Abstract: Efficient last-mile delivery remains a critical challenge for rural agricultural logistics, globally, particularly in cold-climate regions with dispersed agricultural operations. This review evaluates the potential of GIS-enabled truck–drone hybrid systems to overcome infrastructural, environmental, and operational barriers in such settings. This study uses North Dakota, USA as a representative case alongside insights from similar rural regions worldwide. The study conducts a systematic review of 82 high-quality publications. It identifies seven interconnected research domains: GIS analytics, truck–drone coordination, smart agriculture integration, rural implementation, sustainability assessment, strategic design, and data security. The findings stipulate that GIS enhances hybrid logistics through route optimization, launch site planning, and real-time monitoring. Additionally, this study emphasizes the rural, low-density context and identifies specific gaps related to cold-weather performance, restrictions to line-of-sight operations, and economic feasibility in ultra-low-density delivery networks. The study concludes with a roadmap for research and policy development to enable practical deployment in cold-climate agricultural regions.

Abstract: Speed prediction is fundamental to optimizing energy management strategies. Common evaluation metrics such as RMSE and MAE focus primarily on the numerical deviation between predicted and actual speeds. However, when applied to hybrid vehicle energy management strategy optimization, speed prediction models based on these metrics show a random deviation between energy consumption results and the theoretical optimal, indicating that these metrics are not effective in this application domain. To explore a more effective method for evaluating the practical application of speed prediction curves, this study uses multiple metrics to assess numerous speed prediction curves and analyses the correlation between each metric and the deviation from the optimal energy consumption during energy management strategy optimization. The results show that considering acceleration is more aligned with the needs of energy management strategy optimization than merely evaluating the proximity of speed values. Specifically, the standard deviation of the acceleration time ratio deviation performs better than traditional metrics like RMSE and MAE in distinguishing the effectiveness of speed prediction curves. The smaller the standard deviation of the acceleration time ratio deviation between the predicted and actual speed curves, the closer the energy consumption results of energy management based on the predicted speed curve are to the theoretical optimal.

Abstract: Enhancing public transport accessibility (PTA) is an effective strategy for creating sustainable and liveable urban environments. However, calculating the access index from predefined values for walking and waiting times requires further investigation to assess whether available public transport services sufficiently meet traveller needs. This study evaluates PTA to examine additional walking times from home, work, and other locations to various transport modes (bus, train, underground, and tram), and waiting times during different peak periods (morning, inter, evening, and night) as a case study in London. The findings reveal that walking times from home locations exceed PTAL thresholds, with median values surpassing 8 minutes at bus stops and 12 minutes at rail stations, while evening peaks result in higher waiting times than morning periods used in PTAL calculations. The data-driven access index from mobile app data, with reference data from TFL, is further examined for spatial patterns of over- and underestimation areas and to demonstrate how incorporating dynamic spatial and temporal attributes into PTAL offers valuable insights for improving public transport accessibility in urban environments.

Abstract: Shopping centers are significant traffic generators that influence traffic conditions on adjacent road networks. This study evaluates the impact of the Big Fashion shopping center in Novi Sad, Serbia, on two nearby signalized intersections and examines the effectiveness of reconstructing one of them into a turbo roundabout. Traffic counts conducted before (2015) and after (2020) the opening of the shopping center indicate notable increases in peak-hour traffic volumes: 8.38% at the Bulevar cara Lazara–Bulevar oslobođenja intersection and 6.96% at the Bulevar cara Lazara–Fruškogorska Street intersection. The increased demand contributed to higher delays and deteriorated levels of service. In 2023, the latter intersection was reconstructed into the region’s first turbo roundabout. Microsimulation (Synchro 11) using pre-reconstruction volumes revealed substantial operational improvements, including a reduction in the most critical left-turn delay from 165.5 s/veh to 11.9 s/veh. The results confirm that turbo roundabouts can effectively mitigate congestion and enhance intersection performance in urban environments with high directional turning movements.

