Social Sciences

Abstract: Transportation planning in regions with insufficient public transit often relies on observed travel volumes, overlooking unmanifested latent demand. This study proposes a method to estimate latent travel demand (LTD) within a few kilometers of residential areas using open data—population distribution, aggregated activity data, and facility locations—and investigates its validity and regional variation across three Japanese prefectures (Hiroshima, Ibaraki, and Iwate). Validity was assessed by examining the relationship between estimated LTD and observed travel volumes (apparent traffic volume, ATV) derived from smartphone GPS data. Three key findings emerged. First, LTD, combined with facility count, transit availability, and distance, explained observed travel volumes with R2 = 0.61–0.63 on the original scale and R2 > 0.91 on the log–log scale, and likelihood ratio tests confirmed that LTD provides information complementary to, and distinct from, conventional accessibility indicators. Second, regional fixed-effects analysis revealed that while LTD is a significant predictor across all regions, the demand realization rate (ATV/LTD) varies systematically—from 0.021 in transit-rich Hiroshima to 0.045 in car-dependent Iwate—reflecting differences in transit infrastructure and car dependency. The interaction between LTD and transit availability was statistically significant (p = 0.008), with moderate-transit areas showing the largest marginal effect of LTD on realized travel. Third, activity-type analysis showed that mandatory activities (e.g., medical visits, childcare) maintain stable demand realization regardless of transit levels, while discretionary activities (e.g., hobbies, leisure) are strongly transit-dependent. These results demonstrate that LTD estimation offers a practical, scalable diagnostic tool for identifying mobility gaps and prioritizing transit investments in resource-constrained municipalities.

Abstract: Scholarship has noted the ambiguity of new transport modes, such as e-scooters or cargo bikes, that often struggle to fit in within mobility systems organised around established categories of pedestrians, cyclists and motorists. However, this is not a new phenomenon: Dutch mopeds have been persistently framed as ‘not fitting in’ since the 1950s, despite regulatory and technological changes. Drawing on STS and mobilities studies, this paper conceptualises this condition as residuality, to examine how mobility systems position certain modes and practices at the margins of dominant transport categories. The study asks: How has the residuality of mopeds persisted over time and what does this say about mobility systems? We analyse readers’ letters to Dutch newspapers (1950-1975 and 2010-2024) alongside policy documents. Findings indicate that residuality is shaped by institutional, material and cultural elements. Regulatory adaptation around mopeds has not eliminated residuality but contributed to its reconfiguration within the mobility system, displacing it towards fatbikes. Our article contributes to mobilities studies by introducing residuality as a framework for understanding how mobility systems produce and stabilise mobilities that ‘do not fit’. It also shows the value of a long-term perspective to understand contemporary challenges around (micro)mobility modes.

Abstract: Digitalization and sustainability have become key priorities in the development of container terminals. However, existing research has largely examined smart port technologies and sustainability initiatives separately, while empirical evidence re-garding their integration in medium-sized container terminals remains limited. This study investigates the relationship between digitalization and sustainability in con-tainer terminal operations through a qualitative case study conducted at a medi-um-sized container terminal in the Port of Klaipėda, Lithuania. Data were collected through document analysis and semi-structured interviews with employees involved in terminal operations, planning activities, and digitalization and sustainability initia-tives. Qualitative content analysis was applied to identify key themes related to tech-nological implementation, operational efficiency, environmental performance, and organizational challenges. The findings indicate that digital technologies, including the Terminal Operating System, Vehicle Booking System, automated gate solutions, and the Port Community System, contribute to improved operational efficiency, enhanced information exchange, reduced congestion, and more effective resource utilization. Sustainability initiatives, including equipment electrification, renewable energy adop-tion, and energy-efficient infrastructure, support reductions in energy consumption and environmental impacts. Nevertheless, challenges related to system interoperabil-ity, fragmented information flows, organizational adaptation, and employee compe-tencies continue to limit the full realization of these benefits. The results demonstrate that digitalization and sustainability should be viewed as interconnected dimensions of container terminal development rather than independent strategic objectives. The study contributes empirical evidence from a medium-sized Baltic Sea container ter-minal and provides practical insights for ports seeking to advance digital and sustain-able transformation simultaneously.

