Search Results (268)

Urban population growth is intensifying pressure on mobility systems, with road transportation contributing to environmental and sustainability challenges. Policymakers must navigate complex uncertainties in addressing rising mobility demand while pursuing sustainability goals. Advanced technologies offer promise, but their real-world effectiveness in urban contexts remains underexplored. This meta-review comprised three complementary studies: a broad analysis of sustainable mobility with Norwegian case studies, and systematic literature reviews on digital twins and Big Data/AI applications in urban mobility, covering the period of 2019–2024. Using structured criteria, we synthesised findings from 72 relevant articles to identify major trends, limitations, and opportunities. The findings show that mobility policies often prioritise technocentric solutions that unintentionally hinder sustainability goals. Digital twins show potential for traffic simulation, urban planning, and public engagement, while machine learning techniques support traffic forecasting and multimodal integration. However, persistent challenges include data interoperability, model validation, and insufficient stakeholder engagement. We identify a hierarchy of mobility modes where public transit and active mobility outperform private vehicles in sustainability and user satisfaction. Integrating electrification and automation and sharing models with data-informed governance can enhance urban liveability. We propose actionable pathways leveraging Big Data and AI, outlining the roles of various stakeholders in advancing sustainable urban mobility futures. Full article

This study investigates the factors influencing school trip mode choice among senior high school students in the Chiang Rai urban area, Chiang Rai, Thailand, under normal and adverse weather conditions. Utilizing data from 472 students across six extra-large urban schools, a Multinomial Logit (MNL) regression model was applied to examine the effects of socio-demographic attributes, household vehicle ownership, travel distance, and spatial variables on mode selection. The results revealed notable modal shifts during adverse weather, with motorcycle usage decreasing and private vehicle reliance increasing, while school bus usage remained stable, highlighting its role as a resilient transport option. Car ownership emerged as a strong enabler of modal flexibility, whereas students with limited access to private transport demonstrated reduced adaptability. Additionally, increased waiting and travel times during adverse conditions underscored infrastructure and service vulnerabilities, particularly for mid-distance travelers. The findings suggest an urgent need for transport policies that promote inclusive and climate-resilient mobility systems, particularly in the context of Chiang Rai, including expanded school bus services, improved first-mile connectivity, and enhanced pedestrian infrastructure. This study contributes to the literature by addressing environmental variability in school travel behavior and offers actionable insights for sustainable transport planning in secondary cities and border regions. Full article

Urban areas are facing increasing traffic congestion, pollution, and infrastructure strain. Traditional urban transportation systems are often fragmented. They require users to plan, pay, and travel across multiple disconnected services. Mobility-as-a-Service (MaaS) integrates these services into a single digital platform, simplifying access and improving the user experience. This review critically examines the role of MaaS in fostering sustainable mobility ecosystems. MaaS aims to enhance user-friendliness, service variety, and sustainability by adopting a customer-centric approach to transportation. The findings reveal that successful MaaS systems consistently align with multimodal transport infrastructure, equitable access policies, and strong public-private partnerships. MaaS enhances the management of routes and traffic, effectively mitigating delays and congestion while concurrently reducing energy consumption and fuel usage. In this study, the authors examine MaaS as a new mobility paradigm for a sustainable transportation system in smart cities, observing the challenges and opportunities associated with its implementation. To assess the environmental impact, a sustainability index is calculated based on the use of different modes of transportation. Significant findings indicate that MaaS systems are proliferating in both quantity and complexity, increasingly integrating capabilities such as real-time multimodal planning, dynamic pricing, and personalized user profiles. Full article

