Introduction Subjective well-being (SWB) serves as an important indicator of quality of life. However, previous studies have rarely analyzed the interaction effects of commuting and built environment (BE) on SWB. Methods To address this gap, this study uses 9389 samples from the 2018 China Labor-force Dynamic Survey (CLDS) and applies the eXtreme Gradient Boosting (XGBoost) model to examine the nonlinear associations of commuting and BE characteristics with SWB, as well as their interaction effects. Results The results reveal an inverted S-shaped relationship between commute time and SWB, with an empirical threshold around 25 min, beyond which the interaction effects change markedly. SWB declines significantly when population density exceeds 18,000 persons/km2 or the green coverage rate falls below 5%. Improvement measures such as enhancing public transport accessibility, optimizing street lighting, reducing environmental pollution, and elevating community cleanliness may partly offset the burden of longer commutes by mitigating their negative effects on SWB. Conclusions These findings suggest a preliminary estimate of a 25-min threshold at the national level. Transport and planning policies should carefully consider both commuting burden and BE conditions in different urban contexts to better support residents’ SWB.

Reliance on private cars as the default mode of urban mobility causes congestion, high fuel consumption, as well as degradation of the environment and public health. Green (public transit) and active transport are fundamental to sustainable mobility. To encourage public transit use, fuel policies are widely implemented. Despite the availability of data, key knowledge gaps exist: past studies have treated fuel pricing policies and fuel supply disruption policies as identical. Moreover, no integrated analysis exists examining how these distinct policies reshape public transit demand-particularly in developing cities where traffic mix is dominated by gasoline-powered motorcycles that remain cost-competitive with public transit for short trips, and private vehicles predominantly operate on dual-fuel systems. Peshawar, Pakistan, presents a unique context for exploring this gap. Nearly half of private vehicles in the country operate on dual-fuel systems that use both gasoline and compressed natural gas (CNG). This study addresses the gap by analyzing high-frequency hourly ridership data from Peshawar's Bus Rapid Transit (BRT) system during two distinct fuel policy interventions: (Event A: Fuel Pricing Policy) an unprecedented 66% gasoline price surge (GPH) (May-July 2022), and (Event B: Fuel Disruption Policy) a one-month government-imposed CNG supply shutdown (CSD) (January 2023). Using Bayesian structural time-series (BSTS) hour-by-hour Causal Impact analysis, we find that GPH had no significant impact, whereas the subsequent CSD produced stronger and more temporally consistent effects across different periods of the day. The impact of CSD in the early morning was insignificant; the morning peak showed an increase of similar to 23%. The impact remained persistent during the midday off-peak with an average increase of similar to 17% and intensified to similar to 23% in the evening peak and similar to 32% in the late evening. These findings demonstrate how fuel supply policies significantly influence public transport usage. By quantifying ridership elasticity, this study offers actionable insights for policymakers to integrate energy resilience into transit planning, optimize service delivery during fuel crises, and apply a replicable framework for cities navigating energy transitions.

With the increasing frequency of extreme climate events, the flood safety of underground suburban rail transit stations has garnered significant attention. This study aims to investigate the influence mechanisms of multi-dimensional environmental interventions (human guidance, emergency broadcasting, and environmental stress) and individual characteristics on evacuation wayfinding behavior, thereby quantitatively evaluating the evacuation reliability of underground stations. Using a typical underground station of the recently opened Shanghai Airport Link Line as a prototype, an immersive virtual reality (VR) environment was developed to simulate flood disaster scenarios, involving 987 participants who performed evacuation experiments alongside pre- and post-experiment surveys. This research employs binary logistic regression combined with an ensemble learning algorithm (LightGBM) to perform attribution analysis on complex non-linear behavioral decision-making. Key risk factors affecting decisions were identified, and the SHAP (SHapley Additive exPlanations) framework was introduced to conduct global and local sensitivity analyses. The results indicate that human guidance is the core factor in enhancing systemic evacuation reliability. High levels of staff deployment significantly improve participants' evacuation efficiency; furthermore, a significant synergistic effect exists between sufficient manual guidance and detailed emergency broadcasting, which collectively prompts participants to select the optimal safe route. Notably, the sensitivity of path-finding accuracy to environmental stress decreases significantly under strong human intervention. This study fills the gap in flood evacuation research for suburban railways and demonstrates the superiority of large-sample data in the reliability assessment of emergency evacuations. The findings provide important theoretical guidance for developing high-resilience flood emergency plans and optimizing staff deployment under resource-constrained conditions.

As dockless bike-sharing serves as a key sustainable feeder to metro systems, understanding the factors shaping its integrated use is essential for effective policymaking. Using multi-source data from Shanghai and a Light Gradient Boosting Machine (LightGBM) model, this study examines the nonlinear and interaction effects of the built environment on metro-integrated dockless bike-sharing (MDBS) usage. Results identify metro ridership as the strongest predictor (25.07%), followed by distance to the CBD (12.90%) and Sub-CBD (11.65%). Accumulated Local Effects (ALE) analysis shows pronounced nonlinear relationships, suggesting that many built environment attributes influence MDBS usage only within specific thresholds or effective ranges. SHAP dependence plots further reveal substantial interactions between metro ridership and built environment characteristics, helping distinguish intrinsic urban form effects from transit-induced demand. The findings suggest that high metro ridership alone does not ensure effective integration; rather, supportive network connectivity, jobs-housing balance, and pedestrian accessibility are essential for realizing metro-bike integration.

