Future Transp., Volume 6, Issue 3 (June 2026) – 30 articles

Carbon-neutral ship technologies not only protect the environment but also ensure the maritime sector’s future competitiveness and compliance with international regulations. Therefore, while the transition to carbon-neutral solutions in both port investments and ship technologies is an indispensable part of sustainable maritime transport, some safety risks remain uncertain. This study examines the safety aspects of carbon-neutral ship technologies (hydrogen, ammonia, methanol, battery systems, and other alternative fuels) and demonstrates how risks can be managed within the ALARP (As Low As Reasonably Practicable) framework. For this purpose, a risk matrix was created in the study using probability and severity values, an ALARP classification was made, and FMECA/HAZOP (Failure Mode, Effects, and Criticality Analysis/Hazard and Operability Study) summaries were prepared for critical risks. Subsequently, reasonable and practicable mitigation options were presented for each risk, covering technical, operational, and human factor dimensions. Analyses show that hydrogen poses an explosion risk, ammonia has toxicity and environmental impacts, methanol poses an invisible flame risk, and thermal runaway levels in battery systems are unacceptable. Other fuels (biofuels, LNG derivatives (blue fuels, bio-LNG), synthetic gases, and electro-fuels) offer opportunities in terms of sustainability and infrastructure compatibility but also carry some fundamental risks along with limitations in production capacity. Engineering solutions, operational measures, and human factor practices play a critical role in mitigating all these risks. The widespread adoption of carbon-neutral ship technologies is a process that requires a systematic approach not only to environmental sustainability but also to safety. Full article

Electric scooters (e-scooters) have rapidly integrated into university transportation networks; however, there is limited empirical understanding of users’ and non-users’ perceptions, which is essential for developing effective and inclusive policies. This study addresses this gap by analyzing the differential perceptions of e-scooter adoption, safety, and policy preferences at Louisiana State University (LSU). A quantitative, cross-sectional survey was administered to 1036 respondents (592 users and 444 non-users). Statistical analyses, including Chi-square tests and Binary Logistic Regression, were used to identify key perceptual differences and behavioral predictors of e-scooter usage. Results show that users were predominantly male undergraduates, with speed (90%) and convenience (61%) as the primary motivators. Users were over 12 times more likely to perceive e-scooters as safer than walking. In contrast, non-users cited frequent scooter misplacement (84%) as their top barrier to adoption. Logistic regression confirmed that concern about misplacement (Odds Ratio = 0.076) and support for restrictive policies were strong negative predictors of use, while belief in safety and low cost were positive predictors. These findings may help inform campus micromobility policy discussions. The strong negative perceptions associated with scooter misplacement suggest that designated parking hubs and geofencing strategies could help improve campus operations and pedestrian accessibility. In addition, because safety perception was identified as an important predictor of e-scooter use, targeted safety awareness and educational initiatives may help improve rider behavior and address perceived operational safety concerns. This strategy ensures a balance between user adoption incentives and the safety/accessibility needs of the entire university community. Full article

This study examines which onboard human functions remain essential for crewed ships operating under hybrid autonomous operations with Remote Operation Center (ROC) supervision. As maritime operations transition toward higher levels of autonomy, a critical challenge lies in determining the functional boundary between onboard crews and shore-based control systems. A three-round Delphi method was conducted with 20 maritime experts from five stakeholder domains to identify and validate essential onboard functions. The analysis adopts a function-based perspective, distinguishing core functional responsibilities rather than traditional occupational roles. The Delphi analysis resulted in the validation of four primary onboard function groups: Management, Operation and Control, Maintenance and Recovery, and Automation/ICT/Network. All four groups satisfied the predefined importance and stability criteria in Round 2, with mean importance scores ranging from 4.35 to 4.70 and coefficients of variation ranging from 0.09 to 0.12. In Round 3, all function groups also exceeded the minimum CVR threshold of 0.42, with CVR values ranging from 0.70 to 1.00. Operation and Control showed the highest mean importance score (4.70) and CVR value (1.00), indicating the strongest expert agreement regarding its essentiality under hybrid autonomous operations. These results demonstrate that onboard decision-making authority, manual or override capability, technical recovery, and automation-related system supervision remain non-substitutable despite ROC support. The findings provide quantitative evidence for defining minimum onboard functional requirements and offer a structured basis for future discussions on manning, training, onboard–ROC role allocation, and regulatory frameworks for Maritime Autonomous Surface Ships (MASS). This study contributes to clarifying the functional architecture of hybrid autonomous ship operations and supports safer and more accountable human–automation integration strategies. Full article