Abstract: While electric vehicles can reduce the environmental impact of CO2, the extent of this effect depends on the growth of the EV market. Growth models, such as the logistic or Gompertz function models, can predict expected EV sales trends. How well these functions predict EV sales has not yet been comprehensively analyzed. To do so, it would be necessary to look into the future to compare today’s predictions with future data. Since this is not possible, this study took a retrograde approach. It went back in time to use the historical data available then to create forecasts that were then compared with the actual values of subsequent years. For example, a forecast based on data from 2010 to 2014 can be compared with the values achieved in subsequent years from 2015 to 2025. The quality of the functions was assessed using fit indices. When comparing 10 different models, the Gompertz function was found to be the most suitable for predicting the EV market.

Abstract: Electric micromobility may reduce transport emissions, but its system-level benefits depend on which journeys and vehicles it replaces. This study assesses e-bike, e-scooter and e-cargo-bike pathways in an electrifying UK transport system using an expanded Transport Energy Air pollution Model (TEAM-UK). Socio-technical scenarios are modelled annually to 2050, estimating travel activity, energy demand, direct CO₂ and NO_X emissions, life-cycle greenhouse gas emissions and local air pollutants, including non-exhaust sources. Fleet electrification dominates long-run reductions in tailpipe CO₂ and NO_X. However, e-bike-led pathways deliver the largest additional reductions in car-kilometres, energy demand, cumulative life-cycle emissions and non-exhaust PM2.5. E-cargo bike pathways provide more targeted benefits, while e-scooter pathways produce modest system-level gains. Electric micromobility is most effective when embedded in policies that substitute car and van travel with lighter, low-energy modes.

Abstract: Advanced Driver Assistance Systems (ADAS) have demonstrated safety potential and are becoming increasingly available in the vehicle markets across the world. However, drivers’ perceptions, trust, and engagement with these systems in Thailand remain unexplored. This study therefore aimed to explore Thai drivers’ perceptions towards ADAS and investigated factors associated with trust and intention to use. A cross-sectional survey was conducted with 849 licensed drivers in Thailand. The online survey 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 and Spearman’s rank correlations. Results showed that Thai drivers reported positive perceptions of usefulness and intention to use ADAS, while trust was moderate, and barriers and concerns showed variability. Trust demonstrated strong positive associations with perceived usefulness (ρ = .69), perceived ease of use (ρ = .56), and intention to use (ρ = .49). The findings highlight the important role of perceived usefulness, perceived ease of use, and trust in shaping drivers’ intent to use the system and supports the development of learning strategies to enhance ADAS usage whilst promoting utilization of these systems.

Abstract: Environmental governance fundamentally depends on effective collaboration among multiple stakeholders. Classical wicked problem theory maintains that corporate environmental engagement must be grounded in intrinsic motivation to yield genuinely collaborative outcomes; however, this demanding premise has substantially constrained the participation of firms in environmental governance. Addressing this limitation, the present study concentrates on China’s logistics industry—an energy-intensive sector with significant emissions—and constructs a tripartite evolutionary game model encompassing government authorities, logistics enterprises, and the public. The model systematically investigates the effects of three distinct reputation mechanisms—institutional incentives, market-based feedback, and social supervision—on corporate green behavioral choices. The results demonstrate that: (1) logistics enterprises exhibit stronger behavioral responsiveness to market-based reputational feedback than to institutionally incentivized reputation mechanisms; (2) under conditions of weak government regulation or limited public participation, institutional incentive–based reputation mechanisms play a critical role in promoting the adoption of green practices; (3) as government regulation and public participation simultaneously intensify, the marginal effectiveness of institutional incentive–based reputation mechanisms diminishes; and (4) social supervision–based reputation exerts a significant positive effect on corporate green behavior only when complemented by institutional incentive mechanisms. Overall, the findings indicate that reputation-based governance can facilitate behavioral transformation among reactive logistics enterprises confronting wicked environmental challenges in China. Accordingly, this study proposes policy implications emphasizing the enhancement of public participation and the strengthening of governmental governance capacity to maximize the effectiveness of reputational mechanisms.

