Building a sustainable transport system requires effective control of vehicle emissions, a key challenge in pollution and carbon reduction. This paper employs a Difference-in-Differences (DID) model to evaluate the effects of China's upgraded vehicle emission standards (China IV and V) on CO, HC, and NOx reductions. Heterogeneity effects across vehicle types (diesel/gasoline, light-duty/heavy-duty) are further examined. Key findings: (1) Emission standards significantly reduce total vehicle emissions; (2) Diesel vehicles show greater responsiveness than gasoline vehicles, with distinct policy focuses for light-duty compared with heavy-duty types; (3) China IV exhibits a tiered reduction pattern of HC > NOx > CO, while China V shows HC > CO > NOx; (4) Light-duty vehicles outperform heavy-duty vehicles across all emissions, with diesel vehicles demonstrating stronger CO and HC mitigation than gasoline counterparts. These findings offer actionable insights for refining tiered emission regulations and advancing the low-carbon transition in China's transportation sector.
Road traffic noise exposure assessment typically relies on aggregated traffic flow data, which prevents the estimation of high-temporal-resolution noise indicators increasingly recognized as important for health impact studies. To bridge this gap, this research proposes two stochastic disaggregation methods that reconstruct refined vehicle kinematics from aggregated traffic flows, enabling 1-s resolution noise estimation comparable to computationally intensive microscopic traffic modelling chains. Using SUMO microscopic simulation as reference, the disaggregation methods are evaluated in a dense urban area, in Stockholm's S & ouml;dermalm Island. The resulting acoustic indicators calculated, including LAeq,1h, LA10,1h, LA1,1h, and LAeq,1s, show estimates comparable to those obtained from the microscopic traffic noise modelling chain. Robustness and sensitivity analyses show that the proposed methods maintain stable performance even with reduced input data granularity. The proposed methods offer a practical intermediate solution between static annual noise maps and detailed microscopic simulations, enabling cost-effective dynamic noise exposure assessment at an urban scale.
Motivated by carbon neutrality goals, heavy-duty hydrogen fuel cell vehicles (HD-HFCVs) hold significant growth potential in China, particularly in long-distance freight transportation. This study constructs an improved generalized Bass diffusion model incorporating learning curves and SSP scenarios to predict diffusion trajectory and emission reductions for three main types of HD-HFCVs and assess the impact of incentive policies. The obtained findings include: (i) HD-HFCVs will not be market-competitive until after 2030 without additional policy support, and it is highly unlikely to achieve the emission reductions required for carbon neutrality by 2060; (ii) a ban on diesel trucks from 2025 could boost HD-HFCVs adoption by 47%-66% and reduce cumulative emission over 11,000 Mt CO2,eq by 2060; (iii) subsidies also have a nonlinear inverted U-shaped impact on expanding HD-HFCVs deployment, and the optimal subsidy level for maximizing the HD-HFCVs differs from that for maximizing carbon emissions reductions. These findings can provide critical policy implications.
This paper proposes a two-phase sustainable optimization framework for flood relief distribution. The first phase solves a multi-objective problem to optimize shelter-hub locations and response team allocation, considering economic, social, and environmental factors, using a hybrid genetic algorithm and exact method. The second phase evaluates first-phase solutions through a model that incorporates multimodal transportation (truck, drone, and heterogeneous boats) with response team arrival time-window, solved by combining variable neighborhood search, exact method, and dynamic threshold-based procedure. The solution methods are tested on generated instances and case study in Jakarta, Indonesia, a region highly prone to flood disasters characterized by deep flood depths, extensive flooded area, long rainfall duration, and a high potential risk of death. The results demonstrate promising solutions and provide useful managerial insights.
This study examined how traveler-centered evaluations of transport service quality and travel experience are associated with willingness to substitute private vehicle trips with public transport in urban northern Taiwan. A cross-sectional survey of 1,002 adults measured the importance of 11 service-quality dimensions, mode-specific evaluations, and substitution intentions. Repeated-measures comparisons, PLS-SEM, and logistic regression were used to analyze cross-mode evaluations, intention relationships, and perceived private-vehicle replaceability. Intention to substitute private vehicle trips with bicycling was the strongest predictor of intention to substitute such trips with public transport, while public transport satisfaction had a smaller positive effect. In addition, 69.4% of respondents considered private vehicles replaceable. Higher perceived replaceability was associated with cost considerations, safe cycling experience, environmental and infrastructure priorities, weaker attachment to vehicle ownership, and gender. These findings support bicycle-public transport complementarity and integrated strategies to reduce private motorized travel dependence in high-density urban contexts.