Real-time navigation systems are increasingly used to provide optimal driving routes together with accurate travel time predictions that reflect dynamic urban traffic conditions. Recent advances have focused on integrating structured traffic data from traditional APIs with unstructured, context-rich information extracted via semantic crawling of news websites and social media platforms. This survey reviews state-of-the-art approaches that combine these heterogeneous data sources to improve route planning and travel time estimation, with special attention to the challenges posed by incident detection, event extraction, and multimodal data fusion. We discuss core methodologies including natural language processing techniques for event recognition, machine learning models for traffic prediction, and graph-based routing algorithms, highlighting their advantages and limitations. Finally, we outline open research directions for building context-aware navigation systems able to adapt to real urban mobility conditions. Full article

Transit accessibility is a critical determinant of urban equity (SDG-11) in the Global South, a term referring to emerging economies characterised by rapid urbanisation and significant infrastructure deficits. A significant ‘compliance–resilience gap’ persists in intermediate Indian cities. This study evaluates a 10.2 km primary transit corridor in Udupi, auditing 42 transit interfaces across 21 nodes using a unified 14-parameter framework. Analytical reliability was confirmed via inter-rater reliability testing (Krippendorff’s alpha = 0.822). Using a joint display synthesis, technical compliance failures were mapped to qualitative user narratives. Results supported Hypothesis 1 (H1) via chi-square testing, revealing systemic failures (p < 0.05) in ramps (2%) and information systems (0%). Hypothesis 2 (H2) was validated through a one-sample t-test, showing that stakeholder perception (mean = 1.55) was statistically significantly lower than the neutral threshold (t(99) = −21.10, p < 0.001). These deficits triggered restrictive user adaptation strategies, including temporal displacement and forced social dependency. The study establishes a replicable ‘justice-centred’ audit framework to prioritise interventions in resource-constrained urban contexts. Full article

The rapid development of Maritime Autonomous Surface Ships (MASS) requires advanced data-driven approaches for navigation safety monitoring and human-factor-aware risk analysis. This research proposes a fractal–episodic framework for assessing ship-control microvariability from normalized AIS/ECDIS trajectories in risk-oriented navigation monitoring, with particular relevance to MASS. The framework converts local micro-motion irregularities into passage-level indicators through sliding-window analysis of XTE-derived signals; computation of Higuchi, DFA, and Katz fractal measures; formation of a nine-component track signature; min–max normalization; and weighted aggregation into a chaos score complemented by a confidence index. The proposed framework can support intelligent monitoring and decision-support systems in autonomous maritime operations by providing interpretable behavioral indicators derived from AIS/ECDIS data. Full article

Maritime pilotage is increasingly shaped by decarbonisation, digitalisation, and wider sustainability pressures, yet the integration of sustainability thinking into pilotage training remains insufficiently understood. This study addresses that gap by examining sustainability thinking not simply as an issue of awareness, but as a problem of training integration in a safety-critical professional context. Using an exploratory, theory-informed quantitative design, the study reinterprets primary survey data from 39 active maritime pilots through a deductive analytical framework combining sustainability pillars, integration domains, sustainability thinking sub-competencies, composite analytical conditions, and structured interpretive synthesis. The findings show that sustainability-oriented thinking is already present in pilotage practice, but that its integration into training remains uneven. It appears strongest where it is embedded in operational judgement and socially established professional norms, and weaker where it depends on pedagogical reinforcement, institutional consistency, and digital credibility. The main challenge is therefore not the absence of sustainability awareness, but the inconsistent translation of that awareness into trainable, repeatable, and professionally reinforced competence. By deriving barriers, enablers, drivers, and risks, the study offers a more applied framework for understanding sustainability thinking as a training and professional development issue in maritime pilotage. Full article

This study investigates associations between socioeconomic and travel variables among users of the São Paulo metro, focusing on travels made for work and study purposes, which are expected to reflect regular commuting patterns, and identifies the main variables associated with mobility characteristics within this group. Using data from the 2017 Origin–Destination Survey conducted by the São Paulo Metro Company, a set of 10,522 respondents was analyzed. The statistical analysis employed Pearson correlation, factor analysis of mixed data (FAMD), and multiple linear regression. The findings indicate that both socioeconomic and travel variables were significantly associated with mobility characteristics among metro system users in the Metropolitan Region of São Paulo (RMSP). The main variables associated with these mobility characteristics were the distance between origin and destination, the distances to the respective stations, travel duration, age, study status, employment status, education level, Brazilian Criteria score, and number of vehicles. Based on the FAMD, these variables were organized into multiple dimensions that could be descriptively grouped into three main groups of information: travel burden and spatial accessibility; life-stage and educational/occupational profile; and life-stage and socioeconomic position. The socioeconomic composition of consistent metro users predominantly includes middle and middle-lower economic classes, with lower economic class, lower household income, and lower education levels being associated with longer travel distances and durations. The study also revealed that most metro travels are within 20 km, with an average travel time of 74 min. These findings suggest that improved infrastructure and better-distributed metro networks throughout the RMSP may contribute to enhancing accessibility, promoting social inclusion, and improving transportation equity. Full article