Abstract: This paper discusses the sustainability of Urban Air Mobility (UAM) in smart cities across four dimensions: environmental, social, economic, and operational impacts. In the long term, UAM aircraft are expected to be autonomous and unmanned, however, there some UAM aircraft will have pilots in the immediate future. Economic factors reflect the financial viability of UAM, the business case for operations, public impacts from subsidies for vertiport and power infrastructure, and potential indirect costs from increased electricity demand and grid upgrades. Environmental impacts include energy use, emissions, and noise. Social considerations include vertiport siting, public acceptance, employment effects, land use changes, and distributional equity. Operational sustainability encompasses technical readiness, regulatory conditions, and UAM missions such as cargo delivery, passenger transport and emergency response. Using existing literature and case studies from U.S. cities to provide a summary of relevant topics, we analyze a UAM business case framework and estimate travel time savings for airport-to-downtown trips in Dallas and New York. We compare UAM energy intensity and emissions versus conventional transportation modes using a New York City application, and examine how vertiport siting impacts travel times, land use, and neighborhood noise. Operational considerations highlight early use cases most likely to deliver near-term benefits. We conclude with a research agenda to address gaps and guide sustainable UAM deployment.

Abstract: 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 1970’s, 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.

Abstract: E-cargo bikes are niche mode amongst domestic users, with strong potential to reduce car dependence, particularly in suburban areas where usage is growing. However, there is a lack of research on both domestic e-cargo bike use and suburban use. Policy makers lack basic metrics for average speed, trip distance, as well as more detailed analysis of routes taken and the types of roads / paths used. Using GPS data from trackers on 12 household e-cargo bikes (7150km travelled, 1750 trips in Leeds, Brighton, Oxford), we, calculate key metrics and information about the types of routes used. Average speeds per trip are 11.8km/ hr, mean trip length 4.6km. Speeds vary with route type. Domestic e-cargo bikes are largely unhindered by hills. Major roads are used where cycle infrastructure is lacking (Leeds 48% of km travelled). Cycle infrastructure is used where present and suitable quality (Oxford 37% of km travelled).

Abstract: This paper reports on an online survey of 2,000 English adults, designed to inform the debate about the potential for wider adoption of e-micromobility modes, such as e-bikes, e-cargo bikes and e-scooters. It shows that, by 2023, take-up was already greater than for electric cars, with 11% of households owning at least one of those vehicles and 9% of adults using one at least once a month. On average, users were more likely to be male, young, well-educated urban dwellers, but findings also suggested relatively high take-up by people with children, greater appeal to women than conventional cycling, and the potential to appeal to a wider range of age groups over time. Use of e-micromobility was associated with more varied mobility strategies, and lower levels of frequent car use. Over 50% of adults were interested in trying out vehicles, and evidence from other UK trials and existing users suggests that being able to trial vehicles may be key for purchase decisions. On balance, non-users were broadly positive (or neutral) towards these modes, though with particular concerns arising around the safety of e-scooters and their relationship with pedestrians. Cost, fear of theft, difficulties with storage and parking, unsafe road environments and lack of confidence cycling all emerged as key barriers. Users of e-micromobility were less likely to be sedentary and more likely to be meeting physical activity targets than non-users, highlighting important synergies with other active travel modes (i.e. walking and cycling), but any measures to increase uptake need to find ways to ensure that different active travel modes can safely coexist.

Abstract: Urban transportation is a crucial aspect of modern societal development, with bus route optimization playing a central role in urban transit planning. Well-designed bus routes can enhance the efficiency and attractiveness of public transportation, alleviate traffic congestion and pollution, and ultimately contribute to the overall growth of a city. This study investigates the selection of bus stop locations and route optimization from three perspectives: population density, facility distribution, and route length. Firstly, a scheme for optimizing bus stop locations is proposed based on population grid data, Points of Interest (POI), and road network data. Next, candidate points are generated using the road network, and a new heuristic algorithm is introduced to initially establish optimized routes. A nondominated sorting algorithm is then employed to identify the optimal solution set, balancing population coverage, facility accessibility, and route distance. The proposed method for bus stop location and route optimization is universally applicable to urban bus routes and can be validated through case studies in different cities. Finally, an empirical analysis is conducted using Route 119 in Kunming City, Yunnan Province, as a case study. Compared with the original bus route, the optimized route demonstrates improvements of 18.26% in route distance, 15.79% in Points of Interest (POI) accessibility, and 10.53% in population coverage.

