This paper considers the problem of evaluating the effectiveness of the finished goods warehouse of a manufacturing company, in which a modified TPM method - Total Productive Management (TPM2) - was applied to improve productivity. A multi-stage methodology was proposed, including a decision to modify the system, determination of the scope of changes, monitoring the results obtained and a multi-criteria evaluation of the changes made. The decision to make modifications to the existing system was motivated by the lower-than-expected quality of customer service (frequent delivery delays). With regard to the transport department, lean flow pillar activities were focused on analysing losses (muda) in warehouse processes (product loading and package unloading). The purpose of these activities was to minimise interruptions in warehouse processes (product loading and package unloading). "The steps for solving the problem" methodology based on Deming's PDCA cycle was used to solve the problem. The analysis covered, among other things, the information flow processes between production planning and the customer service department, the planning processes of the dispatcher, the efficiency of the loading processes, and the causes of interruptions in warehouse operations. The analyses employed the chronometry of selected works, the 5W + 1H method and the Pareto method. By using the 5S method and some characteristics of the SMED method, the organisation of loading work was decisively changed (shunting yard changes, appropriate buffers for transport equipment). The changes introduced in the system were monitored for several months. Appropriately defined OEE indicators were used to assess the behaviour of the system after the changes. The indicators consider the use of available warehouse time, the efficiency of the loading process and the quality of the tasks performed. The results that can be achieved are presented using the specific example of the finished goods warehouse of a manufacturing company in the FMCG sector.

With the rapid development of intelligent rail transportation, the realization of intelligent detection of railroad foreign body intrusion has become an important topic of current research. Accurate detection of rail edge location, and then delineate the danger area is the premise and basis for railroad track foreign object intrusion detection. The application of a single edge detection algorithm in the process of rail identification is likely to cause the problem of missing important edges and weak gradient change edges of railroad tracks. It will affect the subsequent detection of track foreign objects. A combined global and local edge detection method is proposed to detect the edges of railroad tracks. In the global pixel-level edge detection, an improved blok-matching and 3D filtering (BM3D) algorithm combined with bilateral filtering is used for denoising to eliminate the interference information in the complex environment. Then the gradient direction is added to the Canny operator, the computational template is increased to achieve non-extreme value suppression, and the Otsu thresholding segmentation algorithm is used for thresholding improvement. It can effectively suppress noise while preserving image details, and improve the accuracy and efficiency of detection at the pixel level. For local subpixel-level edge detection, the improved Zernike moment algorithm is used to extract the edges of the obtained pixel-level images and obtain the corresponding subpixel-level images. It can enhance the extraction of tiny feature edges, effectively reduce the computational effort and obtain the subpixel edges of the orbit images. The experimental results show that compared with other improved algorithms, the method proposed in this paper can effectively extract the track edges of the detected images with higher accuracy, better preserve the track edge features, reduce the appearance of pseudo-edges, and shorten the edge detection time with certain noise immunity, which provides a reliable basis for subsequent track detection and analysis.

The carbon emissions are essential for climate change and 26% of the world's carbon emissions are related to transport. But focusing only on fewer carbon emissions might be biased at times. In order to keep a balance be-tween economic growth and carbon emissions reduction, this paper evaluated the performance of carbon control by considering the input factors and output factors together, which is more comprehensive and reliable. Firstly, this paper has calculated the transport carbon emissions reduction efficiency (TCERE) based on the model of super SBM with undesirable outputs. The input factors include capital stock, labor force and fossil energy con-sumption. And the output factors include gross domestic product and carbon dioxide emissions. Then the influenc-ing factors of TCERE were analyzed using econometric models. The economic growth, transport structure, tech-nology level and population density were posited as influencing factors. This paper creatively proposed the per capita nighttime lights brightness as a new indicator for economic growth. An empirical study was conducted in East China from 2013 to 2017, and this study has found that the relationship between TCERE and economic growth shows an U-shape. Besides, transport structure and technology level both show a positive impact on TCERE. The implications of our findings are that: (a) The TCERE declines slower in East China, giving us reason to believe that the improvement of TCERE is predictable; (b) When economic growth exceeds the turning point, economic growth is conducive to the improvement of TCERE. We could develop the economy boldly and confident-ly; (c) Increased investment in railway and waterway transportation infrastructure projects is needed to strengthen the structure of the railway and waterway transportation systems. Furthermore, the general public and businesses should be encouraged to prefer rail or river transportation; (d) Investment in scientific and technological innova-tion should be enhanced in order to produce more efficient energy-use methods.