The Barcelona Metropolitan Area (AMB) has implemented various policies to reduce car use and promote more sustainable mobility. Initiatives such as superblocks, Low Emission Zones (LEZs), and the Bicivia network aim to transform the urban model in response to environmental and congestion challenges. However, the high reliance on private vehicles for intermunicipal travel, uneven infrastructure, and social resistance to certain changes remain significant issues. This study examines the evolution of mobility patterns and assesses the effectiveness of the above policies in fostering real and sustainable change. A mixed-methods approach was adopted, which combined an exploratory factor analysis (EFA) of 2011–2024 data, trend linear regression, and a comparative international analysis. The EFA identified four key structural dimensions: traditional transport infrastructure, active mobility and bus lines, public bicycles and mixed use, and transport efficiency and punctuality. The findings reveal a clear reduction in private car use and an increase in sustainable modes of transport. This indicates that there are prospects for future transformation. Nonetheless, challenges persist in intermunicipal mobility and the public acceptance of the measures. This study provides empirical and comparative evidence and emphasizes the need for integrated metropolitan governance to achieve a resilient and sustainable urban model. Full article

Generation Z (Gen Z; born 1997–2012) is reshaping global tourism through digital fluency, ethical awareness, and a desire for authentic, sustainable travel experiences. This study surveys 413 Gen Z travelers across France, Portugal, Romania, and the USA to map their booking behaviors, information sources, transport modes, accommodations, dining practices, and leisure activities. The findings reveal a strong preference for independent online bookings and social-media-influenced destination choices (Instagram, TikTok), with air and car travel being used for long-distance journeys and walking/public transit being used for local journeys. Accommodation spans commercial hotels and private rentals, while informal, local dining and nature- or culture-centered leisure prevail. Chi-square tests were performed to identify differences between countries. To reveal distinct traveler segments and their country’s modulations towards sustainability, a hierarchical cluster analysis was performed. The results uncover four segments: “Tech-Active, Nature-Oriented Minimalists” (32.3% in France); “Moderate Digital Planners” (most frequent across all countries, particularly dominant among Romanian respondents); “Disengaged and Indecisive Travelers” (overrepresented in the USA); and “Culturally Inclined, Selective Sustainability Seekers” (>30% in France/Portugal). Although sustainability is widely valued, only some segments of the studied population consistently act on these values. The results suggest that engaging Gen Z requires targeted, value-driven digital strategies that align platform design with the cohort’s diverse sustainability commitments. Full article

With the advent of Autonomous Vehicles (AV) technology, extensive research around the design of on-demand mobility systems powered by such vehicles is performed. An important part of these studies consists in the evaluation of the economic impact of such systems for involved stakeholders. In this work, a cost–benefit analysis (CBA) is applied to the introduction of AV services in Paris-Saclay, an intercommunity, south of Paris, simulated through MATSim, an agent-based model capable of capturing complex travel behaviors and dynamic traffic interactions. AVs would be implemented as a feeder service, first- and last-mile service to public transit, allowing intermodal trips for travelers. The system is designed to target the challenges of public transport accessibility in periurban areas and high private car use, which the AV feeder service is designed to mitigate. To our knowledge, this study is one of the first CBA analyses of an intermodal AV system relying on an agent-based simulation. The introduction of AV in a periurban environment would generate more pressure on the road network (0.8% to 1.7% increase in VKT for all modes, and significant congestion around train stations) but would improve traveler utilities. The utility gains from the new AV users benefiting from a more comfortable mode offsets the longer travel times from private car users. A Stop-Based routing service generates less congestion than a Door-to-Door routing service, but the access/egress time counterbalances this gain. Finally, in a periurban environment where on-demand AV feeder service would be added to reduce the access and egress cost of public transit, the social impact would be nuanced for travelers (over 99% of gains captured by the 10% of most benefiting agents), but externality would increase. This would benefit some travelers but would also involve additional congestion. In that case, a Stop-Based routing on a constrained network (e.g., existing bus network) significantly improves economic viability and reduces infrastructure costs and would be less impacting than a Door-to-Door service. Full article