Abstract: Bike-sharing services contribute to reducing emissions and conserving natural resources within urban transportation systems. They also promote public health by encouraging physical activity and generate economic benefits through shorter travel times, lower transportation costs, and decreased demand for parking infrastructure. This paper examines the use of shared micro-mobility services in the Italian cities of Firenze and Bologna, based on an analysis of GPS origin–destination data and associated temporal coordinates provided by the RideMovi company. Given the still limited number of studies on free-floating and electric bike-sharing systems, the objective of this work is to quantify the performance of electric bikes and E-scooters in bike-sharing schemes and compare it to traditional, muscular bikes. Results show that E-bikes are from 22 to 26\% faster on average with respect to muscular bikes, extending trip range in Bologna, but not in Firenze. Electric modes attract more users than traditional bikes, E-bikes have from 40 to 128\% higher daily turnover in Bologna and Firenze, and E-scooters from 33 to 62\% higher in Firenze with respect to traditional bikes. Overall, turnover is fairly low, with less than 2 trips per vehicle per day. The performance is measured in terms of trip duration, speed, and distance. Further characteristics such as daily turnover by transport mode, trip purpose, and user type are investigated and compared. The results aim to support planners and operators in designing and managing more efficient and user-oriented services.

Abstract: As the population arises everyday, road accidents also increase. More than half of all road traffic deaths and injuries involve vulnerable road users, such as pedestrians, cyclists and motorcyclists and their passengers. One of the many reasons is the lack of road lighting. Without sufficient road lighting, it is necessary to provide some means to guide the drivers along dark roads. Road studs are among the most important devices that help in preventing cars from running off the road or their lanes and making our roads safer. They reflect the light from a car's headlights to allow the driver to observe the curves and corners of the road from a distance. Even in the dark, the driver is easily able to see the road alignments, ends, and corners of the road and judge where to turn, what lane of the road to adopt and in turn, drive safely. This makes studs extremely useful on poorly lit roads. They provide effective night guidance even under adverse weather conditions. The aim of this study is to provide better understanding and to get an overview of the developments of road studs through time. The data collection has been carried out through the search engines Google Scholar and Google Search. Therefore limitations, selections and exclusions have been made by the authors. This study focuses only on the variables stated above, the effectiveness of road studs to road safety and visibility . The method for answering the research questions was conducted by systematic literature search and review.

Abstract: In this study, a traffic flow analysis was conducted using a multi-agent simulation to evaluate the effect of reducing CO2 emissions through the penetration of connected autonomous vehicles (CAVs) equipped with vehicle-to-vehicle (V2V) communication functionality. By exchanging local information among CAVs, alleviating traffic congestion without the need for cooperative vehicle control, and reducing CO2 emissions by up to 20% is possible. In addition, we analysed the impact of CAV penetration rate and communication range on CO2 emissions, demonstrating that the reduction effect in CO2 emissions tends to appear more prominently once the penetration of CAVs reaches a certain threshold. In particular, when the communication range is narrow, a significantly high penetration rate is required before the benefits of CO2 reduction become evident. Furthermore, a wider communication range is not necessarily more desirable. These findings suggest that limiting the communication range may enable more efficient use of road traffic information. Although each CAV acts solely based on its own self-interest, route selection based on local information leads to the emergence of swarm intelligence, resulting in improved efficiency at the collective level.