Abstract: Technological advances such as Vehicle-to-Grid (V2G) have the potential to support renewable energy integration and grid stability, but their large-scale deployment depends on user participation, particularly in public charging environments. While prior research has examined V2G from technical and system-level perspectives, limited attention has been given to users’ perspectives on participating in V2G services outside the home. This study investigates electric vehicle (EV) users’ willingness to engage in V2G services in public spaces, with a focus on incentives, expectations, and everyday routines. A mixed-method approach was applied, combining a survey of 544 car users with two waves of user-centered interviews. The survey data were analyzed using factor analysis and linear regression models, while the interview data were thematically analyzed. The results show that users’ evaluations of V2G are shaped by sustainability expectations, perceived efficiency, and uncertainties, and that preferences for public V2G participation are strongly influenced by convenience, clarity of the offer, and existing parking and charging conditions. Economic incentives, such as reduced charging or parking costs, were generally preferred over more complex reward models, while concerns related to control over trips, battery degradation, and trust in service providers remain important barriers. The findings highlight the need for user-centered design of public V2G services that align with users’ routines, preferences, and expectations to improve user acceptance.

Abstract: While pricing policy has emerged as a critical demand-side lever for decarbonizing mobility, its bidirectional effects on modal shift remain unexplored. Dynamic pricing in high-speed rail (HSR) creates a double-edged environmental outcome: advance discounts attract passengers from aviation, yet last-minute premiums may reverse these gains. Using 2.4 million price observations from Madrid--Barcelona, we introduce a carbon leakage framework that quantifies this phenomenon for the first time. Our analysis reveals a structural tension: while early-bird pricing attracts 274,431 annual passengers from aviation---saving 23,650 tonnes CO2/year---last-minute scarcity premiums systematically drive passengers back to air travel. Literature-calibrated elasticity estimation (ε = −0.95) shows that 22.3% of last-minute tickets exceed the \euro{}120 aviation threshold, creating 1,511 tonnes CO2 leakage annually (6.4% offset of gross savings). Critically, scenario analysis demonstrates that price caps at \euro{}110--120 eliminate leakage entirely while preserving 94% of operator revenue. These findings establish actionable thresholds for carbon-aware revenue management, demonstrating that demand-side decarbonization requires not only attracting passengers to sustainable modes but also preventing their reversal to high-carbon alternatives.

Abstract: The use of light electric vehicles, such as e-bikes and e-scooters, is increasingly being adopted as a sustainable transport solution in urban areas. This is driven by the need for cleaner, faster, and space-efficient mobility solutions in urban areas. Although research on LEVs has grown over time, it remains fragmented across disciplines, creating a need for an integrated study on how LEVs contribute to sustainable transport in urban areas. This study conducted a bibliometric review to identify key themes in light electric vehicles and sustainable transport in urban areas and proposed future research agendas based on the conceptual patterns and research gaps. It utilised the Scopus database, focusing on the 552 publications from 2000 to 2025 retrieved on 30 September 2025. The Biblioshiny application (version 5.0) was used to perform bibliometric performance analysis and science mapping techniques. Results revealed that the publication trend steadily rose from 2015, with a significant upsurge after 2020, with an annual growth rate of 18.69%. Three dominant themes were identified: sustainability, integration with public transport, and technological innovations, alongside underexplored areas such as shared e-micromobility, freight delivery, as well as policy and governance. Future research should capture full lifecycle impacts, expand access to light electric vehicles beyond current user groups, and align rapid technological advances with inclusive governance frameworks.

Abstract: To deeply explore the mechanism of consumers' electric vehicle (EV) purchase behavior and address research gaps related to insufficient consideration of psychological latent variables and neglect of consumer heterogeneity in existing studies, this study constructs a latent class model (LCM) that integrates personal attributes, vehicle attributes, and six psychological latent variables: perceived usefulness, perceived ease of use, perceived risk, environmental awareness, purchase attitude, and purchase intention. Based on 1,044 valid questionnaires collected from Urumqi, latent profile analysis (LPA) is used to classify consumers. The results indicate that EV consumers can be divided into five distinct latent profiles with significant differences in purchase preferences: risk-avoidance type, moderate-low intention wait-and-see type, utility-oriented and low environmental concern type, high utility cognition and low risk proactive type, and all-dimensional high-intention core type. Socioeconomic and vehicle-related factors exert heterogeneous impacts on the psychological variables and purchase decisions of each profile. This study clarifies the intrinsic psychological mechanism of EV purchase behavior, providing a theoretical basis and targeted strategy references for the government and enterprises to promote EV adoption and advance sustainable transportation development.