Driving in snow presents major safety challenges due to reduced visibility and slippery road conditions. Simulation-based tools may help improve hazard awareness; however, their effectiveness depends on how realistically they represent real-world driving experiences. This study examines the perceived realism and learning outcomes of a browser-based snow-driving simulation. A total of 87 licensed drivers with prior snow-driving experience interacted with a first-person browser-based simulation and subsequently completed a structured questionnaire. Composite indices were developed to measure Real-World Risk Perception (RWRP), Simulation Realism (SRI), Learning and Reflection (LEARN), and Awareness and Behavioral Reconsideration (AWARE). Quantitative analyses included reliability testing, descriptive statistics, correlation analysis, and multiple regression, complemented by qualitative thematic analysis. Results showed that perceived simulation realism was significantly associated with self-reported learning and awareness outcomes, whereas prior real-world risk perception was only weakly associated with post-simulation responses. Behavioral consistency between reported real-world and simulated driving behaviors was limited, suggesting that increased cognitive awareness does not necessarily correspond to behavioral equivalence. Qualitative findings identified limitations in vehicle dynamics, environmental complexity, traffic interactions, and emotional realism. Overall, the findings suggest that perceived realism plays a central role in shaping learning and awareness outcomes in browser-based driving simulations. The study highlights the educational potential of accessible web-based simulation environments while also emphasizing limitations in behavioral realism and transfer. Full article

Rapid urbanization has intensified congestion, environmental pressures, and transport inequities, thereby increasing interest in Smart Urban Mobility (SUM) as an approach that combines digital technologies, sustainable transport strategies, and data-informed decision-making to respond to these challenges. However, the evaluation of SUM remains fragmented due to the absence of harmonized assessment frameworks and the diversity of methodologies applied across smart city contexts. This study presents a systematic literature review of evaluation approaches and indicator architectures for SUM in smart city contexts. Using a PRISMA-guided screening process, 33 eligible studies were selected from 412 retrieved records. Three main methodological groups were identified: quantitative approaches, multi-criteria decision-making methods, and qualitative or participatory frameworks. A total of 273 indicators were organized into eight factor categories, confirming the multidimensional nature of smart mobility assessment while also revealing limited consistency in indicator selection and application across studies. Across the selected studies, current evaluation practices are increasingly linked to project prioritization, planning, and decision support; however, their effectiveness remains constrained by data inconsistencies, governance fragmentation, and insufficient user inclusion. These findings highlight the need for assessment frameworks that are sufficiently comparable to enable cross-city learning, yet flexible enough to reflect local contexts and institutional realities. Full article

Autonomous mobility is reshaping how travel time is perceived, experienced, and monetised. Most existing studies have examined the value of time (VOT), willingness to pay (WTP), comfort and safety perception, digital services, and user perception as isolated phenomena, with limited efforts to integrate these dimensions into unified analytical frameworks. This study aims to address the fragmented nature of existing research by developing an integrated understanding of user behaviour in autonomous mobility, linking VOT, WTP, psychological constructs, and service-related factors within a unified analytical perspective. A systematic review methodology following PRISMA 2020 guidelines was applied. A total of 81 peer-reviewed studies published between 2015 and 2026 were included and analysed, focusing on Private Autonomous Vehicles (PAVs) and Shared Autonomous Vehicles (SAVs). The results reveal three main trends. First, autonomous travel introduces greater flexibility in time use and enables productive or leisure activities during travel. Second, behavioural aspects of VOT and WTP are strongly influenced by psychological constructs such as trust, safety, and risk perception. Third, notable differences emerge between PAV and SAV contexts, particularly in terms of comfort, control, and safety perception. The literature predominantly employs stated preference surveys, discrete choice models, and hybrid models incorporating psychological factors. However, fragmentation persists in modelling behavioural aspects of time perception and shared mobility services. This study provides a structured synthesis of existing evidence and highlights key research gaps by integrating economic, psychological, and service-related dimensions. The findings emphasise the importance of context-specific and psychologically informed modelling approaches to better understand user acceptance and behavioural adaptation in autonomous mobility systems. Full article

