The rapid evolution of autonomous driving technologies is deeply reliant on high-quality datasets and robust data management solutions that cannot be achieved by a single actor working in isolation. This paper examines the growing role of datasets and data spaces in the mobility domain, providing a conceptual analysis of current approaches and identifying key challenges and emerging trends that inform the design of an autonomous vehicle data space framework. While peer-to-peer solutions for data sharing already exist, the concept of data spaces can significantly scale these approaches, creating a self-improving feedback loop that enhances the performance of AI-based driving models by matching the demand for data with the data offered by providers. Specifically, the paper addresses the challenge of acquiring intra-vehicular data to enhance assisted driving functionalities and proposes a methodology for building a data space specialized for automated driving applications. Finally, the paper explores representative use cases, including a Level 4 automated shuttle, TalTech iseAuto, Estonia, and an all-terrain vehicle provided by CERTH, Greece, by conceptualizing data sharing across different domains, where data providers and consumers exchange information within a data space framework, driving continuous improvement in model performance.
Urban transport systems face mounting pressures from climate change, rising energy prices, and ageing assets. Meeting these challenges sustainably requires extending the useful life of infrastructure, improving energy efficiency, and reducing waste across the whole lifecycle. Circular economy principles offer a systemic approach: design for longevity, maintainability, and reuse, while favouring interventions that avoid resource-intensive renewals. Digitalisation is a key enabler of this shift, as Artificial Intelligence-assisted analytics, edge sensing, and Digital Twins make hidden conditions visible and reduce diagnostic costs. This paper presents three European pilot cases from the Interreg CE4CE project to demonstrate these capabilities. In Leipzig, a layered infrastructure monitoring system enabled the detection of eight critical overhead line faults and three previously undetected broken rails during regular passenger service. In Bergamo, high-frequency onboard sensing and Energy Flow Simulations optimized energy recuperation and identified pantograph-infrastructure “hot spots.” In Gdynia, a Digital Twin environment was used to simulate over 110 electrification scenarios, optimizing fleet size and infrastructure investment needs. The results confirm that integrating digital monitoring and simulation tools into public transport operations facilitates a shift from reactive to predictive management, directly supporting resource efficiency, asset longevity, and the transition toward a circular urban mobility system.
Sustainable forestry is fundamental for the supply of raw materials for sustainable products such as wooden houses, packaging, and biofuels. Efficient transport is vital for sustainable forestry, as road transports of roundwood from the forest to recipient industries account for 50
Efficient emergency distribution is critical for European disaster response. In Europe, although truck-UAV systems provide a flexible solution to logistics bottlenecks, the continent’s coastal regions and expansive plains experience highly volatile wind conditions, which expose UAV operations to substantial uncertainties in wind dynamics and energy consumption. By incorporating a novel wind risk penalty term and an urgency-aware prioritization scheme, the proposed model ensures both mission stability and response timeliness. We develop four unique operators and integrate them into a modified Greedy Randomized Adaptive Search Procedure (GRASP)-Adaptive Large Neighborhood Search (ALNS) framework to adapt to the uncertain wind conditions and complex energy consumption. Experimental results based on transformed benchmark scenarios demonstrate that the proposed hybrid algorithm yields a 6.68
Pedestrians enter railway infrastructure at locations where access is prohibited for various reasons, such as taking shortcuts, attempting vandalism, theft or suicide. This behaviour poses a safety concern for both trespassers and railway operations. In Czechia, publicly available orthophotos were used to identify and analyse locations exhibiting evidence of railway trespassing. A total of 1,118 such sites were identified, mostly in built-up areas and farther from authorized crossing points. Historical orthophotos revealed that 727 sites (65
Abstract Autonomous modular vehicles create new possibilities within public transportation services, responding to growing needs for flexible and sustainable transport. Modularity allows for vehicle configurations with standardised load units, enabling multimodal (i.e., interoperable across multiple modes, such as road and rail) and co-modal (i.e., integration of both passenger and goods) transportation systems. Although Demand Responsive Transit (DRT) systems see a substantial body of literature, this is the first comprehensive review focusing on modularity, multimodality, and co-modality in public transportation. This review synthesises 45 peer-reviewed articles, identified through a systematic Scopus search and enriched by snowball sampling, across three dimensions: transport system design, modularity implementation, and optimisation methodologies. The results show a concentration of research on road-based systems, whereas modular systems across multiple transportation modes remain largely underexplored. Similarly, most research attention is given to line- or corridor-based systems, highlighting the need to further explore systems with a more flexible network design to leverage the benefits that modular vehicles offer. Co-modality receives growing interest, but has not yet been systematically analysed from a modular perspective. Methodologically, most studies rely on analytical methods to optimise planning and operations, with less attention to simulation-based methods, which may better include real-world uncertainties, and Artificial Intelligence-based (AI-based) methods, which offer promising opportunities for more robust, flexible and scalable modular transport systems. This review contributes to the field of POD-based multimodal transportation as the first structured overview of multimodal and co-modal aspects in modular transport systems, identifying key research gaps and outlining directions for future research.
