This study examines the Public Bicycle Sharing System (PBSS) in Ahmedabad, the patterns of ridership, user behavior, and spatial distribution in various density zones based on the data provided by MYBYK. In 2023, 577,228 rides were registered, and high ridership areas were concentered around educational and recreational locations, which recorded 350,000 rides in 132 stations. In the high-density zones, the analysis indicates that most of the preference is for the short-distance ride, 55 percent of the total number of trips are made by zero-displacement rides, which is a good indicator that the trend is shifting towards leisure cycling. The monthly ridership trends show some significant variations, with the highest number of 68,529 rides in March, indicating that significant operation policies should be implemented when demand is very high. The dominance of short-term subscriptions (87.3%) indicates the users' preference for flexibility, whereas the number of rides longer than 5 km (424,893 trips) shows a growing tendency towards longer trips. Furthermore, a multinomial logistic regression model was used to identify factors affecting the use of PBSS, with emphasis on the relationships between the type of subscription, length of trip, time of use, and weather conditions. The model findings indicate that short-term users tend to have shorter rides, and the length of the trip is highly influenced by climatic conditions. The results can be useful to policymakers and urban planners to optimize the PBSS operations and improve sustainable mobility in Ahmedabad and other urban settings.

Both single- and multi-agent reinforcement learning have been widely used to solve different problems of autonomous driving. Single-agent methods have already outperformed traditional rule-based algorithms in several areas, but recent research shows that multi-agent algorithms have an even greater potential. Encouraging cooperative behavior and communication between the agents leads to their altruistic behavior, which results in safer and more reliable driving strategies while applying techniques such as parameter sharing and curriculum learning helps to deal with the increased complexity of the problem. This paper aims to compare the performance and reliability of single- and multi-agent reinforcement learning algorithms through the example of a yield-sign controlled intersection.

Human resources are among the most critical assets of large organizations. Human resource management and employee motivation play a key role in organizational performance, presenting a constant challenge for senior management. The Iranian Railway Company, employing over 22,000 staff and transporting nearly 50 million t of cargo annually, is one of the largest state-owned enterprises in Iran. Employee satisfaction significantly influences the company's performance indicators. The equitable allocation of motivational benefits impacts freight and passenger volumes, as well as transportation safety. Failure to allocate these benefits fairly can lead to employee dissatisfaction, undermining motivation and performance.This study presents a method for allocating welfare and motivational benefits based on employee performance. Key performance criteria within the railway industry, such as net tkm, loading and unloading tonnage, wagon travel speed, income, accident rates, and passenger train delays, were identified. The relative importance of these criteria was determined using the pairwise comparison method. Monthly values for each criterion were calculated across 21 regions of the Iranian railway network. Motivational resources were then allocated accordingly through a hierarchical approach. The implementation of this model led to improvements in key productivity indicators, such as freight volumes and transportation efficiency, thereby enhancing overall company performance and employee satisfaction.

There are many studies on the impact of high-speed rail (HSR) on airplane travel demand, economic growth, connectivity, accessibility, and regional development. However, the degrees to which HSR affects the demand for conventional passenger trains and operation of conventional rail are rarely discussed. This study attempted to fill this gap by identifying and investigating the potential impact of introducing a high-speed rail network in Indonesia, focusing on its implications for conventional passenger trains and the overall sustainability of the transportation system, utilizing an origin-destination matrix, a stated preference survey and choice analysis. The results show that fare is an influential attribute compared to travel time. High-speed trains are substitutes for medium to long distance and complementary for short distance travel. The study provides valuable insights for transport operators, policymakers, and planners. Key findings include a nuanced understanding of potential demands, travel attributes, strategic considerations for optimizing HSR operational plans and sustainable transport.

The aim of the paper is to investigate the wind effect on vehicle aerodynamic properties. Two-dimensional Ahmed body is examined with Computational Fluid Dynamics (CFD) method. During the analysis, two different studies were carried out. First, the simplified vehicle shape was examined in standing position, only the force from the wind velocity affected the body. The drag coefficient change was examined in case of different wind velocities and angles of attack. After that, the effect of wind while driving was investigated. The body was defined as a moving object and at another inlet the wind was defined. Different travelling and wind velocities with different angles of attack were studied. Based on the results of the simulations, a comprehensive impact of the wind can be measured on the drag coefficient. This proves that the wind has a measurable effect on vehicle aerodynamic properties and must be taken into account when investigating the effect of weather conditions on vehicle aerodynamic properties.