Although electric vehicles (EVs) provide environmental benefits, their impacts on travel behavior, specifically transit choice, remain underexplored. Empirical evidence on how the built environment and EV ownership jointly influence transit choice is, to our knowledge, scarce. This study addresses gaps by applying double machine learning to a large-scale household travel survey from Wuhan, China, in 2020. The approach simultaneously addresses nonlinear effects and the endogeneity of vehicle ownership when examining relationships among the built environment, vehicle ownership, and transit choice. The results show that EV ownership reduces the probability of using transit more than conventional vehicles, about 26% versus 16%. For EV users, built environment attributes gain predictive importance, yet many exert weaker or opposite effects on transit choice, suggesting possible failure of conventional transit-supportive interventions. Using causal machine learning, these findings provide new evidence for revisiting the behavioral foundations of transit-oriented development in the context of transportation electrification.
Rapid urbanization makes traditional, fixed-frequency municipal solid waste (MSW) collection highly inefficient, causing severe operational and environmental impacts. This study develops a GIS-based optimization framework for high-density MSW collection integrating spatial demand analysis with mixed-frequency routing and scheduling. Demand heterogeneity was quantified using GIS-based hot spot analysis to assign collection frequencies, and a standard GIS network solver was reconfigured to emulate periodic capacitated arc routing under high-density conditions. The resulting mixed-frequency plan was evaluated against the municipality's fixedfrequency system in the & Ccedil;igli District of & Idot;zmir. Spatial optimization reduced the number of collection points by 31.63% and container inventory by 25.69%. Operational performance improved through an 18.75% reduction in collection time and a 19.02% reduction in fuel consumption. Correspondingly, collection-related CO2 emissions decreased by 19.91%, contributing 7.54% toward decarbonization targets. By combining low computational cost with the simplicity of a "no-code" GIS environment, this approach offers a scalable, practical solution to improve urban service logistics.
Currently, there is a limited understanding of how cycling, health outcomes, air pollution exposure, build environments and social factors are interrelated, especially for children from disadvantaged communities. This study investigates three main questions: (1) whether engaging in cycling provides net health benefits despite exposure to air pollution, (2) How community and built environment affect children’s physical and mental health and (3) whether these relationships differ between disadvantaged and non-disadvantaged groups. Using data from three Michigan counties, two Structural Equation Models were developed with five key factors: cycling, community, build environment, health, and air pollution. Results show that cycling significantly improves children’s physical and mental health in both groups, with benefits exceeding the negative impacts of pollution exposure. For non-disadvantaged children, community factors representing social and neighborhood conditions significantly enhance both physical and mental health. However, these community effects are weak or insignificant for disadvantaged groups. The impact of build environment on mental health is slightly positive in non-disadvantaged groups but negligible for physical health and for disadvantaged groups. All these suggest that cycling can have a vital role in shaping health when social and environmental conditions are less favorable.
The urgent need to decarbonize transportation has positioned zero-emission vehicles (ZEVs) as a critical solution, yet understanding the complex dynamics driving their adoption remains a challenge. Our model integrates seven state variables includes four vehicle types, emissions, mobility, infrastructure and four incentive programs, revealing previously unidentified adoption patterns. Monte Carlo simulations with 90% confidence intervals project adoption trajectories through 2027, incorporating prediction uncertainty based on historical model error and market dynamics. Growth-focused investments lead to a 3.0% carbondioxide (CO2) emission reduction by 2027 and achieve 158% higher cumulative CO2 savings than incentive-focused strategies. Budget allocation prioritizing 70% growth-focused initiatives over 30% incentives improves adoption rates and market stability. These results reshape understanding of ZEVs adoption and provide actionable insights for accelerating transportation decarbonization worldwide.
This study examines how specific components of bus transit service influence older adults' life satisfaction and self-rated health through the mediating role of active aging. Using survey data from 709 older adults and interviews with 25 participants collected between 2024 and 2025 in Shanghai, China's most aged megacity, we integrate structural equation modeling with large language model-assisted qualitative analysis. Transit service is disaggregated into bus stop infrastructure, on-board features, and driver service experience. Results show that driver service has a strong positive effect on active aging, which in turn enhances both health and life satisfaction, whereas infrastructure-related features exhibit weak or negative indirect effects. Qualitative findings indicate that unmet assistance needs, unreliable information, and inconsistent use of accessibility features undermine the psychological benefits of physical improvements, while attentive driver behavior strengthens confidence and autonomy. These findings highlight the importance of human-centered service in translating infrastructure investment into well-being gains.