This study investigates how physical alterations of paths, including changes in surfacing and lighting conditions, impact the behaviour of pedestrians and cyclists. Using drone-based imaging technologies, data were collected from shared-use paths in two Swedish cities during periods of reduced daylight hours. The study focused on speed and slalom, examining how these measures varied across different subsets defined by combinations of physical changes and lighting conditions. The results reveal that both physical alterations and lighting conditions significantly influence pedestrian and cyclist behaviour. These findings provide valuable insights into the complex interplay between environmental factors and user behaviour on shared pathways. Understanding how changes in environmental features influence pedestrian and cyclist behaviour can guide urban planning and infrastructure design aimed at improving safety and efficiency for all path users.
Independent mobility is crucial for social participation and quality of life in later life, especially in rural areas where public transport is limited. Light electric vehicles (LEVs) may offer a promisingalternativefor everyday mobility; however, it remains unclear which features and contextual conditions are necessary for their usability and acceptance among older adults in their rural settings. As an initial exploratory step within the LeiFlex project, this mixed-methods design study aimed to assess feasibility and generate preliminary insights for further development. A user-centred study was conducted with 20 participants (≥ 60 years) in rural Saxony-Anhalt, Germany. Participants completed supervised test drives with a commercially available LEV, followed by standardized usability and acceptance questionnaires (System Usability Scale, Technology Usage Inventory) and semi-structured focusgroup discussions. Qualitative data were analysed using content analysis and complemented by descriptive quantitative results. Overall, attitudes towards LEVs were positive, although usability was rated as marginal (SUSmean= 53.0). Key requirements included intuitive braking logic, clear visual and acoustic feedback in safety-critical situations, ergonomic adjustability, and perceived safety. Gender differences were observed. Women reported lower usability and were more sceptical, despite having a similar intention to use. The findings suggest that acceptance of LEVs among older adults is shaped not only by modifiable usability and design characteristics, but also the conditions in which these vehicles are used. These preliminary results highlight the need for flexible, modular, and age-appropriate LEV concepts and provide a basis for further investigation in larger and more comprehensive studies.
Abstract Park and Ride systems are typically associated with rail stations or conventional urban Public Transport hubs. In Gleisdorf, Austria, a highway-based bus stop with nearby Park and Ride facilities is being developed to improve regional Public Transport accessibility in a semi-rural commuter corridor. This paper examines the planned system as an exploratory pre-implementation case study and investigates factors influencing the willingness to switch from Private Transport to Public Transport via Park and Ride. An online mobility survey was conducted before construction, and the stated choice experiment was completed by 479 respondents with access to a private car. Binary logit models with respondent-level clustered standard errors were estimated for the full sample and for subsamples within and outside the perceived catchment area of the planned facility. The results identify real-time delay information, annual Public Transport pass ownership, destination-side parking costs and relative travel time conditions as key determinants of switching intentions. Respondents within the perceived catchment area appear more routine-oriented, whereas respondents outside the catchment area evaluate the concept more hypothetically and attribute-based. For implementation, the findings point to three priorities: providing decision-relevant information before the interchange, encouraging trial use among commuters in the perceived catchment area, and avoiding substantial travel time disadvantages for the highway-based bus service during peak periods.