Creating a city that prioritizes the needs of children and youth is crucial for sustainable urban development. This paper reviews and explores best practices from international experiences excelling in Transportation Safety, Mobility, Accessibility, Sustainability, Climate Change, Inclusivity, 6 Es Approach, and Youth Empowerment and proposes an integrated and comprehensive framework for the establishment of the best practice child- and youth-friendly cities that would contribute to enhancing the safety of young people. The practices were assessed using predefined evaluation criteria to identify gaps, and then the elements and the requirements of the integrated and comprehensive framework were determined to bridge the identified gaps. An intensive review of 13 practices revealed that most studies (10 out of 13) fully considered Inclusivity and partly considered the 6 Es Approach criteria. However, the least considered criteria were Transportation Safety, Mobility, Climate change and Youth Empowerment. Interestingly, among all studies, only one considered Climate Change. The study concludes that future studies should incorporate underrepresented areas to bridge gaps. It emphasizes that local governments should prioritize child- and youth-friendly initiatives, adhering to human and child rights principles and recognized standards like the UNICEF Local Governance Approach. The study also highlights the importance of fostering meaningful participation from youth and children, focusing on capacity building, and committing to non-discrimination. Finally, it calls for clear roles and responsibilities for local governments, partnerships with public and private sectors, and regular monitoring and assessment to ensure the sustainability and success of these initiatives.

One of the contemporary trends is to emphasize the benefits of using hybrid drives over conventional ones. Due to the growing popularity of this category of drives, research on the optimization of energy consumption is becoming increasingly popular. The paper presents research on a selected plug-in hybrid drive unit of one of the most popular vehicle manufacturers in the European Union. The research process itself was based on the assessment of the use of active DISTRONIC cruise control as a tool that can influence the optimization of energy consumption of the tested drive unit. The experiment was conducted in real road conditions. Analyses were carried out for several drive system configuration modes: hybrid, E-mode, E-save and charging. The results of the experiment indicated the most optimal drive setting mode on expressways. These analyses emphasize the significant role of intelligent systems as a tool that allows for the optimization of energy consumption of vehicles equipped with plug-in hybrid units. The presented research is dedicated to users of this category of vehicles, while at the same time providing an answer to the question of how to effectively use the described drive unit in real operating conditions.

Labor mobility between the Czech Republic and Germany represents a significant factor for the economic and social integration of cross-border regions. The manuscript focuses on the proposal of a methodology in the context of retrieving and analyzing data on labor mobility, with the aim of creating a strategy to support cross-border cooperation. Through a combination of specific quantitative and qualitative methods, the manuscript analyzes the key determinants of mobility, barriers, challenges and factors affecting the commuter decision-making. The findings show that the major obstacles to cross-border mobility include administrative burden, differences in working conditions, and institutional misalignment. The manuscript also discusses the possibility of optimizing the current data retrieval system using advanced technologies and analytical tools. The conclusions of the study provide suggestions for streamlining cross-border mobility and supporting labor market integration in the Czech-German border region.

This literature review categorizes machine learning studies in traffic accident severity prediction, providing a comprehensive overview of the diverse applications and advancements in this field. It begins with a comparative analysis of machine learning models, highlighting the performance of various algorithms such as Random Forest, XGBoost, and Support Vector Machines (SVM) in predicting accident severity. The review also explores factor-specific studies, emphasizing the influence of road, environmental, and vehicle-related factors on crash outcomes. These studies demonstrate the critical role of factors such as road type, weather conditions, and vehicle characteristics in determining accident severity. Additionally, crash-type-specific prediction models have been developed, showcasing the ability of machine learning models to tailor predictions based on the nature of the crash, whether involving pedestrians, vehicles, or specific collision types. The review also examines hybrid and ensemble approaches, which combine multiple algorithms to enhance prediction accuracy. These approaches leverage the strengths of individual models to improve overall performance, offering a promising direction for future research. By categorizing the studies into these key areas, this review provides a structured understanding of the state-of-the-art in machine learning applications for traffic accident severity prediction and identifies opportunities for further development to enhance prediction robustness, accuracy, and applicability in real-world traffic safety management.

Network sensitivity has recently become an important topic, with an increasing number of articles appearing on the subject. Identification of critical sections within transport networks is also essential for transport modelling. A well-developed methodology can help to improve the network to minimize the impact on road users in the event of future emergency cases (accidents or attacks). For this purpose, existing articles were assessed to determine what progress has been made in network sensitivity and what parameters and approaches have been used so far. After reviewing the articles, it became clear that a significant number of methods have been used to identify critical sections, and it became visible which areas are worth further work. Among the methodologies used, simplified networks have been the main focus, but there is a need for a more detailed analysis based on industry demand and available data.