Accurate short-term traffic prediction is a key requirement for modern traffic management systems, yet many existing approaches remain focused on offline evaluation and do not address the challenges of continuous real-time deployment. In this work, we present a context-aware spatiotemporal graph convolutional network (STGCN) framework designed for low-latency, scalable traffic forecasting under operational conditions. The proposed approach integrates structural information from the road network, temporal regularities derived from historical data, and a residual correction mechanism trained on systematic prediction errors observed during real-time operation. The framework is designed to remain lightweight, enabling continuous minute-level inference without computational overhead that would hinder long-term deployment. The methodology is evaluated in two real-world case studies of different scale and complexity. In Thessaloniki, Greece, multiple forecasting models are evaluated across different temporal resolutions using one-minute speed data, with the proposed STGCN selected for real-time deployment. A residual correction module trained on historical prediction errors further improves real-time forecasting accuracy compared to the baseline STGCN deployment. Scalability is further demonstrated in the South Holland region of the Netherlands, where the same architecture is applied to a larger network and extended to multi-horizon forecasting. Results show that the proposed framework achieves competitive predictive performance while maintaining low computational cost, and that incorporating residual error learning provides a robust and practical solution for improving forecasting accuracy in real-world deployments. These findings highlight the importance of combining domain-specific modeling with operational considerations in traffic prediction systems. Full article

Predicting origin–destination flows in high-density bike-sharing systems remains challenging due to the lack of models that jointly capture temporal dynamics and behavioral variability in urban mobility. In this study, we introduce a spatio-temporal optimal transport framework with dynamically calibrated behavioral regularization that integrates physical network costs with historical mobility priors to infer latent behavioral structure in trip patterns. Unlike static or purely predictive approaches, the proposed framework captures temporal spillovers across hourly intervals, allowing for the continuous evolution of mobility flows. We reinterpret the regularization parameter as a behavioral persistence indicator governing the trade-off between cost minimization and prior adherence. This parameter is dynamically calibrated over a 12-month period using Kullback–Leibler divergence from historical priors, enabling a behavioral diagnostic perspective on mobility regimes. Empirically, we uncover statistically significant regime shifts: weekday mobility is dominated by cost-efficient flows, whereas weekend behavior exhibits stronger adherence to historical mobility patterns and greater variability. We further identify systematic weather-related modulation, with adverse conditions associated with reduced behavioral persistence and patterns consistent with a contraction of discretionary mobility. These findings demonstrate that the proposed framework yields an interpretable behavioral metric for urban mobility systems. This has implications for adaptive mobility management, enabling data-driven rebalancing strategies that respond to temporal variation in behavioral regimes. Full article

This study proposes a transparent, data-driven framework for behavior recognition based exclusively on IMU measurements, hypothesizing that vehicular jerk-based features can help in differentiating driving behavior. Unlike studies relying on direct jerk values, our approach derives novel findings from jerk-based features. For rolling windows of 300 samples, a comprehensive set of statistical and dynamic descriptors is extracted, including amplitude, variance, standard deviation, coefficient of variation, standard error, skewness, and kurtosis, as well as jerk-based features such as jerk_std, jerk_variance, jerk_amplitude, and jerk_spikes. Statistical analysis is used to identify features with strong discriminative power. The selected features are used to compute the Driving Score (DS) and, along with the Kernel Density Estimation (KDE) and associated statistics, provide a driver’s profile. Low DS values are consistently associated with increased jerk variability, whereas high DS values correspond to smoother and more controlled motion profiles. The robustness of the proposed framework is evaluated using several machine learning classifiers as baselines, with the jerk-based features as inputs. For the aggressive driver class, the Driving Behavior Score (DBS) model reports a Recall of 0.952 and an F1 of 0.925. For the normal driver class, the DBS model reports a Recall of 0.839 and an F1 of 0.879. The model has a total accuracy of 0.907. Also, Logistic Regression and ensemble models like Extreme Gradient Boosting (XGB) and Random Forest (RF) perform well. The proposed framework offers an explainable, computationally efficient alternative to conventional machine-learning classifiers for identifying aggressive drivers. It relies on lightweight statistical computations being suitable for real-time implementation. Full article