Abstract Shared cargo bike systems offer a practical and promising alternative for short-distance urban trips, especially where cars dominate last-mile delivery and household travel. Yet, their adoption across most of the European continent remains limited and highly context dependent. Understanding the profiles of potential users is essential for designing targeted interventions that promote uptake. Hence, this study draws on survey data from 941 respondents across Budapest to segment potential users. Using Latent Class Cluster Analysis, this study explores the heterogeneity of urban travelers’ attitudinal profiles, identifying how these variations shape their openness to adopting shared cargo bike services. Based on the analysis, 6 clusters are identified: disengaged, sceptics, indifferent, explorers, supporters, and enthusiasts. While middle-aged individuals are almost evenly distributed across clusters, older respondents are mainly disengaged or sceptics, and younger adults show a higher concentration in the indifferent cluster. Car users are predominantly found among sceptics, whereas multimodal individuals or cyclists are typically supporters or enthusiasts. By integrating segmentation which includes attitudinal factors, this study informs targeted strategies for shared cargo bike uptake and offers insights that support sustainable mobility transitions in other urban contexts.
The economic viability of the rehabilitation of nautical bottlenecks on inland waterways depends on the scope and sequence of carrying out the rehabilitation projects. The authors develop a modular cost-benefit analysis (CBA) tool, which enables an economic evaluation and prioritization of rehabilitation projects and show its functionality on a defined transport corridor. The tool calculates the Net-Present-Values (NPV) and Benefit-Cost-Ratios (BCR) for each nautical bottleneck and marginal NPVs for neighbouring bottleneck combinations. Finally, a Greedy-Algorithm is used to rank the investment projects. The findings show a strong path dependency, which means that benefits arise solely when all bottlenecks were rehabilitated. Isolated measures are mostly ineffective. Using the algorithmic approach facilitates a planning of investment projects within the transport corridor. The study contributes theoretically by enlarging the current state of literature in the field of economic evaluation of transport infrastructure investments. Practically, the paper contributes by providing an adjustable CBA tool which facilitates decision-making for policy, project sponsors or waterway maintenance companies. Further research should focus on using real transport and vessel data and the integration of ecological aspects, which could be additional factors, for or against carrying out rehabilitation projects.
Port automation has progressed from standalone mechanical devices to interconnected, data-driven systems that support global trade. This paper examines how port automation has grown, focusing on equipment systems such as quay cranes, Automated Guided Vehicles (AGVs), and Automated Stacking Cranes (ASCs) and other automated yard equipment. Early improvements boosted lifting capacity and reduced manual effort, while newer approaches integrate artificial intelligence (AI), Internet of Things (IoT) sensors, and digital twin simulations to coordinate equipment in real time. These advances have raised efficiency, lowered emissions, and improved safety. However, some obstacles remain. Port managers must solve cooperation challenges, data protocol differences, and cybersecurity risks. Research indicates that advanced autonomy, including reinforcement learning, dynamic scheduling, and AI-based condition monitoring, will be important to solve these problems. The sector also needs to make standards, which ensure that equipment from different managers can communicate easily. Advances in electrification and energy management can help achieve sustainability targets, including reduced carbon footprints and optimized resource consumption. By examining a combined bibliometric analysis, qualitative content coding, and term-frequency analysis, this study reviews current progress and outlines future directions and emphasizes solutions that account for technical, organizational, and social factors. This approach may help modern ports remain adaptable, reliable, and resilient in a rapidly shifting maritime environment.