The rational planning of land around rail transit stations in cities can effectively improve the convenience of transportation and economic development of cities. This paper briefly introduced the transit-oriented development (TOD) mode of urban planning. We constructed a hierarchical structure for evaluating the quality of land plan-ning of urban rail transit stations through the analytic hierarchy process (AHP) method. The structure started from three large aspects, i.e., traffic volume, regional environmental quality, and regional economic efficiency, and every large aspect was divided into three small aspects. Then, an optimization model was established for land planning of rail transit stations. The land planning scheme was optimized by a genetic algorithm (GA). To enhance the optimization performance of the GA, it was improved by coevolution, i.e., plural populations iterated inde-pendently, and every population replaced the poor chromosomes in the other populations with its excellent chro-mosomes in the previous process. Finally, the Jinzhonghe street station in Hebei District, Tianjin city, was taken as a subject for analysis. The results suggested that the improved GA obtained a set of non-inferior Pareto solutions when solving a multi-objective optimization problem. The distribution of solutions in the set also indicated that any two objectives among traffic volume, environmental quality, and economic efficiency was improved at the cost of the remaining objectives. The land planning schemes optimized by the particle swarm optimization (PSO) algo-rithm, the traditional GA, and the improved GA, respectively, were superior than the initial scheme, and the opti-mized scheme of the improved GA was more in line with the characteristics of the TOD mode than the traditional one and the PSO algorithm, and the fitness value was also higher. In conclusion, the GA can be used to optimize the planning design of land in rail transit areas under the TOD mode, and the optimization performance of the GA can be improved by means of coevolution.

The article presents a method of determining the characteristics of exhaust emissions and fuel mass consumption in real driving conditions based on a single test using the Monte Carlo method. The exhaust emission characteristics used are the relations between the emissions and the average vehicle speed, and the characteristic of the fuel mass consumption is the dependence of the fuel mass consumption at the average vehicle speed. The results of empirical research of a passenger car with a spark-ignition engine in the RDE test were used. The use of the Monte Carlo method made it possible to select the initial and final moments of averaging the process values, thanks to which it was possible to determine the discrete values of the characteristics for various values of average vehicle speeds. The determined discrete characteristics of the particulate mass and number emissions and fuel mass consumption relative to the average vehicle speed were approximated by polynomial functions of the second and third degree. The determined discrete characteristics, presented as sets of points, were characterized by a relatively small dis-persion in relation to their polynomial approximations. The average relative deviation of the points of discrete characteristics from the value of the polynomial was in most cases small less than 4%, only in the case of the number of particles emitted deviated from this, as the average relative deviation of the measured points from the determined polynomial was nearly 14%. Combined with the results of RDE empirical studies, the Monte Carlo method proved to be an effective method for determining the characteristics of exhaust emissions, measured in real vehicle operating conditions. The main advantage of the proposed method was a significant reduction in the actual workload necessary to carry out the empirical research where it became possible to determine the charac-teristics in a large range of vehicle average speed values with just one drive test. Using standard methods of meas-uring this type of data, it would be necessary to conduct multiple tests, driving at different average vehicle speeds.

Scheduling buses in public transport systems consists in assigning trips to vehicle blocks. To minimize the cost of fuel and environmental impact of public transport, the number of vehicle blocks used should be as small as possi-ble, but sufficient to cover all trips in a timetable. However, when solving real life transportation problems, it is difficult to decide whether the number of vehicle blocks obtained from an algorithm is minimal, unless the actual minimal number is already known, which is rare, or the theoretical lower bound on the number of vehicles has been determined. The lower bound on the number of vehicle blocks is even more important and useful since it can be used both as a parameter that controls the optimization process and as the minimum expected value of the respective optimization criterion. Therefore, methods for determining the lower bound in transportation optimiza-tion problems have been studied for decades. However, the existing methods for determining the lower bound on the number of vehicle blocks are very limited and do not take multiple depots or heterogeneous fleet of vehicles into account. In this research, we propose a new practical and effective method to assess the lower bound on the number of vehicle blocks in the Multi-Depot Vehicle Scheduling Problem (MDVSP) with a mixed fleet covering electric vehicles (MDVSP-EV). The considered MDVSP-EV reflects a problem of public transport planning encoun-tered in medium-sized cities. The experimental results obtained for a real public transport system show the great potential of the proposed method in determining the fairly strong lower bound on the number of vehicle blocks. The method can generate an estimated distribution of the number of blocks during the day, which may be helpful, for example, in planning duties and crew scheduling. An important advantage of the proposed method is its low calculation time, which is very important when solving real life transportation problems.

Suggesting the proper location for logistics facility can be considered as a decision making problem, wherein the final solution/decision is affected by multiple external or even internal circumstances. In order to address the decision making issues, various multi-criteria decision making (MCDM) techniques may be implemented; and hence, they can be applied even when making a decision about an adequate logistics service center (LSC) placement in an examined territory (i.e., national logistics network of the selected territory), which is an aim of this manuscript. Following the statements above, as for the individual instruments of MCDM to be implemented in terms of the crucial objective of this research, the definite decision making process will be carried out by applying the Analytic Hierarchy Process (AHP) followed by the Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS), on the basis of criteria weights defined by the Saaty pairwise comparison method. The methods used appear to be ideal instruments towards decision making on the most suitable location which is represented by the region in our case. Subsequently, these will be ordered from the most preferred to least one by using a preference ranking. As a result of the application of AHP and TOPSIS approaches, based on the conducted calculations in regard to decision making on identifying the proper LSC location out of eight selected regions, one specific region will be defined as the most suitable (so-called compromise) scenario. Individual tools allow for reducing the number of assigned criteria that are taken into account in searching process for individual solutions. In order to objectify the entire decision making procedure, ten topic-involved experts having practical experience with a subject of logistics object allocation will be asked to participate in the process. Preferences differ from one decision maker (expert) to another; hence, the outcome depends on who is making decisions and what their goals and preferences are.