Pedestrian safety at urban intersections requires risk-aware mechanisms that extend beyond binary collision detection toward comparative prioritization among multiple agents. This study introduces the Intelligent Pedestrian Model (IPM), a reference-normalized scalar framework that represents pedestrian risk as a function of trajectory, contextual, infrastructural, and behavioral factors, decomposed into Exposure and Severity components. Building on IPM, the Safety-Prioritized Trajectory Model (SPTM) operationalizes the Exposure component using an observation-only, leakage-free kinematic proxy embedded into a cost-aware negative log-likelihood objective. Evaluation on the ETH/UCY benchmark under a strictly inductive protocol shows that moderate prioritization (β ≈ 1.0) improves best-of-K multimodal performance (ALL FDE@K: 0.979 → 0.970 m) while maintaining mean displacement accuracy within seed-level variability. The results indicate that Exposure-based weighting does not act as a global accuracy enhancer but redistributes predictive capacity toward safety-relevant motion regimes. Validation currently covers two ETH/UCY folds under a controlled inductive protocol, while broader cross-fold evaluation remains for future work. Full article

Real-time crash risk assessment is a key component of proactive road safety management, enabling the identification of hazardous conditions within short temporal intervals before crashes occur. Traditional crash-based models are unsuitable for such applications due to the rarity, reporting delay, and stochastic nature of crash data. Traffic conflicts, capturing near-miss interactions between road users, provide a practical alternative for real-time safety analysis. Over the past decade, numerous modelling approaches have been developed to translate conflict information into crash risk estimates; however, the literature remains fragmented and lacks a unified analytical synthesis. This review presents a state-of-the-art, model-centric analysis of conflict-based approaches, classifying them into five paradigms: statistical/regression-based, Bayesian, extreme value theory (EVT), machine learning (ML), and hybrid models. Beyond classification, the study conducts a structured cross-paradigm comparison across key dimensions, including conflict representation, data characteristics, temporal modelling, uncertainty treatment, validation strategies, computational complexity, and operational readiness. The paradigms are further interpreted through the complementary lenses of conflict frequency and severity. The review identifies key research gaps, including fragmented conflict definitions, challenges in modelling rare and extreme events, incomplete treatment of uncertainty and spatiotemporal dynamics, and limitations in validation, transferability, and deployment. Emerging research directions include standardized and adaptive conflict indicators, EVT–machine learning integration, integrated uncertainty-aware frameworks, advanced spatiotemporal modelling, transferable models, and scalable real-time implementation. By combining structured evidence mapping and cross-paradigm synthesis, this study supports model selection, development, and deployment for dynamic crash risk assessment. Full article

Advanced Driver Assistance Systems (ADAS) and Automated Driving Systems (ADS) are becoming convenient modes of transportation; however, their safety remains a critical concern as crashes continue to occur. To reveal crash trends and temporal variations, this study develops time-series forecasting models to predict future crash counts of such vehicles. The crash dataset released by the National Highway Traffic Safety Administration (NHTSA) has been used here. Two univariate forecasting models—the Seasonal Autoregressive Integrated Moving Average (SARIMA) and the Facebook Prophet model—have been used here for different datasets. The models were trained on 30 months of data (July 2021 to December 2023) and validated on 6 months of data (January–June 2024). Validation metrics include Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and Theil’s U1 statistic. Results showed that Facebook Prophet significantly outperformed SARIMA for both datasets, achieving an RMSE of 2.71 and an MAPE of 6.9% for ADAS, and an RMSE of 2.24 and an MAPE of 8.85% for ADS. For both systems, the model revealed empirically observed cyclical patterns and consistent rising trends. ADAS crashes exhibit a bimodal temporal pattern, with recurring peaks in January and May–June, alongside notable troughs in February–March and August–September. ADS displays a trimodal pattern, with recurring peaks in April–May, August and October, alongside notable troughs in December and the early winter months. These patterns represent empirically identified temporal regularities rather than causally attributed seasonality. From the future forecasts for July to December 2024, the model showed that ADAS crashes are expected to range between 40 and 80 per month, while ADS crashes are projected to remain between 20 and 40 per month. These findings underscore the need for proactive safety measures and enhanced regulatory oversight during identified high-risk periods to mitigate the growing trend in AV crashes. Full article