Planned Special Events (PSEs), including sports fixtures, concerts, and festivals, can impose substantial additional demand on already constrained road networks, often resulting in severe congestion and reduced network reliability. However, evidence on effective traffic management strategies for mitigating these impacts in highly congested urban regions remains limited. This study investigates the transport impacts of PSEs, with a focus on sports events in Greater Manchester, United Kingdom, and evaluates intervention strategies to enhance traffic performance and network resilience across both England’s Strategic Road Network (SRN) and the Greater Manchester local road network. A systematic review of 31 studies was first undertaken to identify interventions to managing event-related travel demand, leading to the classification of six categories of intervention. Historical traffic data, including speed, flow, and occupancy, were then analysed to identify recurrent bottlenecks and critical capacity constraints of event-related congestion. Building on these insights, Dynamic Lane Management (DLM) strategies, specifically reversible lanes and dedicated lanes, were modelled and assessed using the open-source Simulation of Urban MObility (SUMO) platform. The results indicate that reversible lane operation can deliver substantial improvements in network performance, reducing delays by up to 47.43
Abstract Connected Autonomous Vehicles (CAVs) are changing transportation networks and introducing new ways to strengthen and adapt to disruptive events. This systematic scoping review uses PRISMA-ScR to examine how CAVs affect transportation network resilience, focusing on four key attributes of resilience: Robustness, Redundancy, Resourcefulness, and Rapidity (“4Rs”). The review analyzes 106 peer-reviewed articles published between 2014 and 2025. Quantitative synthesis of the “4Rs” centered framework reveals a significant research imbalance: Robustness ( n = 106) and Resourcefulness ( n = 102) are the most extensively documented attributes, followed by Redundancy ( n = 83), while Rapidity ( n = 31) remains the least explored. The review identifies Traffic Congestion (34.91%), Cyber Disruptions (14.15%), and Communication Failures (12.26%) as the primary disruptive events quantified in the literature. The study found that CAVs significantly improve the Robustness and Redundancy by maintaining a constant flow of traffic and creating alternative routes during disruptions. Likewise, Resourcefulness is benefited due to the ability of CAVs to collect information in real time, allowing for effective resource allocation and decision-making during system recovery, while Rapidity remains the least explored attribute, particularly in the post-disruption phase. Furthermore, the empirical basis distribution is predominantly simulation/analytical studies (71 out of 106), while purely empirical studies remain limited (13/106), and pilot/field/FOT evidence is scarce (4/106). Overall, the study highlights the main factors to be taken into account in the implementation of CAVs.
Rail transport is increasingly preferred for its safety and reliability, which can be achieved through a variety of advanced design solutions and technological supports. Among them, ensuring a continuous power supply is crucial for the reliable execution of energy-dependent critical operations. Inspired by the Italian railway infrastructure, here the focus is on Integrated Power Supply (IPS) systems that support critical functionalities, such as the signaling system that regulates railway traffic movement. Fault-tolerant IPS designs are implemented to address unforeseen fault events at the IPS level, which could lead to safety or availability issues. This paper presents a refined stochastic model-based evaluation framework to aid in comparing various IPS redundant architectures with respect to dependability characteristics, mainly focusing on reliability and availability metrics. Major contributions pertain to i) the modeling of four IPS architectures; ii) the integration of models to analyze the IPS behavior under normal and critical conditions; iii) the consideration and modeling of battery failures; iv) a comprehensive analysis campaign to compare the considered IPS architectures in terms of both dependability and cost. A designer can effectively utilize the findings from this analysis to determine the most appropriate IPS organization that meets dependability criteria while also considering potential energy consumption savings. The analyses conducted under realistic parameter settings demonstrate that architectures featuring redundancy at the level of individual IPS components yield the best performance in terms of both dependability and cost. For instance, under specific parameter settings, the CR and IR architectures outperform the SR and MR architectures by roughly three orders of magnitude in terms of Mean Time Between Failures (MTBF), achieving MTBF values of up to approximately 6× 10^7 h.