This study investigates the factors affecting the choice of school travel mode among students in Mersin, Türkiye, focusing on walking, private car, public transit and school bus. A two-step modeling approach was adopted. First, a latent class cluster analysis (LCCA) was applied to identify subgroups of students with similar characteristics. Then, separate multinomial logit (MNL) models were estimated for each cluster. The data come from the 2022 Urban Transport Master Plan household survey and include 2798 students from 2092 households. The results show that trip distance is the most consistent and significant factor across all clusters, as increasing distance makes students more likely to use motorized modes instead of walking. Gender also demonstrates a consistent influence in specific clusters, where male students are less likely to travel by private car. Similarly, residing in a single-family house consistently increases the likelihood of car use in multiple clusters. Conversely, the influence of household structure, parental education, income, and household size differs significantly between clusters, underlining the importance of considering group-level differences in school travel behavior. These findings suggest that policies aiming to promote sustainable school travel should be sensitive to the needs of different student groups. Integrating land use and transportation planning may help to support active and shared modes of travel. Full article

Improper stakeholder practices are considered a primary driver of food loss. This study aims to investigate the consequences of pre- and post-harvest practices on extending the shelf life of agro-food products, identifying which practices yield the highest marginal returns for quality. Using Fractional Regression Models (FRM) and Ordinary Least Squares (OLS), the research analyzed data from 343 Egyptian grape farmers and intermediaries. Key findings at the farmer level include significant food loss reductions through drip irrigation (13.9%), avoiding maturity-accelerating chemicals (24%), increased farmer-cultivated area (6.1%), early morning harvesting (8.7%), and improved packing (13.7%), but delayed harvesting increased losses (21.6%). For intermediaries, longer distances to market increased losses by 0.15%, while using proper storage, marketing in the formal markets, and using an appropriate transportation mode reduced losses by 65.9%, 13.8%, and 7.9%, respectively. Furthermore, the interaction between these practices significantly reduced the share of losses. The study emphasizes the need for increased public–private partnerships in agro-food logistics and improved knowledge dissemination through agricultural extension services and agri-cooperatives to achieve sustainable food production and consumption. This framework ensures robust, policy-actionable insights into how stakeholders’ behaviors influence postharvest losses (PHL). The findings can inform policymakers and agribusiness managers in designing cost-efficient strategies for reducing PHL and promoting sustainable food systems. Full article

This study is titled “Success Factors in Transport Interventions: A Mixed-Method Systematic Review (1990–2022)”. The purpose of the systematic review is to (1) identify effective interventions for transitioning individuals from private car reliance to sustainable transport, (2) summarise psychosocial theories shaping transportation choices and identify enablers and barriers influencing sustainable mode adoption, and (3) determine the success factors for interventions promoting sustainable transport choices. The last search was conducted on 18 November 2022. Five databases (Scopus, Web of Science, MEDLINE, APA PsycInfo, and ProQuest) were searched using customised Boolean search strings. The identified papers were included or excluded based on the following criteria: (a) reported a modal shift from car users or cars to less CO₂-emitting modes of transport, (b) covered the adoption of low-carbon transport alternatives, (c) comprised interventions to promote sustainable transport, (d) assessed or measured the effectiveness of interventions, or (e) proposed behavioural models related to mode choice and/or psychosocial barriers or drivers for car/no-car use. The identified papers eligible for inclusion were critically appraised using Sirriyeh’s Quality Assessment Tool for Studies with Diverse Designs. Inter-rater reliability was assessed using Cohen’s Kappa to evaluate the risk of bias throughout the review process, and low-quality studies identified by the quality assessment were excluded to prevent sample bias. Qualitative data were extracted in a contextually relevant manner, preserving context and meaning to avoid the author’s bias of misinterpretation. Data were extracted using a form derived from the Joanna Briggs Institute. Data transformation and synthesis followed the recommendations of the Joanna Briggs Institution for mixed-method systematic reviews using a convergent integrated approach. Of the 7999 studies, 4 qualitative, 2 mixed-method, and 30 quantitative studies successfully passed all three screening cycles and were included in the review. Many of these studies focused on modelling individuals’ mode choice decisions from a psychological perspective. In contrast, case studies explored various transport interventions to enhance sustainability in densely populated areas. Nevertheless, the current systematic reviews do not show how individuals’ inner dispositions, such as acceptance, intention, or attitude, have evolved from before to after the implementation of schemes. Of the 11 integrated findings, 9 concerned enablers and barriers to an individual’s sustainable mode choice behaviour. In addition, two integrated findings emerged based on the effectiveness of the interventions. Although numerous interventions target public acceptance of sustainable transport, this systematic review reveals a critical knowledge gap regarding their longitudinal impact on individuals and effectiveness in influencing behavioural change. However, the study may be affected by language bias as it only included peer-reviewed articles published in English. Due to methodological heterogeneity across the studies, a meta-analysis was not feasible. Further high-quality research is needed to strengthen the evidence. This systematic review is self-funded and has been registered on the International Platform of Registered Systematic Review and Meta-analysis Protocols (INPLASY; Registration Number INPLASY202420011). Full article