This study examines the determinants of bilateral trade asymmetries between Türkiye and its three main EU partners—Germany, Bulgaria, and Romania—over 2002–2024. Within the gravity framework, bilateral symmetry in trade data implies that reported exports should equal partner imports (Xᵢⱼ = Mⱼᵢ). Deviations from this condition reflect systematic distortions caused by valuation practices, institutional gaps, and crisis-induced disruptions. This study employs a fixed-effects panel framework to identify the structural and contextual determinants of mirror−data asymmetries in Türkiye–EU trade. Using HS2−level mirror statistics from TÜİK and Eurostat, three asymmetry measures—the Bilateral Asymmetry Index (BAI), Absolute Logarithmic Difference (ALD), and Relative Symmetry Index (RSI)—are estimated through a fixed-effects panel model. Results show that a one−unit improvement in logistics performance (LPI) reduces asymmetry by approximately 0.17 points (p < 0.01). Maritime connectivity (LSCI) shows a small but statistically significant positive coefficient, while exchange rate volatility remains insignificant. The effects of global crises are heterogeneous: the 2008 financial crisis significantly increases asymmetry (+0.07, p < 0.01), whereas COVID−19 is associated with a reduction in asymmetry (−0.04, p < 0.01). The interaction between LPI and crisis periods is negative and significant (−0.03, p < 0.05), confirming that a stronger logistics capacity buffers crisis-induced reporting gaps. Country-specific results reveal that Romania drives much of the variation (within−R² = 0.26), while Germany remains largely insulated from crisis effects. The findings highlight that deviations from bilateral symmetry are driven by structural and institutional factors rather than random error. Policy recommendations stress harmonized customs valuation, digital logistics integration, and enhanced Türkiye–EU statistical coordination to strengthen trade data reliability and crisis resilience. Full article

The transformation of transport is necessary not only for climate change mitigation, but also for increasing competitiveness, developing modern technologies in transport, and improving the well-being and quality of life of the population. This article discusses the current state of the transformation of transport and infrastructure to low/zero emission within EU member states and, in particular, their smart cities. This article discusses the challenges, modern technologies, disadvantaged groups and overall concept of transformation with the aim of designing the most effective strategy for transport transformation in the EU, potentially at the smart cities level. The potential relationship between the position of EU member states in the Climate Change Performance Index (CCPI) ranking and different environmental and non-environmental indicators in the EU is identified and analyzed. Regression and ordered logit models are calculated. The results show that only minimum indicators are not correlated, and greenhouse gas emission (GHG), urbanization rate in the EU member state and the ratio of private car ownership to public transport usage have a significant impact on the potential transformation of transportation and a country’s ranking in the CCPI. The odds ratio for urbanization rate is 3.18 (+1 means better ranking 24 times greater) and 4.68 for the ratio of private car ownership to public transport usage (+1 means better ranking 108 times greater). The discussion of the article defines research trends aimed at improving the level of transport transformation and challenges related to successful transformation. Full article

Cooperative Connected and Automated Mobility (CCAM) is a critical paradigm for overcoming the limitations of single-vehicle intelligence and enabling coordinated intelligent transportation. To address the lack of systematic reviews towards recent CCAM advances, this paper presents a comprehensive review of relevant publications from the past five years. First, we establish a unified framework spanning communication, perception, decision-making, and control, and clarify the associated core components and technologies. Then, we identify three major bottlenecks that constrain large-scale CCAM deployment: uncertainty propagation along the perception-decision-control (PDC) chain, misalignment between functional safety and SOTIF standards, and inadequate end-to-end cybersecurity protection. In the context of 5G-A/6G, edge computing, and large-language-model-driven intelligence, we further propose targeted research directions. This survey aims to provide a systematic reference for theoretical investigation and engineering implementation. Full article

This article conducts a thorough comparative analysis of public transport systems in Curitiba and Lisbon, focusing on cost-efficiency and structural performance from the user’s viewpoint. Curitiba is noted for pioneering the BRT model in the 1970s, while Lisbon is evolving towards a multimodal system with substantial investments in integration and user-centric policies. Employing a case study methodology and mixed analytical approaches, the analysis examines governance structures, network architecture, financing mechanisms, and service quality indicators. The findings indicate that although Curitiba imposes a similar or higher fare burden relative to user incomes, it offers significantly lower service value across various dimensions, including modal diversity and infrastructure quality. In contrast, Lisbon’s integrated governance model for bus and tram networks proves effective in enhancing accessibility and sustainability, despite some coordination issues with centrally governed transport networks. This study contributes to the international discourse on the limitations of single-modal transport systems and highlights the necessity of institutional integration, long-term investment, and adaptive governance frameworks for urban mobility transformation in the 21st century. Full article

Unsignalized intersections involve complex interactions among heterogeneous road users and are associated with elevated safety risks. Although surrogate safety measures derived from high-resolution trajectories enable proactive safety assessment, such data are not widely available in routine monitoring systems, which often provide only coarse-grained traffic observations. This study examines how the inferability of surrogate safety information changes as the available traffic data become progressively coarser. Using the high-resolution inD dataset, we implement a controlled feature degradation framework across three nested levels of data granularity and develop intersection-specific models for three tasks: critical conflict detection, dominant direction classification, and vulnerable road user (VRU) involvement identification. Model performance and changes in variable importance are evaluated using PR-AUC and SHAP analysis. The results show clear task-dependent degradation. Models based on high-granularity data achieve strong overall performance, with an average PR-AUC above 0.88. Dominant direction classification remains relatively robust as data granularity decreases, with PR-AUC declining from 0.970 to 0.893, whereas VRU involvement identification deteriorates substantially, from 0.991 to 0.697. The results further indicate that vehicle-based traffic variables retain meaningful predictive value for conflict detection and direction classification but are insufficient for reliable inference of VRU-related risk. Interpretability analysis shows a progressive shift in model reliance from kinematic interaction variables to coarser exposure-related and structural descriptors as observability decreases. These findings clarify the relationship between data granularity and task-dependent surrogate safety inference at unsignalized intersections. Full article