The increasing frequency of compound disasters, like 2024’s Hurricane Beryl (tropical cyclone, blackouts, and extreme heat) in Texas, challenges traditional evacuation models, as the dynamics of cascading threats are poorly understood. Using aggregated, high-resolution location data and a multi-stage analytical framework, we show that cascading blackouts under extreme heat triggered a delayed but significant increase in evacuation, with outage-affected communities exhibiting daily evacuation rates 3.0 percentage points higher after landfall than comparable non-outage communities. Spatial lag analysis further indicates that evacuation responses were clustered across neighboring communities. Furthermore, Explainable Machine Learning (XGBoost-SHAP) reveals that socioeconomic factors and racial composition were stronger predictors of evacuation than outage and heat exposure. Crucially, the proportion of white residents was the most powerful positive predictor of leaving. These findings suggest that compound disasters can simultaneously stimulate evacuation while exposing deep inequalities in who is able to respond effectively, underscoring the need for equity-centered disaster planning.
Low Emission Zones (LEZs) are a central instrument of environmental policy across Europe, yet little is known about how public acceptability evolves as regulations are progressively reinforced. Using Madrid (Spain) as a case study, this paper examines acceptability of LEZ reinforcement by adopting a dual perspective that combines individual determinants with the spatial structuring of support and opposition. Based on a face-to-face survey conducted in 2024 (n=1,697), binary logistic regression and spatial statistical techniques (Global Moran’s I and LISA) are integrated. Results show acceptability is driven primarily by mobility-related attributes and anticipatory perceptions, while socio-demographics play a limited role. Spatial analysis reveals a sectoral divide, with opposition in affluent, car-dependent northern areas and support in less affluent, transit-reliant southern areas. Overall, the findings indicate that reinforcement trajectories can generate geographically uneven acceptability, calling for place-sensitive policy packaging, equity-oriented mitigation, and communication strategies that emphasise functional mobility benefits alongside environmental goals.
Low Emission Zones (LEZs) are a flagship climate-air policy in European transport, yet evidence on how design choices redistribute congestion and accessibility is limited. We evaluate nine proposed LEZ variants for Wroc & lstrok;aw (Poland) for 2025, combining three spatial extents with three compliance stringency levels. Using the city's official PTV Visum macromodel, each variant is implemented by scaling within-zone link capacities to reflect the share of compliant vehicles and re-running assignment to derive peak-hour flows, speeds, and travel-time skims. We then quantify short-trip potential accessibility (population-weighted, exponential decay) and classify transport zones by sensitivity using k-means on changes in travel time to the city centre. Results reveal a strongly design-dependent relation between environmental ambition and network performance, with pronounced spatial heterogeneity, providing a policy-ready, transferable framework for LEZ design and phasing.
User-organized Pre-pooled Ride-hailing (UPR) is a user-coordinated form of shared mobility that layers social media coordination on top of commercial ride-hailing within bounded trust-based communities (e.g., campuses, workplaces, or residential compounds). We designed a comprehensive survey with 24 choice scenarios and integrated sociodemographic, revealed-behavior, and attitudinal measures including environmental perceptions, then estimated mixed logit models for all respondents and for organizers versus followers. UPR is most competitive for longer, daytime trips and for first-/last-mile access to metro hubs, offering a lower per-person cost than solo ride-hailing and faster door-to-door travel than public transport. UPR choice is negatively associated with ride-hailing and platform ride-pooling use, implying substitution. Climate-mitigation beliefs increase UPR choice probabilities, while stronger trust requirements and privacy concerns constrain uptake. The findings highlight how environmental perceptions and community trust jointly shape decentralized pooling and its potential role as a low-impact complement to transit in fringe areas.
Truck platooning is a promising strategy to reduce carbon emissions in freight transport, but its real-world mitigation potential across large-scale traffic networks has not been fully quantified. This study develops a data-driven framework to empirically evaluate the carbon reduction effects of truck platooning using eight months of high-resolution GPS trajectories. Advanced map-matching, an enhanced Longest Common Subsequence trajectory similarity method, and graph-based clustering are applied to identify spontaneous platooning events and estimate associated emission reductions. The framework also enables analysis of the spatiotemporal distribution of platooning opportunities across a regional road network. Results indicate that fully exploiting observed spontaneous platooning could cut total fuel consumption by 11.8% compared with a no-platooning baseline. Notably, only 35.8% of platoons occur within a single enterprise, highlighting the importance of cross-company coordination. These findings provide empirical evidence and methodological support for policymakers and industry stakeholders to promote sustainable freight transport through effective platooning strategies.
