim of this study is to develop a framework for investigating a comprehensive set of Key Performance Indicators (KPIs) for the assessment of railway Intelligent Transportation Systems (ITS). The framework is established through four main steps: (1) development of a comprehensive set of KPIs for railway ITS; (2) validation of developed KPIs and collection of judgments from experts through a Delphi questionnaire; (3) evaluation of KPIs weights for assessing railway ITS with the Group Analytical Hierarchy Process (GAHP); and (4) presentation of a SWOT analysis for the developed KPIs by the authors. The results of the framework are presented as a set of 25 indicators for evaluation of railway ITS and their impacts. The framework could be helpful for selecting KPIs of ITS in another mode of transportation. Monitoring of the contributions of ITS towards sustainable railway can be achieved by a developed set of indicators which are classified in accordance with sustainable dimensions.