Real-time traffic signal control represents a key challenge in modern intelligent transportation systems, particularly under highly variable traffic flows and the presence of priority vehicles. This study proposes a hybrid framework for adaptive signal plan control at a signalized intersection. The framework integrates deep learning-based traffic prediction, surrogate-based performance evaluation, and reinforcement learning-based adaptive control. Short-term traffic flow is predicted using recurrent neural networks, providing anticipatory information for traffic control decisions. Based on predicted flows and generated candidate signal plans, a machine learning surrogate model enables fast estimation of key performance indicators, including average vehicle delay and queue length. Adaptive control is implemented using the Proximal Policy Optimization algorithm within the SUMO environment via TraCI, which enables real-time fine-tuning of signal phases. A dedicated priority and stability module ensures effective emergency vehicle preemption and adaptive public transport priority while preserving intersection stability. Simulation results show that the proposed framework reduces average vehicle delay by up to 35% compared with FT and by up to 15% compared with standalone RL, while also improving traffic flow efficiency and priority vehicle performance. Full article

Growing mobility demand and declining vehicle utilization motivate dual-use vehicles that can alternately transport passengers and freight. This work presents an early-stage utility value analysis to select a baseline concept and integrates it into model-based systems-engineering architecture development of an autonomous dual-use shuttle. Existing dual-use-capable shuttle concepts were screened and comparatively assessed using a utility value analysis with exclusion criteria and weighted evaluation criteria, including operational versatility, module exchange flexibility, infrastructure effort, battery positioning, and technology readiness. Criterion weights were derived by pairwise preference analysis, emphasizing the versatility of use scenarios. The highest-ranking concept, 101 Modular Mobility, was selected as the reference architecture. Subsequently, a SysML system model was developed in a MagicGrid-structured model-based systems-engineering (MBSE) process, covering stakeholder needs, key use cases such as transport service usage, module exchange, and automated charging, and the resulting system context and interfaces. The system model is augmented by a tailored Grey Box structural viewpoint within the MagicGrid workflow to make module boundaries and inter-module interfaces explicit for the modular dual-use shuttle architecture. The resulting model provides a traceable early architectural baseline for further refinement and subsequent verification activities. Full article

This study proposes a rigorous optimization framework for the design of traffic counting station locations in large-scale highway networks, with specific application to Thailand’s national highway system. A mixed-integer linear programming (MILP) model is developed to determine the optimal sensor placement under budget-constrained scenarios while explicitly incorporating existing infrastructure. The model aims to maximize origin–destination (OD) flow observability and minimize estimation error, measured by the percentage of OD flows intercepted and root mean square error (RMSE). The proposed framework is validated using a real-world network. The results demonstrate that the optimized design significantly outperforms conventional approaches, including random and high-flow-based selection methods, achieving over 70% reduction in estimation error and 93% of OD flows intercepted with a feasible number of stations. Furthermore, the statistical representativeness of the selected locations is validated across spatial, functional, and traffic characteristics and traffic measurement errors. The findings provide a scalable and cost-effective decision-support tool for transport authorities in developing countries seeking to modernize transportation planning, traffic management, and infrastructure development under limited resources. Full article

