Abstract: Multimodal transport refers to the integrated transportation in a logistics system in the form of multiple transportation modes, such as highway, railway, waterway, etc., and the interconnection through containers and other tools, and path optimization is the key link to achieve cost reduction and efficiency increase. Based on whether to consider low-carbon, uncertainty, and special cargo transportation, the literature is divided into five areas: traditional multimodal transport path optimization, multimodal transport path optimization considering low-carbon, multimodal transport path optimization considering uncertainty, multimodal transport path optimization considering low-carbon and uncertainty, and multimodal transport path optimization considering special transport needs. The literature on multimodal transport path optimization since 2016 is summarized to sort out the current research status. Finally, with the development of multimodal transport to collaborative transport and the improvement of the application of in-depth learning in different fields, the research mainly focuses on two future research directions: collaborative transport and the use of in-depth learning to solve uncertain problems and combines it with the problem of multimodal transport route optimization to explore more efficient and perfect transport solutions.

Abstract: Vehicle telematics aims to enhance fuel efficiency, reduce emissions, improve diagnostics, promote road safety, and optimize fleet management. Vehicle telematics solutions can be implemented using either smartphone or cyber-physical based systems, offering various applications within the realm of Intelligent Transportation Systems (ITS). This study systematically reviews the existing literature on the applications of smartphone and cyber-physical-based vehicle telematics within ITS. A comprehensive search was conducted in the Web of Science (WoS) and Scopus databases, with the search completed in October 2024. Studies focused on the vehicle telematics applications in ITS were included. Out of 397 articles related to smartphone-based vehicle telematics, 54 were selected for an in-depth review. Similarly, 37 articles were shortlisted from 210 identified studies on cyber-physical-based vehicle telematics. The review reveals that vehicle telematics is utilized in various applications, including eco-driving, eco-routing, driver behavior monitoring, vehicle health diagnostics, road pavement condition monitoring, and fleet management. This systematic review highlights the current state of vehicle telematics in ITS, analyzing and comparing different solutions developed using smartphones and cyber-physical systems. It also identifies existing challenges, reports on scientific trends, and suggests future research directions for expanding the application of vehicle telematics.

Abstract: Magnetic levitation (maglev) technology offers significant advantages for rail transport, including frictionless propulsion, reduced noise, and lower maintenance costs. However, its widespread adoption has been limited due to the need for dedicated infrastructure incompatible with conventional rail networks. The MaDe4Rail project, funded by Europe’s Rail Joint Undertaking, explores Maglev-Derived Systems (MDS) as means to integrate maglev-inspired solutions into existing railway corridors with minimal modifications. This paper is focused in the so-called “hybrid MDS” configuration that refers to levitating systems compatible with the existing railway infrastructure. The evaluated scenario could benefit from the introduction of hybrid MDS based on magnetic levitation, where a group of pods is used in a virtual coupling configuration. In this way, this case study aims to achieve an increase in the capacity of the traffic line by significantly reducing the travel time while maintaining a similar energy consumption to that of the current conventional trains operating on this line. Simulation results indicate that hybrid MDS can optimize railway operations by leveraging virtual coupling to enhance traffic flow and reduce aerodynamic drag. The system achieves a balance between increased speed and energy efficiency, making it a viable alternative for future rail transport. An initial cost-benefit analysis suggests that hybrid MDS could deliver substantial economic advantages, positioning it as a promising solution for enhancing European railway networks with minimal infrastructure investment.

Abstract: Thanks to Light Detection and Ranging (LiDAR), autonomous vehicles are able to detect different objects in their environment and measure the distance between them. This device gives an un-manned ground vehicle the ability to see its surroundings in real time. However, the accuracy of LiDAR can be reduced, especially in rainy weather, fog, urban smog and the like. These factors can have disastrous consequences as it increases errors in the vehicle's control computer. The aim of this research was to determine the most appropriate LiDAR frequency for autonomous vehicles, depending on the distance to them and scanning frequency in various weather conditions, therefore it is based on empiric data obtained by using the RoboPeak A1M8 LiDAR. The results obtained in rainy conditions are compared with the same ones in clear weather, using stochastic methods. A direct influence of both the frequencies used and the rain on the accuracy of the LiDAR measurements was found. Range measurement errors increase in rainy weather; as the scanning frequency increases, the results become more accurate but capture a smaller number of object points. The higher frequencies lead to about five times less error at the farthest distances compared to the lower frequencies.