Abstract: Advanced Air Mobility (AAM) has emerged as a transformative solution for time-critical healthcare logistics. It promises to overcome ground transport limitations in emergency response, organ transport, and medical supply distribution. Despite rapid technological progress in electric vertical takeoff and landing (eVTOL) systems, automation, and airspace management, scholarly work on AAM in healthcare remains fragmented across disciplines. Therefore, a systematic synthesis is needed to consolidate knowledge, evaluate methodological maturity, and guide future research and policy directions. This review examines how AAM has been conceptualized, modeled, and applied within healthcare contexts. It addresses three questions: (1) What bibliometric patterns characterize research on healthcare-focused AAM? (2) What technical, regulatory, and societal barriers constrain its integration? (3) How do current approaches optimize time-critical missions? The study conducts a systematic review of 121 peer-reviewed publications (2015–2025) using five major databases. The analysis combines bibliometric mapping, thematic synthesis, and semantic network visualization to identify conceptual patterns, research clusters, and interdisciplinary linkages. Six dominant themes emerge: design, logistics, airspace, acceptance, regulation, and economics. These themes reflect the multidimensional evolution of AAM research. Findings show rapid growth since 2020, driven by advances in automation and electrification. However, the study also reveals persistent fragmentation between engineering-driven feasibility studies and policy or healthcare-oriented research. The field is transitioning from technical prototyping toward integrated frameworks that address safety, governance, and healthcare system alignment. This review contributes a unified socio-technical perspective on AAM for healthcare. It offers conceptual clarity and identifies priority directions for empirical validation, equity-focused deployment, and regulatory harmonization. The study provides actionable insights for researchers, practitioners, and policymakers seeking to translate aerial mobility innovations into resilient, equitable healthcare delivery systems.

Abstract: This paper addresses the distributed control problem for high-speed trains subject to unknown actuator faults, input saturation, and parametric uncertainties—including structural variations among actuators, external resistance, and inter-carriage forces. A carriage-scale distributed adaptive fault-tolerant controller is designed based on a multi-agent dynamics model. The controller incorporates an adaptive law to estimate uncertain parameters and a second-order auxiliary system to mitigate the effects of input saturation on closed-loop stability. Simulation results demonstrate the controller’s effectiveness in achieving accurate dual-closed-loop tracking of both speed and position under actuator fault and input saturation conditions.

Abstract: The present article relates to the description of phenomenological relations of amorphous material behavior within the framework of viscoelasticity and elastic-viscoplasticity theory, as well as to the creation of its digital analogue. Ultra-high-molecular-weight polyethylene (UHMWPE) is considered in the study. The model is based on the results of a series of experimental studies. Free compression of cylindrical specimens in a wide range of temperatures [-40; +80] °C and strain rates [0.1; 4] mm/min was performed. Cylindrical specimens were also used to determine the thermal expansion coefficient of the material. Dynamic mechanical analysis (DMA) was performed on rectangular specimens using a three-point bending configuration. Maxwell and Anand models were used to describe the material behavior. In the framework of the study, a temperature dependence of a number of parameters was established. This influenced the mathematical formulation of the Anand model, which was adapted by introducing the temperature dependence of the activation energy, the initial deformation resistance, and the strain rate sensitivity coefficient. Testing of the material models was carried out in the process of analyzing the deformation of a spherical bridge bearing by a multi-cycle periodic load. The load corresponds to the movement of a train on a bridge structure, without taking into account vibrations. It is shown that the viscoelastic model does not describe the behavior of the material accurately enough for quantitative analysis of the stress-strain state of the structure. It is necessary to move on to more complex models of material behavior to minimize the discrepancy between the digital analogue and the real structure. It is established that taking into account plastic deformation while describing UHMWPE allows this to be done.

Abstract: To address the issues of inefficiency and high costs in obtaining data on the residential distribution of public transport passengers at present, this paper proposes an approach of "estimating the residential distribution of public transport passengers based on characteristics such as housing prices of residential Point of Interest (POI) and the convenience of public transport and its stops". First, from two aspects—public transport travel and the selection of public transport stops—eight influencing factors for the selection of public transport stops during travel are identified. Based on these factors, a regression model for the number of public transport passengers from residential POI to their corresponding stops is constructed, through which the number of passengers traveling from each residential POI to all accessible public transport stops is obtained. This number is then used as a weight to allocate the actual passenger flow of each public transport stop to respective residential POI, thereby realizing the estimation of the residential distribution of public transport passengers. Furthermore, this approach enables the estimation of the proportion of trips made from residential areas to specific public transport stops and the overall proportion of public transport trips among all travel modes from residential areas. The proposed estimation method is verified and evaluated using Shenzhen as a case study. The results show that the Mean Absolute Percentage Error (MAPE) of the proposed model is 72.024%, which outperforms the XGBoost model that uses the same set of characteristics.