The transition to autonomous driving is creating mixed traffic environments in which human-driven vehicles, partially automated vehicles, and autonomous vehicles must continuously interact, adapt, and respond to one another. This paper presents a comprehensive review of driver behavior in mixed traffic with autonomous vehicles, with emphasis on the sociotechnical nature of human–machine coexistence. The review synthesizes recent evidence on behavioral adaptation in car-following and tactical decision-making, trust calibration, situational awareness, takeover performance, internal and external human–machine interface design, surrogate safety metrics, vehicle-to-vehicle communication, operational design domains, and data-driven scenario generation. The literature shows that drivers do not respond to autonomous vehicles uniformly. Instead, behavior varies by driving style, perceived predictability of the automated vehicle, interface transparency, and traffic context. The review also emphasizes that these interaction patterns are context-dependent and may differ substantially across regions, particularly in dense mixed traffic environments. While some adaptations can improve stability and safety, others can encourage opportunistic maneuvers, overtrust, confusion, or degraded takeover quality. The review also highlights that crash data alone are insufficient to assess safety in mixed traffic, and that near-miss analysis, surrogate conflict metrics, and scenario-based evaluation are essential for understanding safety-critical interactions. Across the literature, a central inference emerges: adaptation to autonomous vehicles is real, but it is not automatically stabilizing. Safe deployment therefore depends not only on technical vehicle performance but also on behavioral legibility, transparent communication, calibrated trust, and robust evaluation under diverse real-world conditions. The paper concludes by identifying major research gaps, including the lack of longitudinal studies, incomplete standardization of surrogate metrics, limited understanding of vehicle conspicuity effects, and the need for integrated frameworks that jointly assess driver behavior, system design, and scenario-based safety. Full article

Dry ports are becoming increasingly important elements of port–hinterland transport systems, particularly as maritime gateways face rising congestion, infrastructure pressure, and coordination challenges within global supply chains. As international trade expands and logistics networks grow more complex, inland terminals are progressively evolving into integrated intermodal platforms that support more efficient freight distribution between seaports and their hinterlands. This study presents a PRISMA-based systematic review of research on dry port–seaport systems covering the period 1980–2025. Following a structured screening and selection procedure, peer-reviewed publications were identified and analyzed to examine conceptual developments, thematic orientations, geographical scope, and decision-making perspectives within the field. Particular attention is given to the growing relevance of digital transformation, including artificial intelligence and machine learning, in shaping future dry port operations and network design. By synthesizing existing contributions and identifying research gaps, this review provides a consolidated understanding of the evolution of dry port research and outlines key directions for advancing sustainable, resilient, and data-driven port–hinterland systems. Full article

The rapid increase in the number of cars in large cities has made efficient parking management one of the major challenges of urban transportation systems. The present study aims to develop a smart framework for sustainable allocation of parking spaces in urban environments, and presents an integrated approach based on digital twin and multi-objective optimization. In this framework, a digital model of the urban parking system is created that is able to analyze real and simulated data related to parking demand, space occupancy status, and traffic flow and support optimal allocation decisions. The results of the analysis show that using the proposed framework can reduce parking search time by an average of 28%, make the distribution of parking use more balanced, and consequently reduce the amount of pollutant emissions from vehicle movement by about 17%. Also, sensitivity and scalability analyses show that the proposed model also has stable and reliable performance in large urban networks. These results indicate that the proposed framework can be used as an effective tool for developing sustainable parking management systems in smart cities. Full article

To improve ship AIS trajectory prediction under pronounced spatiotemporal coupling and dynamic maneuvering conditions, this study proposes a Spatio-Temporal-Hybrid-Multi-head Attention model (STHA) integrating multiscale convolution, bidirectional long short-term memory, and multi-head attention. Historical AIS data from the Zhoushan waters in 2024 were preprocessed through screening, cleaning, outlier removal, resampling, and cubic spline interpolation to construct trajectory samples. Comparative experiments were conducted against BP, BiLSTM, and BiGRU using MAPE, RMSE, and R² as evaluation metrics. The results show that STHA achieves the best overall predictive performance, more accurately follows trajectory variations across different vessel types, and exhibits better robustness in scenarios involving turning and speed changes. These findings indicate that the proposed model is effective for high-precision ship trajectory prediction and can provide useful support for subsequent collision risk assessment and navigation safety assistance. Full article

This study addresses the question of how augmented reality (AR) technologies can be designed and integrated into maritime navigation systems to meet the needs of young navigators within contemporary socio-technical bridge environments. The article is based on a qualitative, literature-based research methodology involving a structured analysis and synthesis of peer-reviewed journal articles and conference proceedings related to AR interfaces, human performance, decision support, and maritime training. The reviewed studies indicate that AR can enhance perceptual and situational awareness by overlaying navigational information directly into the navigator’s field of view, thereby reducing head-down time, improving spatial alignment of information, and supporting performance in low-visibility and high-traffic conditions. The literature also shows that AR-enabled visualizations and shared displays can support individual and team-based decision-making by facilitating real-time, context-aware information exchange on the ship’s bridge. Safety-related benefits are identified as indirect outcomes of improved perception and cognitive support rather than as isolated technological effects. Simultaneously, the findings highlight that these benefits depend strongly on human-centered interface design and appropriate training. The study concludes that AR has significant potential to enhance maritime navigation for young navigators when integrated as part of a balanced socio-technical system combining technology, human factors, and structured education. Full article