Abstract: Hydrogen fuel cell vehicles are one of the most promising alternatives to achieve transport decarbonization targets thanks to their moderately high efficiency and low refueling time combined with zero exhaust emissions operation. In order to reach reasonable power density figures, fuel cell systems are generally supercharged by radial compressors, which can encounter significant limitations associated to surge and choke operation especially in high altitude. Alternatively, the current paper explores the altitude operation of a fuel cell system combined with a roots compressor. First, the balance of plant model is build in Simscape platform combining a physical and chemical 1D fuel cell model for the stack, calibrated against literature data at different pressure and temperature values, and the characteristic maps of the roots compressor. Then, the model is used to explore the balance of plant operation in a working range between 10 and 200 kW and an altitude range between sea level and 5 km. The results show that the compressor is capable to operate around the highest efficiency area (between 60 and 70%) for a wide range of altitude and power conditions, limiting the negative impact of the altitude in the system efficiency up to 3%. However, once the compressor efficiency falls below 60% the balance of plant performance rapidly drops, overcoming the benefits of the working pressure on the fuel cell stack operation and limiting the peak net power produced.

Abstract: The cross-border port serves as a crucial cross-border travel connecting mainland China with Hong Kong and Macau, directly impacting the overall satisfaction of cross-border travel. While previous studies on neighborhoods, communities, and other areas have thoroughly examined the heterogeneity and asymmetry in satisfaction, research on the satisfaction of cross-border travel at ports remains notably limited. This paper explores the heterogeneity and asymmetry of cross-border travel satisfaction using gradient boosted decision trees (GBDT) and k-means cluster analysis under the framework of three-factor theory, aiming to demonstrate the latest scientific research results on the fundamental theories and applications of artificial intelligence. The results show that prevalent asymmetric relationships between factors and cross-border travel satisfaction, with the factor structure exhibiting heterogeneity across different groups. High-income individuals were more likely to prioritize the reliability of cross-border travel, whereas low-income individuals tended to emphasize the convenience of travel. Finally, this paper proposes improvement priorities for different types of passengers, reflecting the practical application of advanced mathematical methods in artificial intelligence to drive intelligent decision-making.

Abstract: Abstract Rail fasteners secure rail tracks to sleepers to prevent displacement, which makes them critical for railway safety. Regular track inspection is essential as long-term usage leads to fastener defects and track irregularities that require accurate measurement of geometric parameters of fasteners for proper maintenance. This paper proposes a method for fastener defect detection and geometric parameter measurement based on 3D linear laser sensor. Firstly, a 3D imaging system is constructed based on a 3D linear laser sensor to generate RGB-P bimodal data, which includes an RGB depth image and its corresponding point cloud. Secondly, the visual defect is detected and fastener area is located from the RGB depth image of railway track. By mapping the fastener area of RGB depth image to point cloud, the fasteners point clouds are rapidly segmented from railway track point cloud. Lastly, the PointNet++ network segments the fastener point cloud into individual components. Based on the spatial structure of fastener components, the specifications of insulated blocks, thickness of height adjustment pads, and bolt heights are measured. Experimental validation shows 100% precision and recall in visual defect detection. For fastener components with minimum specification differences of 1 mm, the measurement error remains below 0.5 mm. The system achieves a real-time detection and measurement speed of 4.32 km/h, effectively replacing manual inspection for high-speed railway fasteners.

Abstract: The European Commission has taken a further step towards reducing the number of road fatalities with the road safety policy framework and the long-term goal of Vision Zero by 2050. However, the number of fatalities is not developing as expected and is lagging behind the target. A key pillar of the framework is safe vehicles and their ability to avoid collisions. Although the number of autonomous safety systems in vehicles is increasing, retrofitted systems could also help reduce road accidents. A new retrofit assistance system called Front Brake Light (FBL) helps the driver to assess the intentions of other road users. This system is mounted at the front of the vehicle and worksing similarly to the rear brake lights. The objective of the study is to evaluate the safety performance of an FBL in real accidents at junctions. Depending on the type of accident, between 7.5% and 17.0% of the accidents analysed can be prevented. A further 9.0% to 25.5% could be positively influenced by the FBL, i.e. the collision speed could be reduced. If the FBL were visible to the driver of the priority vehicle, the number of potentially avoidable accidents would increase to a magnitude of 11.5% to 26.2%. The range of accidents in which the consequences can be reduced increases to between 13.8% andto 39.2%.