Mitigating traffic-related carbon emissions is pivotal for achieving carbon peaking targets and advancing sustainable urban development. This study employs spatial autocorrelation and high-low clustering analyses to analyze the spatial correlation and clustering patterns of urban road traffic carbon emissions in Shaanxi Province. The spatiotemporal evolution and structural impacts of emissions are quantified through a systematic framework, while the GTWR (Geographically Weighted Temporal Regression) model uncovers the multidimensional and heterogeneous driving mechanisms underlying carbon emissions. Findings reveal that road traffic CO₂ emissions in Shaanxi exhibit an upward trajectory, with a temporal evolution marked by distinct phases: “stable growth—rapid increase—gradual decline”. Emission dynamics vary significantly across transport modes: private vehicles emerge as the primary emission source, taxi/motorcycle emissions remain relatively stable, and bus/electric vehicle emissions persist at low levels. Spatially, the province demonstrates a pronounced high-carbon spillover effect, with persistent high-value clusters concentrated in central Shaanxi and the northern region of Yan’an City, exhibiting spillover effects on adjacent urban areas. Notably, the spatial distribution of CO₂ emissions has evolved significantly: a relatively balanced pattern across cities in 2010 transitioned to a pronounced “M”-shaped gradient along the north–south axis by 2015, stabilizing by 2020. The central urban cluster (Yan’an, Tongchuan, Xianyang, Baoji) initially formed a secondary low-carbon core, which later integrated into the regional emission gradient. By focusing on the micro-level dynamics of urban road traffic and its internal structural complexities—while incorporating built environment factors such as network layout, travel behavior, and infrastructure endowments—this study contributes novel insights to the transportation carbon emission literature, offering a robust framework for regional emission mitigation strategies. Full article

The rapid evolution of the transport industry requires a deep understanding of user preferences for emerging mobility solutions, particularly carsharing (CS) and autonomous vehicles (AVs). This study employs machine learning techniques to model transport mode choice, with a focus on traffic safety perceptions of people towards CS and privately shared autonomous vehicles (PSAVs). A stated preference (SP) survey is conducted to collect data on travel behavior, incorporating key attributes such as trip time, trip cost, waiting and walking time, privacy, cybersecurity, and surveillance concerns. Sociodemographic factors, such as income, gender, education, employment status, and trip purpose, are also examined. Three gradient boosting models—CatBoost, XGBoost, and LightGBM are applied to classify user choices. The performance of models is evaluated using accuracy, precision, and F1-score. The XGBoost demonstrates the highest accuracy (77.174%) and effectively captures the complexity of mode choice behavior. The results indicate that CS users are easily classified, while PSAV users present greater classification challenges due to variations in safety perceptions and technological acceptance. From a traffic safety perspective, the results emphasize that companionship, comfort, privacy, cybersecurity, safety in using CS and PSAVs, and surveillance significantly influence CS and PSAV acceptance, which leads to the importance of trust in adopting AVs. The findings suggest that ensuring public trust occurs through robust safety regulations and transparent data security policies. Furthermore, the envisaged benefits of shared autonomous mobility are alleviating congestion and promoting sustainability. Full article