Abstract: Aiming at the problems of identification and utilization efficiency evaluation of urban high-intensity development areas, and based on the general trend of urban spatial development from points to areas, this study proposes a method for identifying high-intensity development areas based on seed grid, which involves area growth, merging and segmentation, by drawing on the region growing method in image recognition. In this method, the Getis-Ord Gi* statistic of grid floor area ratio is used as the criterion to screen seed grids, and the region identification results are evaluated from the rationality of the geometric shape of the area and the independence of spatial relations. Furthermore, using gridded permanent population density, digital brightness of urban night-time lights, and point of interest (POI) density data, the utilization efficiency of high-intensity development areas is evaluated from the perspectives of population carrying capacity and industrial agglomeration. Finally, Shenzhen City is taken as an example to verify the proposed identification and utilization efficiency evaluation methods for high-intensity development areas. The results show that the proposed identification method has a good effect, and the identified high-intensity development areas have reasonable geometric shapes and independent spatial relations; in terms of utilization efficiency, the overall utilization efficiency of high-intensity development areas in Shenzhen is relatively low, especially the areas at the edge of the central district and non-central districts fail to attract population activities matching the intensity of construction.

Abstract: This comprehensive review examines the works of reinforcement learning (RL) in ship collision avoidance (SCA) from 2014 to the present, analyzing the methods designed for both single-agent and multi-agent collaborative paradigms. While prior research has demonstrated RL's advantages in environmental adaptability, autonomous decision-making, and online optimization over traditional control methods, this study systematically addresses the algorithmic improvements, implementation challenges, and functional roles of RL techniques in SCA, such as Deep Q-Network (DQN), Proximal Policy Optimization (PPO), and Multi-Agent Reinforcement Learning (MARL). It also highlights how these technologies address critical challenges in SCA, including dynamic obstacle avoidance, compliance with Convention on the International Regulations for Preventing Collisions at Sea (COLREGs), and coordination in dense traffic scenarios, while underscoring persistent limitations such as idealized assumptions, scalability issues, and robustness in uncertain environments. Contributions include a structured analysis of recent technological evolution, and a Large Language Model (LLM) based hierarchical architecture integrating perception, communication, decision-making, and execution layers for future SCA systems, which prioritizes the development of scalable, adaptive frameworks that ensure robust and compliant autonomous navigation in complex, real-world maritime environments.

Abstract: In the Internet of Vehicles (IoV), vehicles need to process a large amount of perception data to support tasks such as road navigation and autonomous driving. However, their computational resources are limited. Therefore, it is necessary to explore the combination of vehicle-road cooperation with edge computing. Roadside units (RSUs) can provide data access services for vehicles, and deploying edge servers on RSUs can improve the data processing capability in IoV environments and ensure the sustainability of vehicle communications, thus supporting complex traffic scenarios more effectively. In this work, we study the deployment of RSUs in vehicle-road cooperative systems. To balance the deployment cost of RSUs and the quality of service (QoS) of vehicle users, we propose an RSU deployment optimization model with six objectives, including time delay, energy consumption and security when vehicles offload their tasks to RSUs, as well as load balancing and the number and communication coverage area of RSUs. In addition, we propose a Wasserstein generative adversarial network (WGAN)-based Two_Arch2 (WGTwo_Arch2) to solve this many-objective optimization problem to better ensure the diversity and convergence of the solutions. In addition, a polynomial variation strategy based on Lecy's flight mechanism and a diversity archive selection strategy with an adaptive Lp-norm are also proposed to balance the exploratory and exploitative capabilities of the algorithm. The effectiveness of the proposed algorithm WGTwo_Arch2 for 6-objective RSU deployment optimization is verified by comparisons with five different algorithms.