Abstract: This study proposes the development of a methodology to define a tool that integrates real-time data and predictive modeling to identify passenger flow and occupancy levels within a multimodal transport hub. This tool enables the implementation of control and planning strategies to ensure a high Level of Service (LOS). The tool is based on a Long Short-Term Memory (LSTM) model and heterogeneous data sources, including an Automatic Passenger Counting (APC) system, are utilized to estimate real-time passenger flow and area occupancy. Moncloa Interchange in Madrid is the case study, and the results reveal that transport-dedicated zones have higher occupancy levels. Methodologically, time series data were standardized to a uniform frequency to ensure consistency, and the model was trained on seven months of available data. The model performs better in high- whereas in low-occupancy zone. Despite maintaining a LOS A, some periods experience temporary congestion. These findings indicate that variations in occupancy levels influence service quality and highlight the essential role of dynamic interchange management. By anticipating congestion through predictive modeling, tailored operational strategies can optimize service levels and improve user experience. This can enhance the attractiveness of public transport, minimizing perceived transfer penalties, making transfers more efficient, and reinforcing the role of transport hubs in sustainable urban mobility.

Abstract: Airport sustainability has gained increasing attention as the aviation industry faces the challenge of balancing economic growth, environmental responsibility and social standards. This study conducts a systematic literature review (SLR) using the OpenAlex database. The PRISMA 2020 (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology was applied to refine the selection process, resulting in 66 relevant studies. Then, a bibliometric-systematic literature review (B-SLR) approach was employed to analyze trends and identify research gaps. Findings indicate that most studies often focus on two sustainability pillars at a time, while neglecting a fully integrated perspective. Not many research works simultaneously address all three dimensions of sustainability (economic, environmental and social), leading to fragmented insights into sustainable airport management. Notably, some industry-driven reports are starting to suggest emerging holistic approaches, but the majority of academic literature remains segmented. Hence, this study highlights the need for a more comprehensive research framework that considers environmental, economic, and social factors concurrently. Future research should integrate these dimensions to develop practical and well-balanced sustainability strategies. While methodological limitations may exist in this work, such as language constraints and dataset selection criteria, this review provides valuable insights into airport sustainability and lays the groundwork for further studies.

Abstract: Urban development within an urban agglomeration is uneven, and the coordinated de-velopment of urban agglomerations is the core task of urban development. Highway transporta-tion volume is related to urban economic development. By regulating the highway transportation volume of each city in the urban agglomeration, highway collaborative transportation is achieved, thereby achieving coordinated development of the urban agglomeration. According to the characteristics of transportation volume regulation, a two-layer complex network regulation model is proposed. The upper nodes are urban entities, and the lower nodes are highway intersections or toll points. Based on the coordinated development of urban agglomerations, a regulation objective function is set. Due to the many variables and constraints, a hierarchical solution method is adopted. A probability search iteration algorithm is proposed innovatively to solve multivariable, many-to-many allocation problems. Taking provincial urban agglomerations as an example, the process of solving the regulation model and realizing the method is explained. The transportation volume regulation methods and strategies proposed in this study realize the best combination of macro-control and micro-control, static and dynamic control, coordinated development, and collaborative transportation. It is an innovative exploration and research on highway transportation volume allocation and collaborative transportation in urban agglomerations and opens up a new direction for research on the coordinated development of urban agglomerations.

Abstract: We analyzed the dynamic equilibrium process between demand and supply in the international airline market by utilizing Granger causality and Bayesian Networks (BN) based on South Korea’s aviation performance data. To examine whether the interrelationship between demand and supply varies depending on the classification of external factors, we tested for changes in causality based on reasonable segmentation of sub-market, time window, and time lag. Based on the results of the Granger causality analysis, we constructed a BN model to determine whether economic factors influence changes in the causal relationship between demand and supply, as well as to track the dynamic equilibrium path of demand and supply. The international airline market was classified into national and foreign carriers, as well as full-service carriers (FSCs) and low-cost carriers (LCCs). Time windows were set on a monthly, quarterly, and annual basis, while time lags were set with the minimum duration based on the unit of time window and the maximum duration based on data availability. Supply variables included the number of operations, available seat capacity, and load factor, whereas demand was represented by the number of revenue passengers. Our findings support the hypothesis that airline supply and demand factors in South Korea’s international airline market exhibit mutual causality. Moreover, the causality from demand to supply was found to be somewhat clearer than the reverse case. As the time window shortened, the interrelationship became more evident, and the influence of demand on supply exhibited a shorter time lag while maintaining a longer duration compared to the opposite direction. In terms of market segmentation, the relationship between supply and demand was more distinct in the LCC market compared to the FSC market and in the national carrier market compared to the foreign carrier market. The BN model incorporating economic factors confirmed that the causal relationship between airline supply and demand could appear independently of economic influences when analyzing total monthly demand. Ultimately, our study confirms the existence of a mutual causal relationship between airline supply and demand in South Korea’s international airline market. From an academic perspective, we provide insights into the dynamic equilibrium characteristics and pathways of supply and demand in the airline industry.