Signalised intersections are critical points in urban road networks with high crash risks due to traffic density and complex manoeuvres. While traditional safety analyses are reactive, current research emphasises proactive methods. Tools such as Crash Modification Factors (CMFs) and Safety Performance Functions (SPFs) are essential for quantifying the impact of geometric and operational design on safety. However, CMFs tailored to specific geographical and traffic conditions remain limited. This study demonstrates that geometric factors significantly influence crash risk at urban signalised intersections. The developed Poisson regression model showed high predictive accuracy, and the derived CMFs and SPFs provided valid results for safety analyses under local conditions. The proposed ‘a’ index and the Overall Sight Distance Index (L), combined through a composite CMFₗ, serve as practical tools for quantifying geometric risk and prioritising intersection improvements. A key finding is the development of the ‘L’ index, a composite geometric parameter that integrates horizontal skew and vertical gradient to represent the overall sight distance condition at an intersection. The L index is incorporated into a composite crash modification factor (CMFₗ) by combining the individual CMFs for MHABIL and SNSL, enabling quantitative assessment of how combined geometric deficiencies affect crash frequency. Lower L values, reflecting more severe geometric constraints, are associated with higher CMFₗ values and thus greater crash risk, providing a practical tool for ranking intersections and prioritising safety interventions. In conclusion, the study’s results offer engineers and urban planners practical, data-driven tools for the proactive management of geometric risks, even under topographical and spatial constraints in urban environments. This approach supports safer intersection design and targeted safety interventions before crashes occur, moving beyond reactive measures.
Road traffic accidents continue to pose a significant challenge to public safety, resulting in substantial human suffering, economic losses, and increasing pressure on transportation systems. This study proposes a data-driven intelligent transportation framework that integrates machine learning and spatial network analysis to support accident severity prediction, risk-aware route recommendation, and emergency response. A comprehensive dataset comprising traffic conditions, weather information, temporal attributes, roadway characteristics, vehicle information, and driver-related factors was analysed to identify the key determinants of accident severity. Multiple machine-learning models were evaluated, and the Multi-Layer Perceptron (MLP) classifier achieved the highest predictive performance, attaining an overall accuracy of 91.2
The aim of this review is to enhance the visibility of the Mobility Culture Framework (MCF). Developed and productively referenced mostly in Germany, the framework suggests to analyse mobility cultures as the interplay of built space and social conditions. It is also designed to be a transformative planning tool for practitioners. Identifying theoretical, methodological and empirical research gaps, the review is a critical intervention and at the same time incentive to further develop the potentials and benefits of cultural perspectives in mobility research. Two key findings are of particular relevance: Firstly, methodological further developments should dare to integrate contexts beyond large cities. In the course of this, the advantages and disadvantages of mixed-method approaches for analysing mobility cultures should also be identified in a more explicit manner. Secondly and more importantly, the conceptual foundations of the framework need to be strengthened. Frequently performed distinctions between cultural approaches in mobility research and their respective theoretical foundations are only partially tenable and often lost in translation. The similarities and productive synergies are substantial and should be the focus of scientific endeavour – rather than the differences.
This study examines the prediction of bus travel times within urban corridors, using an extensive dataset from transit management databases and on-street sensors. The analysis focuses on a range of variables, including corridor configuration, general traffic conditions, and intrinsic bus transit factors. Employing Multiple Linear Regression (MLR), Artificial Neural Networks (ANN), Support Vector Regression (SVR), and Random Forest (RF), bus travel times are modelled across four distinct urban corridors in A Coruña, Spain, considering dynamic variables like traffic flow rate, general travel time, and average stream patronage per bus stop, with patronage showing the strongest influence on bus travel time in three of the four corridors, similar to the sum of all general traffic variables. Furthermore, these models are applied to a joint dataset encompassing all corridors, incorporating static variables such as bus stops per kilometre, and, for the first time in the field, the percentage of corridors with adjacent parking and percentage with one lane, both considered as an interaction. This allows predictions of bus travel time changes due to corridor modifications and travel times for new bus routes in unserved areas. Findings reveal that, for our dataset, none of the three ML approaches has consistently proven to be preferable in bus travel time predictions, while MLR provides competitive results, balancing accuracy and interpretability despite its flexibility constraints. The study underscores the importance of selecting models based on data range and transit context, advocating for simplicity in constrained scenarios. This developed methodology provides a valuable planning tool for transport agencies, adaptable to other urban contexts, and highlights the benefits of optimising corridor configurations to enhance bus travel time performance.