Abstract: Bidirectional electric vehicle (EV) charging represents an opportunity to leverage EVs as flexible energy assets within the power system. By enabling controlled power flow in both directions, bidirectional charging unlocks a wide range of grid services, thereby enhancing grid stability as the energy sector decarbonizes. This paper presents a comprehensive experimental evaluation of bidirectional charging systems (EVCS), focusing on response dynamics and controllability delays critical for grid services. Tests were conducted with a real ISO 15118-20-enabled EV and an EV emulator across configurations, using the Watt & Well 20 kW bidirectional charging bay. The study compares CCS2 and CHAdeMO protocols under varying configuration conditions. Results show that modern chargers achieve sub-second responsiveness, with local communication delays typically below 0.4 seconds and ramping times around 0.5 s. However, power flow reversals introduce an additional delay of approximately 1 s. These updated controllability metrics are essential for validating bidirectional charging in time-critical applications such as primary frequency regulation. The findings highlight the influence of voltage level and modular configuration on dynamic performance, underscoring the need to integrate external control path delays for full-stack validation. This work provides a foundation for modeling and deploying bidirectional EVCS in fast-response grid services.

Abstract: The rapid increase in vehicle numbers and the dynamic nature of traffic patterns have made road traffic management a significant global challenge. Conventional traffic signal systems are unable to adapt to real-time conditions, resulting in unnecessary delays, increased congestion, and higher fuel consumption. In the present study, we review recent advancements in smart traffic signal control systems. Our study highlights the progression from supervised machine learning approaches (ATLCS, priority-based adaptive controls, genetic algorithm-driven optimization models), to deep learning methods (2D-CNN-based vehicle classification), and to reinforcement learning techniques (IntelliLight, FRAP, PressLight, AttendLight, robust DQN-based frameworks, MACOPO, MAGSAC) designed to learn traffic patterns, adapt to dynamic environments, and make data-driven decisions in real time—capabilities not feasible with conventional methods. We divide the scope into machine learning–based, reinforcement learning–based, and hybrid approaches. Our analysis was structured to compare how each model improves traffic efficiency using key performance attributes, including travel time, queue length, waiting time, fuel consumption, and intersection throughput. These models have proven effective at cutting down delays, reducing the length of queues, and enhancing overall traffic flow by adjusting to dynamic traffic conditions. For instance, IntelliLight achieved 72% reduction in travel time compared to fixed-time traffic control, along with significant decreases in average delay. These approaches perform well in different settings, from small intersections to large-scale networks, because they are effective, flexible, and scalable. Overall, our studies highlight that recent advancements in Artificial Intelligence (AI) are crucial for traffic management, with great potential to make urban transportation faster, safer, and more environmentally friendly. We also identify the limitations of these models and suggest future research directions.

Abstract: Quantum computing offers transformative potential to solve complex optimisation problems in transportation and logistics, particularly those that involve large combinatorial decision spaces such as vehicle routing, traffic control, and supply chain design. Despite theoretical promise and growing empirical interest, its adoption remains limited. This systematic literature review synthesises fifteen peer-reviewed studies published between 2015 and 2025, examining the application of quantum and quantum-inspired methods to transport optimisation. The review identifies five key problem domains (vehicle routing, factory scheduling, network design, traffic operations, and energy management) and categorises the quantum techniques used, including quantum annealing, variational circuits, and digital annealers. Although several studies demonstrate performance gains over classical heuristics, most rely on synthetic datasets, lack statistical robustness, and omit critical operational metrics such as energy consumption and queue latency. Four cross-cutting barriers are identified: hardware limitations, data availability, energy inefficiency, and organisational readiness. The review highlights the absence, yet development promise, of real-world deployments, standardised benchmarks, and comprehensive cost–benefit analyses. It concludes with a structured research agenda aimed at bridging the gap between laboratory demonstrations and practical implementation, emphasising the need for pilot trials, open datasets, robust experimental protocols, and interdisciplinary collaboration.

