Future Transportation

As the Kingdom of Saudi Arabia (KSA) accelerates its transition toward smart mobility under Vision 2030, establishing a robust digital infrastructure is paramount for the safe deployment of autonomous vehicles (AVs). High-definition (HD) maps serve as a critical foundation for this infrastructure, yet their deployment is severely bottlenecked by extreme operational costs, massive data processing payloads, and rapid environmental variations across vast highway networks. To address these challenges, this paper proposes a comprehensive, localized national strategy structured around three key tasks. First, it establishes a unified national HD map standard to guarantee seamless interoperability and data sharing among competing AV manufacturers and government transport authorities. Second, it implements an AI-powered baseline workflow using Mobile Mapping Systems (MMS) for high-fidelity static map construction, anchored and validated within designated pilot zones, including the King Abdulaziz University campus and key sectors in the Kingdom. Third, it deploys a decentralized, vision-based crowdsourcing system that leverages active public and commercial vehicle fleets for real-time map maintenance. By integrating a sovereign edge-cloud AI infrastructure that respects local Personal Data Protection Law (PDPL), this framework bridges the gap between high-accuracy baseline mapping and long-term economic sustainability, offering an actionable technical roadmap for scaling a resilient digital transport layer across the Kingdom. Full article

Decarbonizing heavy-duty road transport requires comparing zero-emission options to guide infrastructure investments along strategic corridors. This study develops a scenario-based techno-economic model to evaluate hydrogen fuel cell trucks (HFCTs) and battery electric trucks supported by dynamic wireless power transfer (DWPT) on a 100 km segment of Italy’s A4 motorway in 2030 and 2050 scenarios. The framework integrates traffic flows, vehicle archetypes, infrastructure sizing, and end-to-end energy chains (power-to-hydrogen-to-wheel for hydrogen and grid-to-wheel for WPT) to estimate capital and operating costs, efficiencies, and energy demand. Results show that hydrogen refueling infrastructure requires lower initial investment (approximately €60 million CAPEX and €20 million annual OPEX) than wireless charging systems (€80 million CAPEX and €15 million OPEX). However, WPT achieves significantly higher grid-to-wheel efficiency (96% vs. 62%) and lower per-vehicle energy demand (18 MWh/year vs. 25 MWh/year). These findings highlight a fundamental trade-off: hydrogen solutions offer operational flexibility and are better suited to long-haul or low-density contexts, while WPT systems are more efficient and become increasingly competitive in high-traffic corridors with high infrastructure utilization. Overall, the results suggest that no single technology universally dominates and that optimal deployment depends on traffic density, infrastructure usage, and system integration. A combined implementation of hydrogen and wireless charging technologies may provide the most effective pathway to balance efficiency, flexibility, and cost in future heavy-duty transport systems. Full article

Advanced Driver Assistance Systems (ADAS) have significant potential to improve road safety. However, drivers’ perceptions and acceptance of these systems in Thailand have not been explored. This study investigated Thai drivers’ perceptions towards ADAS and examined factors associated with trust and intention to use. A cross-sectional survey was conducted with 849 licenced drivers. The questionnaire measured perceived usefulness, perceived ease of use, trust, barriers and concerns, expectations and preferences, and intention to use ADAS. Data were analyzed using Mann–Whitney U tests, Spearman’s rank correlations, and multiple linear regression. Results indicated that Thai drivers reported positive perceptions of ADAS regarding perceived usefulness, expectations, preferences, and intention to use. Trust was most strongly associated with constructs such as perceived usefulness, perceived ease of use, and intention to use. Multiple regression identified perceived usefulness, trust, and expectations and preferences as significant positive predictors of intention to use ADAS, whereas barriers and concerns were negatively associated with intention to use. Perceived ease of use was not a significant predictor. These findings highlight the importance of perceived usefulness, trust, and user expectations in shaping intention to use ADAS and support the need for new policies regarding driver education and awareness initiatives in Thailand. Full article

Medium-sized cities in the Global South are at the center of future urban growth, yet their transportation systems remain dominated by car-dependent trajectories. This paper examines how urban infrastructure shapes inclusive access to active commuting using a latent class model across three Mexican cities. We identify two distinct commuter environments defined by infrastructure quality. In low-infrastructure settings, active commuting is concentrated among younger men, consistent with existing literature. In contrast, in high-infrastructure environments, the baseline probability of active commuting is nearly three times higher, so that women and older individuals commute actively at substantially higher absolute rates even though demographic penalties remain present in both environments. Attitudinal variables, often emphasized in policy discourse, are not significant predictors of mode choice. These findings suggest that infrastructure investment is not only a tool for increasing active commuting rates but also a mechanism for expanding mobility access across demographic groups. For rapidly growing medium-sized cities, prioritizing non-motorized infrastructure can play a central role in building inclusive, low-carbon transportation systems. Full article