The Covid-19 pandemic unexpectedly shook the entire global economy, causing it to destabilize over a long period of time. One of the sectors that was particularly hit hard was air traffic, and the changes that have taken place in it have been unmatched by any other crisis in history. The purpose of this article was to identify the time series describing the number of airline flights in Poland in the context of the Covid-19 pandemic. The article first presents selected statistics and indicators showing the situation of the global and domestic aviation market during the pandemic. Then, based on the data on the number of flights in Poland, the identification of the time series describing the number of flights by airlines was made. The discrete wavelet transformation (DWT) was used to determine the trend, while for periodicity verification, first statistical tests (Kruskal-Wallis test and Friedman test) and then spectral analysis were used. The confirmation of the existence of weekly seasonality allowed for the identification of the studied series as the sum of the previously determined trend and the seasonal component, as the mean value from the observations on a given day of the week. The proposed model was compared with the 7-order moving average model, as one of the most popular in the literature. As the obtained results showed, the model developed by the authors was better at identifying the studied series than the moving average. The errors were significantly lower, which made the presented solution more effective. This confirmed the validity of using wavelet analysis in the case of irregular behaviour of time series, and also showed that both spectral analysis and statistical tests (Kruskal-Walis and Fridman) proved successful in identifying the sea-sonal factor in the time series. The method used allowed for a satisfactory identification of the model for empirical data, however, it should be emphasized that the aviation services market is influenced by many variables and the fore-casts and scenarios created should be updated and modified on an ongoing basis.

A substantial part of the environmental issues relies on fossil fuels. This dependence is crucial in transport even though many incentives and interventions have been proposed to reduce pollutant emissions. Electric vehicles with zero emissions might represent a viable solution in urban areas. Many cities encouraged modal shift policies from cars to an e-bike or car-sharing/pooling with electric vehicle fleets. This paper reports the ongoing outputs from a pilot project, relying on a modal shift to the e-bike, promoted in the city of Messina (Southern Italy) by the Ministry of Ecological Transition. The objective is to assess, in the territorial context of Messina, the e-bike as a competitive transport mode in terms of social awareness of eco-friendly mobility solutions. The available dataset consists of about nine months of observations; data on total distance and trips have been gathered for each e-bike. It emerged how, in a typical working day, the average distance travelled is about 6.9 km, the usage rate for working days is about 81 %, and the carbon dioxide reduction is about 245 kg per person each year. During the project, information was also collected on the satisfaction with the e-bike and the quality of travel. It emerged that regular bicycle use has good repercussions on the interviewees' psycho-physical well-being, reducing the stress factor connected with urban mobility. Despite mechanical breakdowns and the lack of an infrastructure dedicated to active mobility representing a limitation, travel comfort and safety are two latent variables that are transversally valid within the population; about 15 % became familiar with the e-bike and made it their primary mode choice for everyday activities. In this sense, outputs represent a starting point for future policies and give back adjustments before introducing similar services to students from the university and second-grade schools.

The Covid-19 pandemic has significantly affected the economic and social spheres of all countries. Restrictions intro-duced to reduce the risk of transmission have changed the structure of population movements. The impact of these restrictions on the characteristics of intercity travel is still an understudied problem. Based on the analysis of statisti-cal data and the results of questionnaires, the article assesses the impact of pandemic restrictions on population mobil-ity in the Western region of Ukraine and changes in the distribution of passenger flows between different modes (bus, rail, private transport, joint travel). In 2020, the volume of passenger traffic in the region decreased by an average of half compared to the previous year. The decline is sharper for rail passenger transport compared to the bus transport. For more developed railway networks, the impact of the pandemic on passenger traffic is more pronounced. Quarantine restrictions have also increased the share of own car travel. According to research, the distribution of intercity trips between modes is influenced by the age and sex of the traveler. During the pandemic, users of transport services who travel with children under the age of 14 choose private transport to travel more often than those who travel alone. The degree of influence of the above factors on the distribution of modes depends on the length of the trip. The application part of the work presents the results of modeling passenger flows of the studied region in the software environment PTV Visum. It was found that at the beginning of the quarantine restrictions the number of intercity trips decreases sharply. As the duration of restrictions increases, the rate of decline in mobility decreases. These data can be further taken into account when planning the work of transport enterprises and meeting the population`s demand for travel. The practi-cal application of the study results is that the identification of trends in the mobility of residents of the studying region depending on the impact of pandemic restrictions allows you to predict the mode and type of vehicles used. Based on these data, you can determine marketing strategies for the development of certain modes and directions of transporta-tion.