This publication presents a multi-faceted analysis of the fuel consumption of motor vehicles and the way human impacts the environment, with a particular emphasis on the passenger cars. The adopted research methodology is based on the use of artificial neural networks in order to create a predictive model on the basis of which fuel consumption of motor vehicles can be determined. A database containing 1750 records, being a set of information on vehicles manufactured in last decade, was used in the process of training the artificial neural networks. The MLP (Multi-Layer Perceptron) 22-10-3 network has been selected from the created neural networks, which was further subjected to an analysis. In order to determine if the predicted values match the real values, the linear Pearson correlation coefficient r and coefficient of determination R² were used. For the MLP 22-10-3 neural network, the calculated coefficient r was within range 0.93–0.95, while the coefficient of determination R² assumed a satisfactory value of more than 0.98. Furthermore, a sensitivity analysis of the predictive model was performed, determining the influence of each input variable on prediction accuracy. Then, a neural network with a reduced number of neurons in the input layer (MLP-20-10-3) was built, retaining a quantity of the hidden and output neurons and the activation functions of the individual layers. The MLP 20-10-3 neural network uses similar values of the r and R² coefficients as the MLP 22-10-3 neural network. For the evaluation of both neural networks, the measures of the ex post prediction errors were used. Depending on the predicted variable, the MAPE errors for the validation sets reached satisfactory values in the range of 5–8% for MLP 22-10-3 and 6–10% for MLP 20-10-3 neural network, respectively. The prediction tool described is intended for the design of passenger cars equipped with internal combustion engines.

This paper presents the issues of modeling the operation process of light utility vehicles operating in military transport systems. The required condition for the effective operation of the system is to maintain the means of transport at the appropriate level of technical readiness. For this purpose, it is necessary to equip the technical system with appropriate resources enabling the efficient implementation of fuel refilling, maintenance and repair processes. Each failure of the means of transport causes a significant reduction in transport capacity, which then results in the inability to perform the planned tasks. Quality control and vehicle operation process management require advanced mathematical methods and tools. Three indicators have been proposed as quantitative characteristics for assessing and optimizing the availability of military vehicles: functional readiness, technical efficiency and airworthiness. To determine their value, a stochastic exploitation model was developed based on the application of the theory of Markov processes. Based on the collected empirical data, a nine-state phase space of the studied process was identified. Operating states were distinguished relating to the implementation of the transport task, refueling, parking in the garage, as well as maintenance and repairs. As part of the considerations for the continuous time, verification of the distributions of time characteristics led to the development of a semi-Markov model. The ergodic probabilities calculated based on the conditional probability matrix of interstate transitions and the expected values of the time spent in the states were used to determine the indicators of functional availability, efficiency and technical suitability. In order to determine the possibility of optimizing the process, a sensitivity analysis was performed. Reducing the amount of time the vehicles must wait for repair by about 50% can improve the values of the indexes from 0.91 to 0.95.

The paper proposes a methodology of reliability testing as applied to vehicles used in military transport systems. After estimating the value of the reliability function using the Kaplan–Meier estimator, reliability models were developed and analysed. The neural model, which achieved the value of the correlation coefficient R exceeding 0.99, was determined to fit the empirical data the best. On the basis of the approximated reliability function of several models, the reliability characteristics of the tested sample of vehicles were determined. Plots of the failure probability density function for all three models had similar courses over a significant part of the function domain. A failure intensity function was also determined, which varied between models. For the exponential and Weibull model, the expected mileage between failures was calculated, which proved impossible for the neural model. The proposed methodology is capable of modelling reliability characteristics based on the observation of an assumed period of the exploitation process of the selected group of military vehicles.

The evolution of changes in shopping in the modern society necessitates suppliers to seek new solutions consisting of increasing the efficiency of transport processes. When it comes to controlling the flow of goods in modern distribution networks, planning and timely deliveries are of particular importance. The first factor creating a competitive advantage involves the tendency to shorten order delivery times, especially for products with a short shelf life. Shorter delivery times, in turn, extend the period of effective residence of the product “available on the shelf”, increasing the likelihood of its sale. The second component in line with the Sustainable Development Strategy consists of aspects related to the protection of the natural environment, in particular those related to car transport. In this case, the fuel consumption and the level of emitted toxic substances (including carbon dioxide) are analyzed and assessed. Bearing in mind the above, this article presents the problem of optimizing the delivery time within the assumed distribution network and its solution, enabling the company to develop and optimal plan for the transport of products with a short shelf life. The paper proposes a model that takes into account minimization of the delivery time, while estimating the values of fuel consumption and CO₂ emissions for the variants considered. The means of transport were medium-duty trucks. Three variants of the assumptions were considered, and algorithms implemented in MS Excel and MATLAB software were used to perform the optimization. Using the MATLAB environment, a more favorable value of the objective function was obtained for the variant without additional constraints. On the other hand, the algorithm implemented in MS Excel more effectively searched the set of acceptable solutions with a larger number of constraining conditions.

Vehicles are important elements of military transport systems. Semi-Markov processes, owing to the generic assumption form, are a useful tool for modelling the operation process of numerous technical objects and systems. The suggested approach is an extension of existing stochastic methods employed for a wide spectrum of technical objects; however, research on light utility vehicles complements the subject gap in the scientific literature. This research paper discusses a 3-state semi-Markov model implemented for the purposes of developing reliability analyses. Based on an empirical course of the operation process, model was validated in terms of determining the conditional probabilities of interstate transitions for an embedded Markov chain, as well as parameters of time distribution functions. The Laplace transform was used to determine the reliability function, failure probability density function, failure intensity, and expected time to failure. Readiness index values were calculated based on ergodic probabilities.

The article discusses the issue of modelling traffic flows and the transport network. Faced with an increase in the number of vehicles in road networks, the problem of congestion and the need to optimise traffic and adapt the transport infrastructure to changing demand are growing, especially in large cities. With this in mind, the authors of this publication developed a model of the road network in the north-eastern part of the Warsaw agglomeration based on the proposed algorithm. Two methods were used to optimise the distribution of traffic flows: the Nash equilibrium and the Stackelberg approach. The Nash equilibrium assumes the aim of achieving equal average times on all roads for each origin–destination (O-D) pair. This describes the state pursued by a decentralised system guided by the individual benefits of the traffic users. On the contrary, the Stackelberg approach aims to achieve optimal travel times for the entire system. The study was carried out for three scenarios that differed in the assumed traffic demand on the road network. The basic scenario assumed the average hourly traffic demand during the morning peak hour based on traffic measurements. On the other hand, the two alternative scenarios were developed as a 10% variation in traffic volumes from the baseline scenario. On the basis of the results, it was concluded that an increase in traffic volumes for all O-D pairs could result in a decrease in traffic volumes on some links of the road network. This means that the transport network is a complex system and any change in parameters can cause significant and difficult to predict changes. Therefore, the proposed approach is useful in terms of traffic forecasting for road networks under conditions of changing traffic flow volumes. Additionally, the total travel time for the entire system differed for each scenario by a percentage difference of 0.67–1.07% between the optimal solution according to the Nash equilibrium and the Stackelberg approach.