In heavy-haul railway systems, effective empty railcar distribution (ERD) can optimize composition planning and meet empty railcar requirements (ERRs) at all loading ends, thereby improving the efficiency of train operations. To solve practical challenges such as the imbalanced supply–demand of empty trains, redundant loading and unloading cycles, and prolonged waiting times, this study establishes a multi-objective and 0–1 integer programming model for ERD at the loading end of a heavy-haul railway. The model can simultaneously maximize the fulfilment of all ERRs, minimize the ERD delay time, and reduce the waiting time in the heavy-train combination problem under complex constraints, including the passing capacity of sections, combination capacity of stations, and ERR at the loading end. While traditional optimization methods such as mathematical programming or heuristic algorithms partially address these issues, they are ineffective under dynamic constraints and state-space explosion. Furthermore, traditional reinforcement learning-based methods, such as Q-learning, exhibit limitations in railway scheduling due to the state-space explosion problem and inadequate model generalization. To overcome these limitations, this study proposes an innovative framework; the ERD at the loading end of the heavy-haul railway is formalized as a Markov decision process and optimized using deep Q-network (DQN) reinforcement learning. In addition, this study proposes an experience data fusion mechanism that integrates the empirical rules of the dispatchers through a modular architecture, achieving real-time constraint compliance while maintaining scalability for practical implementation. The NSGA-II genetic algorithm for multi-objective problems is used in this study to evaluate the performance of the DQN algorithm. The experimental results demonstrate that the DQN algorithm can fully meet ERRs with zero delay and produce optimal schemes for train combinations. Meanwhile, NSGA-II presents superior performance in minimizing the combination waiting time and same-destination train combinations. Meanwhile, the DQN algorithm can identify superior ERD strategies in the expanded-action and state spaces, enabling the effective handling of complex constraint-based ERD. Full article

Empty-vehicle repositioning is a persistent challenge in long-haul road freight because carriers must reduce empty mileage without sacrificing service reliability under lead times, appointment windows, and uncertain load realization. This paper formulates empty-vehicle repositioning on freight corridors as a stochastic control problem with explicit space–time feasibility and a stated within-epoch event order. Lead times couple current dispatch decisions to future capacity, pickup windows impose reachability constraints, and stochastic match feasibility captures information and market frictions. We develop dynamic lower bounds from time-expanded relaxations, showing that dual prices of inventory-balance constraints can be interpreted as space–time scarcity values. We further introduce an order-dependent nested friction decomposition that separates excess empty movement into spatial imbalance, temporal mismatch induced by lead times and time windows, and information frictions. Guided by this structure, we propose price-guided rolling-horizon and generalized-cost policies and evaluate them on synthetic corridor experiments organized around the three friction families. The results reveal service–empty-mileage trade-offs, a pronounced knee in the Pareto frontier, lower service loss under widened tight pickup windows, and strong sensitivity to match feasibility. The PG-RH policy reduces empty-distance exposure and total cost relative to static balancing in the main scenarios while maintaining comparable, but not uniformly dominant, service performance. The framework provides a diagnostic basis for identifying the sources of deadhead and for designing operational interventions that reduce empty mileage without undermining reliability. Full article

Effective traffic management relies on the availability of high-quality traffic data to support real-time decision-making for optimizing traffic flow, enhancing safety, and reducing environmental impacts. This study aims to address the lack of integrated and operational approaches for traffic data quality management by proposing a scalable and adaptable framework for the systematic assessment and enhancement of traffic data. The framework consists of four interconnected layers, including data ingestion, data quality assessment, data imputation and correction, and a real-time alerting mechanism. Its applicability is demonstrated through a real-world case study on traffic signal control plan selection, using sensitivity and simulation-based analyses in SUMO. The results indicate that degraded data quality, particularly due to missing or invalid records, can significantly affect system behavior, leading to suboptimal decisions and reduced traffic performance. These findings highlight the importance of continuous and systematic data quality monitoring as a critical component for reliable and efficient traffic management systems. Full article

Railway transport is increasingly recognized as a key pillar of sustainable mobility, offering a low-carbon and energy-efficient alternative to road and air transport and playing a critical role in achieving climate objectives, regional connectivity, and sustainable tourism development. Despite extensive research on service quality, sustainability, and tourism, their interrelationship within the railway sector remains insufficiently explored. This study aims to systematically analyze the intersection of quality management systems (QMS), sustainability, and tourism in passenger railway transport and to identify structural gaps that hinder their integration. A systematic literature review was conducted following the PRISMA methodology, resulting in a final sample of 37 studies. The findings reveal a significant research gap, particularly the absence of integrated and empirically supported QMS frameworks linking passenger satisfaction with sustainability and tourism objectives. Quality-management-oriented constructs appear in 48.6% of the analyzed studies, sustainability in 32.4%, and tourism in 24.3%, while none demonstrate full integration of all three dimensions. The study contributes by providing a conceptual basis for future research on the integration of operational quality management, environmental performance, and passenger-oriented service quality in railway systems. Full article

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