Abstract: Spatial mobility is vital for the well-being of older adults. Lack of adequate transportation can limit their access to healthcare, services, and social opportunities. For older adults who do not or no longer drive, receiving rides is their primary mode of transportation, but this reliance can burden their ride providers. Measuring and assessing the geospatial burden of providing rides is important for research and policies that aim to address the impact on ride providers and older adults’ unmet travel needs. In this manuscript, we propose an approach that collects data to assess ride providers’ geospatial activity spaces for their own routine activities and for providing rides. By comparing the two activities spaces, we suggest a possible method to operationalize geospatial ride-providing burden, using three potential burden indicators. Using data from a pilot study (N = 18 ride providers), we apply these burden indicators and correlate them to other indicators of burden (i.e., days/month giving rides and monetary costs, missed work, increased per-sonal stress). We conclude that the percentage of the ride-provision activity space that is outside the ride provider's regular activity space may be a useful indicator of geospatial burden of providing rides.

Abstract: To address the systemic issues of emergency medical resource allocation under multi-hazard coupling, this study constructed a hybrid rescue model combining fixed medical facilities and mobile rescue stations. Mixed-integer programming (MIP) was used to achieve three-dimensional optimization of “resource allocation-facility location-casualty transport,” with the objective function being to minimize the total rescue cost (including casualty transport time and waiting penalty costs). The uncertainty in disaster evolution is characterized using a scenario-based random demand representation method. Given the NP-hard nature of the model, PSO and VNS algorithms are designed to enhance solution efficiency through dynamic inertia weight adjustment and multi-modal neighborhood structure. Experimental validation confirms the effectiveness of the model and algorithms, providing practical insights for emergency management.

Abstract: Efficient last-mile delivery remains a major challenge for agriculture in rural regions such as the state of North Dakota in the United States. In these regions, farms are large, dispersed, and dependent on timely access to inputs. Truck–drone hybrid systems offer a potential solution by combining the long-haul capacity of trucks with the speed and flexibility of drones. Economic studies indicate that such proposed hybrid systems can enable faster, lower-cost, and more sustainable delivery of small, time-critical packages. This research further reviews the role of geographic information systems (GIS) in enabling these systems. A combined systematic and thematic review of 82 high-quality publications identifies five domains: GIS applications, truck–drone coordination, smart agriculture integration, rural implementation, and sustainability impacts. The findings show that GIS supports route optimization, drone launch-site selection, and real-time monitoring. Beyond the capacity of drones to extend reach and reduce delays, integrating IoT and AI platforms enhances decision-making and improves efficiency. However, constraints include federal regulations, payload limits, harsh weather (especially in rural areas), and cybersecurity risks. This review concludes that GIS-enabled truck–drone systems can transform agricultural logistics and rural resilience if providers can address regulatory, technical, and security challenges through coordinated innovation.

Abstract: Background: The maritime industry is experiencing a dual transformation driven by decarbonization imperatives and Industry 4.0 digitalization. Green Digital Shipping Corridors (GDSCs) is one of the initiatives that integrate zero-emission technologies to achieve shipping decarbonization. GDSCs utilize advanced digital systems and cross-sector collaboration to enable sustainable, efficient, and resilient green maritime transport. While technological architectures for GDSCs are well studied, the operational readiness of human actors—particularly seafarers and shore-based personnel—remains underexplored. Methods: This study adopts a layered, iterative methodology combining a systematic literature review, industry reports, and expert interviews. Strategic analyses were conducted using McKinsey’s 12 Elements of a Dynamic Operating Model and an upgraded Technology Readiness Level–Human Readiness Level (TRL–HRL) matrix. A five-layer Industry 4.0 architecture tailored to GDSCs was developed, alongside a comparative analysis of traditional and Industry 4.0-enabled maritime systems. A competency mapping framework was designed, aligned with STCW standards, and linked to a KPI-based evaluation and phased implementation roadmap. Results: The findings reveal significant gaps between technology maturity and human readiness, particularly in AI explainability, cognitive load compatibility, and multi-agent coordination. The proposed framework bridges traditional maritime skills with AI-enabled operations, emphasizing human–technology synergy, cybersecurity, sustainability competence, and adaptive training. Conclusion: Aligning technological deployment with structured human-factor readiness strategies is essential to realize the full potential of GDSCs. The integration of competency-based training, human-on-the-loop decision protocols, and continuous feedback mechanisms mitigates operational risks, enhances safety, and accelerates sustainable shipping transformation. The proposed model provides a replicable pathway for policymakers, training institutions, and shipping companies to implement AI-augmented GDSCs effectively.