This study used randomised controlled trials to test the effectiveness of three behavioural interventions, i.e., focalism, social norm, and visualisation, in changing people’s housing and commuting preferences. The experiment was conducted online via Credamo, one of the largest online panel data providers in China. It included only renters who needed to commute in the city of Xi’an, China, as participants in the study. The results show that behavioural interventions significantly increased respondents’ willingness to adopt more sustainable commute modes, such as walking or cycling, and reduced the tendency to use private cars. Among the three behavioural interventions, the social norm intervention had the largest and most significant impact. The findings shed light on the potential of applying behavioural interventions in sustainable urban transport management. More importantly, the results demonstrate the possibility of using behavioural interventions to incorporate sustainable urban development goals into housing decisions. Full article

Technological innovation toward electrification and digitalization is revolutionizing aviation, paving the way for new aeronautical paradigms and novel modes to transport goods and people in urban and regional environments. Advanced Air Mobility (AAM) leverages vertical and digital mobility, driven by safe, quiet, sustainable, and cost-effective electric vertical takeoff and landing (VTOL) aircraft. A key enabler of this transformation is the development of vertiports—dedicated infrastructure designed for VTOL operations. Vertiports are pivotal in integrating AAM into multimodal transport networks, ensuring seamless connectivity with existing urban and regional transportation systems. Their design, placement, and operational framework are central to the success of AAM, influencing urban accessibility, safety, and public acceptance. These facilities should accommodate passenger and cargo operations, incorporating charging stations, takeoff and landing areas, and optimized traffic management systems. Public and private sectors are investing in vertiports, shaping the regulatory and technological landscape for widespread adoption. As cities prepare for the future of aerial mobility, vertiports will be the cornerstone of sustainable, efficient, and scalable air transportation. Full article

Traffic congestion is becoming an increasing problem due to the rapid growth of the population. In the current situation, the mode choice of the people has a direct impact on traffic density. For this reason, many studies have been carried out by researchers and planners to reduce the number of vehicles on the road. Various strategies have been proposed, such as incentives for public transport, parking restrictions, parking pricing and car sharing. It is very important that these strategies are implemented by the institutions in order to reduce traffic during the commuting hours, which coincide with the rush hour. Especially in areas such as shipyards and industrial zones, which are far from the city center and relatively difficult to reach but which provide employment opportunities for thousands of people, a shuttle service is one of the most preferred strategies to discourage employees from using private cars. However, in companies with thousands of employees, this situation generates costs that cannot be ignored. The examined case study similarly needs to optimize and reduce operational costs related to fuel consumption, maintenance and tax expenses by optimizing the number of two different types of service vehicles required for employee transportation at the Yalova Shipyard. For this aim, a dynamic linear programming (DLP) model was used to achieve a cost-effective, sustainable and demand-responsive shuttle service. According to the analysis results, it was concluded that the annual fuel cost of the vehicles will be reduced by 33.9%, the maintenance cost by 35.2% and the annual tax cost by 49.3% by disposing of the unneeded vehicles (27%) in the studied Yalova Shipyard. Taking all these positive improvements into account, it is clear that the optimization study significantly reduces the costs incurred by the service. Full article

The limited availability of mobility data makes it challenging to model demand, especially its spatiotemporal variations. Simultaneously, traditional transport modeling tools often rely on less disaggregated approaches, leading to gaps in understanding. To overcome these limitations, this study introduces the spectral clustering method to uncover major demand patterns considering various transport modes. It focuses on Athens, Greece, and utilizes a set of 1347 reported trips. The study reveals six distinct trip clusters. The first group, “an evening stroll nearby”, captures short distance tours typically undertaken by walking. The second cluster, “my work is nearby but I use my car” highlights a significant trend where individuals with short commutes, less than 6 km, predominantly use private cars. The third cluster, “commuting by metro”, features long-distance trips primarily for work. The fourth cluster reveals long-distance work-related trips with private cars, favored by active residents with high income. The fifth pattern, “trips of young people”, involves midnight recreational and moderate-distance morning trips for education, with an increased usage of public transport. The sixth cluster concerns short distance tours for various activities. The findings indicate that the second cluster’s high reliance on private cars for short trips is problematic. Reducing this reliance should be a priority for policymakers. Full article