Abstract: Distraction is a major contributor to road accidents, especially among children who are easily distracted and may not be fully aware of the traffic situation. It is crucial to understand that children up to a certain age may struggle to halt their movement once initiated. This study conclusively demonstrates that the stopping distance, time, and deceleration of children aged six to ten years after a specific stop signal at different speeds are strongly influenced by the speed of movement and the age of the children. The results show that in the “walking” test configuration, the children were able to stop within a range of 0.47 m to 0.63 m, with a shorter distance for older children. The stopping time ranges from 0.84 s to 1.21 s and correlates positively with age. The stopping time and distance of children were measured in both “running” and “walking” test configurations across different age groups. However, in the “running” test configuration, stopping distance is almost the same across all age groups, with children requiring between 1.72 m and 1.84 m and a stopping time ranging from 1.17 s to 1.28 s. In the “walking” test configuration, children are able to decelerate between 0.91 m/s² and 1.57 m/s², while in the “running” test configuration, they are able to decelerate between 2.24 m/s² and 3.19 m/s².

Abstract: The water environment is a dynamic domain critical to global transportation and commerce, where seaplanes operate during take-offs, landings and ground operations, often near maritime traffic. Canada’s vast remote regions and unique geography increase reliance on seaplanes, espe-cially for private and recreational purposes. This article examines the intersection of aviation and maritime operations through a mixed-methods approach, analyzing seaplane safety on water-ways using quantitative and qualitative methods. First, data from 1,005 General Aviation (GA) seaplane accidents in Canada (1990–2022) is analyzed, revealing 179 fatalities, 401 injuries and 118 destroyed aircraft - significant given seaplanes comprise under 5% of GA aircraft. Of these, 50.35% occurred while the seaplane was not airborne. Second, insights from interviews, focus groups, and questionnaires involving 136 participants are explored through thematic and content analysis. These capture pilot concerns not evident in accident data, such as hazards from jet ski interactions and disruptive boat wakes. The findings highlight risks like limited visibility and maneuverability during waterborne take-offs, worsened by seaplanes’ lack of priority over mari-time vessels in shared spaces. This article concludes with recommendations for both the seaplane and maritime communities, including increasing awareness among boaters about the presence and operations of seaplanes and regulatory adjustments particularly considering the right of way.

Abstract: This research aims to identify patterns and root causes of aircraft downtimes by comparing various forecasting models used in the aviation industry to prevent AOG events effectively. At its heart, this study explores innovative forecasting models using time series analysis, time series modeling and binary classification to predict spare part usage, reduce downtime, and tackle the complexities of managing inventory for diverse aircraft fleets. By analyzing both data and insights shared by aviation industry experts, the research offers a practical roadmap for enhancing supply chain efficiency and reducing Mean Time Between Failures (MTBF). The thesis emphasizes how real-time data integration and hybrid forecasting approaches can transform operations, helping airlines keep spare parts available when and where they’re needed most. It also shows how precise forecasting isn’t just about saving costs it’s about boosting customer satisfaction and staying competitive in an ever-demanding industry. In addition to data-driven insights, this research provides actionable recommendations, such as embracing predictive maintenance strategies and streamlining logistics. These steps aim to ensure smoother operations, fewer disruptions, and more reliable service for passengers and operators alike.

Abstract: Rural public transport networks face significant challenges, often characterized by suboptimal service quality. With advancements in technology, various applications have been explored to address these issues. Autonomous Demand Responsive Transits (ADRTs) represent a promising solution that has been investigated over recent years. Their potential to enhance the overall quality of transport systems and promote sustainable transportation is well-recognized. In our research study, we evaluated the viability of ADRTs for rural networks. Our methodology focused on two primary areas: the suitability of ADRTs (considering vehicle type, service offerings, trip purposes, demographic groups, and land use) and the broader impacts of ADRTs (including passenger performance, social impacts, and environmental impacts). We examined demographic heterogeneity and assessed the influence of demographic factors (age, gender, education, occupation, household income level and disability status) on the implementation of ADRTs in rural settings. The study further delineates the varied perceptions across these socio-demographic strata, underscoring the necessity for demographic-specific trials. Consequently, we advocate for the implementation of ADRT services tailored to accommodate the diverse needs of these demographic cohorts.