E-commerce returns have become a central concern in sustainability research. Returns at least double the transportation distance and reduce the resale potential of online products, resulting in extensive environmental impacts. Very little is known about the consumers behind returns, especially those who return excessively (i.e., “serial returners”). This study examines what drives serial return behavior and how serial returners differ from non-serial returners, using survey data from almost 10,000 online consumers representative for ten European countries. What distinguishes serial returners is a strong convenience-oriented attitude, in which returns facilitate impulsive purchasing and postponed decisions. Serial returners are more likely to be younger, urban consumers with lower education levels. Although they represent only roughly 15% of consumers, they generate about 60% of return-related CO2 emissions. By highlighting serial returners’ disproportionate environmental impact, this study underscores the need for return-reducing initiatives that account for consumer heterogeneity.
Achieving transport decarbonization requires evaluating the sustainability of upstream energy systems that support new energy vehicles (NEV). This study develops an integrated multi-criteria framework combining well-to-wheel life-cycle environmental assessment, levelized cost accounting, and stakeholder-informed weighting to compare 28 energy pathways for battery electric vehicles (BEVs), fuel cell vehicles, and internal combustion engine vehicles using synthetic e-Fuels. Results indicate that BEV pathways supplied by low-carbon electricity generally show favorable environmental and economic performance, while ICEV pathways using grid-electrolysis e-Fuels perform the worst. Among hydrogen pathways, grid-electrolysis with gaseous-hydrogen trailers has the highest well-to-pump cost (CNY 51.93 per 105 kJ), whereas coal-gasification with pipeline delivery is lowest (CNY 17.55 per 105 kJ). Although renewable hydrogen routes currently entail higher costs, they show substantial long-term potential as costs decline. These findings offer critical insights for investment priorities and infrastructure strategies to accelerate NEV adoption.
The aviation industry faces growing pressure to cut emissions, with SAF as a key solution. This study proposes a reusable foresight framework integrating patent analysis, main path analysis, and NLP. Results show: (i) SAF has strong innovation potential, with the US and China as core hubs. (ii) HEFA is the best short-term choice, while ATJ and FT offer greater long-term potential. (iii) Eight core technology clusters are identified: biomass to diesel, regulation of bio-derived paraffin properties, fuel production from vegetable/animal fats and oils, improved cold flow properties, catalyst optimization, bio-renewable fuel blends, bio-derived fuel components, and hydrogenation catalyst recovery. (iv) Four candidate disruptive technologies are identified: diversified feedstock development, feedstock collection/processing, catalytic upgrading, and product purification/separation. (v) Cross-disciplinary technologies like AI, bio-enzymatic catalysis, and coupled integration are common breakthrough directions. This framework supports technology investment, policy making, and R&D prioritization for global aviation decarbonization.
This study investigates the factors influencing access mode choice to Mass Rapid Transit (MRT) in Dhaka, Bangladesh, with a focus on perceived walkability and station context. Data collected from three distinct station types-central business district (CBD), residential, and terminal + peripheral-were analyzed using a Hybrid Choice Model, incorporating objective variables and latent perceptions of walkability. Results show a distance-based hierarchy of access modes: walking dominates short trips, rickshaws serve medium-range trips, and buses cover longer distances. Among walkability perceptions, walkway quality significantly influences access mode choice, while street vibrancy and safety have limited effects. Other significant factors include travel time, cost, access time to feeder bus, and station context. Walking is preferred at residential station, while rickshaws and buses dominate at terminal + peripheral station. The findings suggest that developing integrated multimodal transit systems, prioritizing feeder strategies based on station contexts, and revising transit-oriented development (TOD) paradigms are essential for efficient and equitable urban development.
As global climate extremes intensify, assessing how bike-sharing systems respond and recover from weather disruptions is increasingly important for urban resilience. This study develops a comparative framework using a time-series Transformer model and scenario-based simulations to evaluate bike-sharing demand under extreme heat, cold, heavy rain, and snowfall, using hourly trip and weather data from 14 cities worldwide between 2023 and 2025. The weather sensitivity analysis shows that extreme heat reduces demand by over 40% in hotter cities but has limited impacts in temperate ones, while extreme cold and snowfall cause the strongest suppression effects, often exceeding 80%. To further evaluate system resilience, we simulate recovery curves after rainfall and snowfall shocks. North American systems typically rebound within 6-7 hours, whereas Montreal and Seoul show more prolonged recovery periods, while Oslo and New York recover faster due to stronger winter cycling adaptation and operational preparedness. These findings highlight how climatic exposure, travel behavior, and operational readiness shape resilience and provide evidence to support climate-adaptive management and long-term system planning.
