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Photo-oxidation of methane (CH 4 ) using hydrogen peroxide (H 2 O 2 ) synthesized in situ from air and water under sunlight offers an attractive route for producing green methanol while storing intermittent solar energy. However, in commonly used aqueous-phase systems, photocatalysis efficiency is severely limited due to the ultralow availability of CH 4 gas and H 2 O 2 intermediate at the flooded interface. Here, we report an atomically modified metal-organic framework (MOF) membrane nanoreactor that promotes direct CH 4 photo-oxidation to methanol at the gas-solid interface in a reticular open framework. We show that the domino synergy between colocalized single-atom palladium and iron on MOF nodes enables efficient generation and in situ utilization of H 2 O 2 in the absence of liquid water, thus circumventing H 2 O 2 dilution. Meanwhile, the “breathable” MOF membrane, optimized by solar-driven interfacial water management, provides high-flux channels to facilitate efficient gas diffusion and rapid methanol desorption and transfer. As a result, we demonstrate over 210 hours of continuous photosynthesis of 0.25 M methanol with unity selectivity, achieving an exceptional methanol productivity of 14.4 millimoles per gram of catalyst per hour. The photooxidation of CH 4 to CH 3 OH is limited by the availability of reactants at flooded interfaces. Now the authors report a modified metal organic framework nanoreactor to promote the direct photooxidation of CH4 to CH 3 OH at the gas-solid interface.

To promote the sustainable development of urban smart transportation systems, this study constructs a structural equation model (SEM) based on the Technology Acceptance Model (TAM), incorporating extended variables including social influence, environmental awareness, privacy concerns, and service similarity to investigate users’ behavioral intentions toward Mobility as a Service (MaaS). The research systematically examines key factors influencing user adoption behavior and their underlying mechanisms, providing theoretical foundations and practical guidance for optimizing MaaS system design and policy making. Using SEM as the core analytical framework, this study employs mediation analysis, moderation analysis, and multigroup comparison to empirically examine the direct and indirect effects among variables, as well as group heterogeneity. Data were collected through an online questionnaire survey, with Analysis of Variance (ANOVA) applied to identify the differential impacts of demographic and travel behavior characteristics on users’ intentions and related psychological constructs, thereby supporting precise user segmentation and evidence-based policy interventions. Key findings include the following: (1) Social influence, ease of use, and environmental awareness boost MaaS adoption, while privacy concerns hinder it. (2) Freelancers/self-employed weaken the positive effects of usefulness, ease of use, and social influence on adoption. (3) Service similarity and ease of use effects vary significantly between single-mode and multimodal commuters. The findings extend the theoretical boundaries of TAM and provide both theoretical and practical support for the development of sustainable urban transportation systems.

Demand-responsive transit (DRT) is an innovative public transportation model that dynamically adjusts routes based on passengers’ specific demands. While existing studies offer insights into routing, scheduling, and network design, they remain fragmented, with limited integration of user behavior, policy relevance, and sustainability. To address these gaps, this paper develops a scenario-based evaluation framework that synthesizes bibliometric evidence, operational conditions, modeling approaches, and evaluated outcomes. Using CiteSpace, we conducted keyword co-occurrence and clustering analysis. Thematic clusters such as “routing and scheduling,” “network design,” “stated preference,” “public transport,” and “demand-responsive transit” were mapped to a three-tier analytical structure. Scenarios integrate economic, environmental, and social dimensions, enabling comparative insights across urban, rural, and intercity scenarios. The scenario-based approach offers two key advantages: (1) it captures heterogeneity across operational environments, ensuring that evaluation frameworks are not overly generalized. Research shows that urban scenarios emphasize scheduling precision, rural pilots face cost-efficiency but enhance resilience, and intercity services depend on multimodal synchronization. (2) It facilitates synthesis by linking technical models with real-world outcomes, enhancing policy relevance. This study contributes to sustainable transport research by providing a coherent, empirically validated, and conceptually integrated framework for evaluating DRT systems.

Human–machine shared control has been widely adopted to enhance driving performance and facilitate smooth transitions between manual and fully autonomous driving. However, existing authority allocation strategies often neglect real-time assessment of scenario complexity and driver workload. To address this gap, we leverage non-invasive eye-tracking devices and the 3D virtual driving simulator Car Learning to Act (CARLA) to collect multimodal data—including physiological measures and vehicle dynamics—for the real-time classification of scenario complexity and cognitive workload. Feature importance is quantified using the SHAP (SHapley Additive exPlanations) values derived from Random Forest classifiers, enabling robust feature selection. Building upon a Hidden Markov Model (HMM) for workload inference and a Model Predictive Control (MPC) framework, we propose a novel human–machine shared control architecture with adaptive authority allocation. Human-in-the-loop validation experiments under both high- and low-workload conditions demonstrate that the proposed strategy significantly improves driving safety, stability, and overall performance. Notably, under high-workload scenarios, it achieves substantially greater reductions in Time to Collision (TTC) and Time to Lane Crossing (TLC) compared to low-workload conditions. Moreover, the adaptive approach yields lower controller load than alternative authority allocation methods, thereby minimizing human–machine conflict.