Abstract: The application highlights the potential of integrating GIS and BIM systems, particularly for safety and operational management. However, inherent structural differences between the two systems often require model simplifications, especially in complex projects. The study also reveals that GIS-BIM interoperability is not always seamless, emphasizing the need for further research to enhance integration processes and improve system compatibility in future applications.

Abstract:

As a critical infrastructure, the transportation network impacts health, safety, comfort, and the economy, making it highly vulnerable to disruptions that significantly affect social and economic well-being. To maintain optimal service during such disruptions, the critical components that are vulnerable to disruptions must be identified and their impact on network performance must be understood. This study proposes a method for identifying network vulnerabilities in worst-case scenarios by targeting critical components derived from network topological parameters. These parameters serve as proxies for performance and are utilised to generate critical-link attacks to assess the network vulnerability. In addition, this study proposes a straightforward and simplistic modelling framework using topological parameters to assess the impact of critical link attacks on traffic flow changes. To characterise network performance and traffic volume changes under critical-link attacks, this study utilises the complementary cumulative distribution function (CCDF), which has rarely been utilised in previous studies. The proposed method was applied to a real network in the Colombo Municipal Council (CMC) area in Sri Lanka. The findings of this study will help us understand the impact of critical-link attacks on transportation network performance and traffic flow and develop proactive policies to address vulnerabilities and improve overall network performance.

Abstract: We propose a vertiport location-allocation methodology for Urban Air Mobility (UAM) from the perspective of transportation network topology. The location allocation of vertiports within a transportation network is a crucial factor in determining the unique characteristics of UAM compared to existing transportation modes. However, as UAM is still in the pre-commercialization phase with significant uncertainties, there are limitations in applying location-allocation models that optimize objective functions such as maximizing service coverage or minimizing travel distance. Instead, vertiport location-allocation should be approached from a strategic perspective, taking into account public capital investments aimed at improving the transportation network by leveraging UAM’s distinct characteristics compared to existing urban transportation modes. Therefore, we present a methodology for evaluating the impact of vertiport location-allocation strategies on changes in transportation network topology. To analyze network topology, we use the Seoul Metropolitan railway network as the base network and construct scenarios where vertiports are allocated based on highly connected nodes and those prioritizing structurally vulnerable nodes. We then compare and analyze global network efficiency, algebraic connectivity, average shortest path length, local clustering coefficient, transitivity, degree assortativity and modularity. We confirm that while allocating vertiports based on network centrality improves connectivity compared to vulnerability-based allocation, the latter approach is superior in terms of network efficiency. Additionally, as the proportion of vertiports increases, the small-world property of the network rapidly increases, indicating that the vertiport network can fundamentally alter the structure of multimodal transportation systems. Regardless of whether centrality or vulnerability is prioritized, we observe that connectivity increase exponentially, while network efficiency changes linearly with the increase in vertiport proportion. Our findings highlight the necessity of a network-based approach to vertiport location-allocation in the early stages of UAM commercialization, and we expect our results to inform future research directions on vertiport allocation in multimodal transportation networks.

Abstract: Supply chains, which have numerous participants, are exposed and vulnerable. In recent years, this has been evident in disruptions caused by circumstances that have changed the context, such as: 1) the COVID-19 pandemic, 2) the Suez Canal blockade, 3) the war in Ukraine. These circumstances caused disruptions in supply chains and surprised numerous participants in the international market, individual organizations, but also states and entities around the world. This caused confusion and large financial losses for numerous global market participants, but also for all citizens on the planet. To significantly reduce the damage in future crises, it is necessary to act preventively and proactively. In the paper, the authors research, explain and propose three original models to prevent major damage: 1) a model for individual organizations, 2) a national economy model and 3) a global model. The authors conduct research by applying methods of scientific cognition and analysing three case studies from the recent past. It is concluded that by applying the models presented in the paper, the resilience of supply chains increases, and the damage from disruptions in supply chains in future crises can be significantly reduced, and the quality of life of everyone on the planet less threatened.