Abstract:

This work addresses the features of railway transition curves’ curvature, especially at extreme points. In particular, should it be smooth at the extreme points or not, or something else? Such a question is not accidental. This is based on the main results that the present authors obtained while optimizing the shape of polynomial railway transition curves. It appeared for the same quality function, that depending on the transition curve degree, the optimum shapes represented curvatures either possessing bends or close to smooth at the extreme points. Such a discrepancy raises the question of its reason, i.e., the factor influencing most the appearance of bends at the extreme points of the curvatures. The continuity of G₀ and G₁ at such points was considered. At the same time, the hypothesis was formulated that a long time while negotiating the curve is the factor mostly influencing the existence of the mentioned bends in the extremities of curvature. Two cases were considered to ensure the long time of vehicle passage through the curve, with a great curve length and a small vehicle velocity, respectively. To verify the hypothesis, the optimizations of the shape of the transition curves of 5^th, 9^th, and 11^th degrees and the simulations of the railway vehicle behaviour were performed. The hypothesis turned out to be true, however, easier in application at High-Speed Rail conditions. It was shown that the transition curves shapes got in assumed circumstances did not have the bends at the extreme points. The tendency to smooth the curvature can be univocally noticed. It resulted in calmer vehicle movement, expressed by vehicle body lateral dynamical characteristics. Corresponding results of the simulations and transition curve optimizations were presented and compared.

Abstract:

In rural arterial networks, poor sensor coverage, high vehicle speeds, and intricate traffic dynamics make Traffic State Estimation (TSE) an essential task. The intricacies of rural surroundings are not adequately captured by traditional TSE approaches, which rely on single-source data like loop detectors and GPS. This results in safety hazards like over speeding, queue spillback, and short headways. This study presents a novel strategy to overcome these issues by fusing sophisticated deep learning models with data from several sources. By combining a Graph Attention Temporal Convolutional Network (GAT-TCN) with traditional Kalman Filter (KF) variations (Extended, Unscented, and Sliding Window), we suggest a hybrid architecture. With its ability to capture both multi-resolution temporal dynamics and dynamic spatial dependencies, the GAT-TCN model performs noticeably better than conventional techniques in terms of Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). By combining loop detector data and Bluetooth trip durations, empirical validation on a real-world rural toll route shows that the GAT-TCN improves safety by enabling early detection of important occurrences like over speeding and queue spillback and produces more accurate traffic projections. The findings demonstrate how combining multi-source data with state-of-the-art machine learning algorithms can enhance rural areas’ transportation efficiency and safety. The findings demonstrate how combining multi-source data with state-of-the-art machine learning algorithms can enhance rural areas’ transportation efficiency and safety. This study offers a scalable framework for proactive rural traffic management, marking a departure from conventional traffic status estimation in favor of safety-actionable insights.

Abstract: Implementing effective cycling mobility requires infrastructure that enhances safety and reduces travel time. A common metric for tracking progress is the total length of dedicated cycling infrastructure. However, this does not always correlate with increased cycling usage. For instance, in Italy (2008–2015), cycling infrastructure grew by 48%, but ridership remained unchanged. Design quality, behavioral, and contextual factors all influence this dynamic. This study analyzes a 16-year time series (2009–2024) of monthly cyclist flows surveys in Bologna, Italy. It focuses on flows, gender, and bike lane usage. It represents the most detailed and longest series of its kind in the country. Findings show a positive correlation between infrastructure growth (meters per inhabitant) and cyclist flows, though this weakened significantly after COVID-19 and the extensive introduction of non-exclusive bike lanes on mixed use roads from 2020. Regression analysis reveals that new bike flows per new meter/inhabitant of infrastructure were 3 times greater before 2020. The study identifies two likely causes: the insufficient perceived safety of the newly introduced mixed traffic lanes from 2020 and the lack of attractiveness of bike mode for the female population, as highlighted in the decreasing trend in usage of bike infrastructure by female riders after 2020.