Promoting low-carbon travel modes is crucial for China’s transportation sector to achieve the dual carbon goals. When exploring the mechanisms behind individuals’ travel decisions, the relationships between factors such as the built environment and transportation policies are often derived from prior experience or subjective judgment, rather than being grounded in a solid theoretical foundation. In this paper, we build on and integrate the Theory of Planned Behavior (TPB) and the Technology Acceptance Model (TAM) by introducing built environment perception (BEP), encouraging policy perception (EPP), and restrictive policy perception (RPP) as either perceived ease of use (PEOU) or perceived usefulness (PU). The integration aims to explain how the latent variables in TPB and TAM jointly affect low-carbon travel intention. We conduct a traveler survey in Shanghai, China to obtain the data and employ a structural equation modeling (SEM) approach to characterize the latent mechanisms. The SEM results show that traveler attitude is the most critical variable in shaping low-carbon travel intentions. Perceived ease of use has a significant positive effect on perceived usefulness, and both constructs directly or indirectly influence attitude. As for transportation policies, encouraging policies are more effective in fostering voluntary low-carbon travel intentions than restrictive ones. Considering the heterogeneity of the traveling population, differentiated policy recommendations are proposed based on machine learning modeling and SHapley Additive exPlanations (SHAP) analysis, offering theoretical support for promoting low-carbon travel strategies.

Malignant traffic accidents are typical devastating events suffered by the urban road network. They cause severe functional loss when loading on the urban road network is high, exerting a significant impact on the operation of the city. The resilience of a road network refers to its ability to maintain a certain level of capacity and service when disturbed by external factors and to recover after a disturbance event, which is a crucial factor in the construction of transportation infrastructure systems. A comprehensive understanding of the adverse effects of malignant traffic accidents on the urban road network is imperative, and resilience is a concept employed to systematically explain this. This study investigates the impact of malignant traffic accidents on the resilience of the urban road network. A simulation is carried out focusing on an ideal urban road network, describing the temporal and spatial distribution of the average speed of road sections in the network. Inspired by the simulation experiment results, the ideal resilience curve is summarized, and the theory of resilience concept portrayal is innovatively developed into “6R” (redundancy, reduction, robustness, recovery, reinforcement, and rapidity). Combining the topological and “6R” resilience attributes of the urban road network, the urban road network resilience evaluation system is constructed, which yields an all-round and full-process evaluation for the urban road network with malignant traffic accidents. Results show that under malignant traffic accidents, the resilience of high-class surface roads, such as primary roads, is the poorest, suggesting that more attention and resources must be devoted to high-class surface roads. This study on the urban road network deepens the understanding and portrayal of its resilience and proposes an evaluation method to analyze its performance under disruption events.

Personal travel pattern is significant to transportation analysis and modeling, and the rapid development of in-depth application of location-based services makes it possible to obtain large-scale positioning data. So, it is crucial to develop proper algorithm to identify trips/trip-segments from individual positioning records. This article presents an automatic trips/trip-segment detection method based on instantaneous Global Positioning System records collected by smartphones. The method consists of a series of procedures including data cleaning and pre-processing, inferring and removing pseudo trip ends, as well as trip combination. The result of the model has been compared with the “ground truth” collected and verified by volunteers. Finally, 1954 trips from 125 volunteers were identified and the overall detection accuracy is between 97.5% and 98.7% with a 95% confidence level. Besides, purity was introduced to evaluate the performance of the proposed method. In addition, the integration of instantaneous speed over time shows an excellent performance in calculating the trip distance.

This study has been motivated from a real western-style food delivery problem in Dalian city, China, which can be described as a vehicle routing problem with time windows. An integer linear model for the problem is developed, and an improved artificial bee colony algorithm, which possesses a new strategy called an adaptive strategy, a crossover operation, and a mutation operation, is proposed to solve the problem. Then, the effectiveness of the proposed improved artificial bee colony is first validated by some benchmark instances. Furthermore, results obtained on a real-case instance for western-style food delivery problem in Dalian city are also discussed. In this case, the results indicate that the improved artificial bee colony algorithm is a feasible method to solve the real vehicle routing problem with time windows such as western-style food delivery.

Alleviating traffic congestion and developing sustainable transportation systems in a city can be assisted by promoting environmentally friendly transportation modes such as walking, cycling, and public transport. Strategies for promoting these desirable transportation modes can be identified based on a sound understanding of how commuters choose travel modes. In this study, multi-day commuting travel mode data was used to explore factors that influenced commute mode choice. A multinomial logit model and a binary logit model were proposed to study commuter travel behavior. The results showed the following. (1) Age, gender, and marriage indirectly influence the commute mode choice; (2) The cost of travel mode has little effect on commute mode choice; (3) The probability of commute mode change mainly influences the car mode choice; (4) The number of transfer times and the distance to the nearest public transport stations are main factors that restrict commuters from choosing public transport; (5) The number of bicycles in the family and commute distance are main factors that restrict commuters from choosing cycling for commuting. Based on these findings, several potential measures are demonstrated to policymakers and transportation planners to alleviate traffic congestion and develop sustainable transportation systems.

China is expected to have more children now that its family planning policy has been relaxed, and the influence of children on transportation and sustainability should not be neglected. This study uses econometric methods to explore the impact that the presence of children has on household car ownership, car-travel behavior of family members, and variability in their car-use frequency across weekdays and weekends. Models are estimated using multi-day travel patterns imputed from GPS-enabled smartphone data collected in Shanghai, China. Results indicate that: (1) households with children have more private cars than those without children, and the presence of preschoolers and pupils both increase families’ demand for car ownership; (2) travel behavior of people from households with children is influenced subtly by the children’s presence, which leads them to prefer to travel by car, although the presence of retired or unemployed household members can weaken that influence; and (3) car-travel frequency of individuals is significantly different between weekdays and weekends, with the presence of pupils in the household diminishing that variability and the presence of preschoolers enlarging it. Policymakers and transportation planners should be concerned about these issues and take appropriate measures.