As population aging accelerates, adapting the Community Life Circles (CLC) framework to the mobility needs of older adults has emerged as an important concern in urban planning research. Although multi-scale approaches are well established in built environment studies, they have rarely been applied within the hierarchical CLC framework to examine scale-dependent built environment effects on older adults' active travel (cycling and walking) across the 5-, 10-, and 15-min tiers, limiting the development of scale-sensitive planning guidance. To address this gap, this study applies a LightGBM model combined with Shapley Additive Explanations (SHAP) to investigate how built environment attributes are associated with older adults' active travel across 5-, 10-, and 15min CLCs in urban areas of Guangzhou, China. Sidewalk density and proximity to bus stops are more strongly associated with active travel at smaller spatial scales (5- and 10-min CLCs), whereas metro availability and park availability become more salient at the 15-min CLC scale. Threshold analysis further shows that stronger positive associations with active travel are observed when sidewalk density reaches 15 km/km2 in the 5-min CLC and around 10 km/km2 in the 10- and 15-min CLCs. In addition, land-use mix exhibits distinct nonlinear patterns across scales, with a V-shaped relationship in 5-min CLC, an inverted V-shaped relationship in 10-min CLC, and a weaker association in 15-min CLC. These scale-specific patterns may help explain inconsistencies in previous studies. By accounting for scale sensitivity and nonlinear effects, this study offers empirical support for agefriendly and activity-supportive planning within the CLC framework.

Exploring the critical roles of various factors on determining the wayfinding performance of passengers during a subway fire emergency is important. By developing a virtual reality environment of an underground urban railway station, an immersive virtual reality escape experiment was conducted with a head-mounted device to record the wayfinding performance of 1,151 participants. The experimental data include trajectory data of the participants in the escape task (e.g., time and three-dimensional coordinates) and survey data of personal information. Binary logistic regression, general linear regression, LightGBM, and other methods were employed to analyze the effects of station staffs' guidance, emergency broadcast, signage, and other factors on wayfinding performance, with station staffs' guidance being a factor rarely addressed in previous studies. The large sample size enabled the application of ensemble learning methods and facilitated fine-grained group difference analyses that are difficult to achieve in traditional small-sample evacuation experiments. Results show that emergency broadcast and the guidance of station staffs had a highly significant impact on evacuees' route choice. Education exhibited a positive influence on both evacuees' route choices and evacuation time. Furthermore, information overload was observed among evacuees under the influence of multiple information sources. The results of this study can not only serve as a theoretical basis for policymakers to understand the determinants of wayfinding performance, but also provide guidance for underground urban railway station or metro station evacuation plan in a fire emergency.

Accurate accident prediction is crucial for proactive safety management on urban expressways. However, its practical efficacy is hindered by several complex challenges, including the heterogeneity of causal data, the need to model the full temporal evolution of risk, and the synergistic, non-linear interactions between variables. To address these challenges, this study proposes BGAR, a dual-channel deep learning framework. The framework features a dual-channel architecture to disentangle static and dynamic data streams, a bidirectional GRU to model the complete risk lifecycle, and a multi-head attention mechanism to weigh critical factor combinations. Validated on a real-world expressway dataset, BGAR demonstrates superior predictive accuracy, outperforming the strongest of 12 established baseline models by 3% in terms of . More importantly, it provides a diagnostic tool that translates forecasts into actionable control strategies. By pinpointing risk drivers, the framework enables a fundamental shift from reactive response to precise, proactive safety management, thus bridging the gap between prediction and prevention. The predictive target is the short-term accident count for the monitored corridor, enabling operators to quantify imminent risk levels in addition to identifying their drivers.

INTRODUCTION:Understanding risk factors associated with perceived and objective neighborhood environments is critical for community-level interventions to prevent falls. This is a cross-sectional design to explore how perceived and objective neighborhood environment are related to falls among community-dwelling older adults in densely populated urban areas. Building upon identified neighborhood environment risk factors, this study proposes preliminary community-level interventions tailored to each determinant. METHODS:This study analyzed data of 400 community-dwelling older adults (age = 70.9 ± 8.0 years; 49.8% female) in central urban areas of Guangzhou, China. Five categories of variable, perceived neighborhood environment, objective neighborhood environment, sociodemographics, health status, and physical activity, were incorporated. Objective neighborhood environment comprised accessibility to ten types of facilities within 500-meter residential buffers and 1 km2 grid-level population density, measured by ArcGIS using geospatial data. Univariate analyses were employed to select variables and multivariable binary logistic regression were used to establish the adjusted model. RESULTS:Older adults who perceived low accessibility to service facilities (OR = 2.502, 95% CI: 1.230-5.088), unsatisfying streetscapes (OR = 1.814, 95% CI: 1.001-3.286), and unsafety neighborhood (OR = 2.614, 95% CI: 1.103-6.192) had higher probabilities of reported falls. Surprisingly, having parks (OR = 0.524, 95% CI: 0.319-0.861) or subway stations (OR = 0.556, 95% CI: 0.326-0.951) within the 500-meter residential buffer, and living in neighborhoods with relatively low population density (OR = 0.842, 95% CI: 0.731-0.972) were associated with an increased risk of falls. Young age (OR = 0.927, 95% CI: 0.887-0.970), low income (OR = 2.449, 95% CI: 1.476-4.064), using walking aids (OR = 1.789, 95% CI: 0.960-3.337), and self-rated good health (OR = 0.392, 95% CI: 0.175-0.879) were risk factors of reported falls. Engaging in physical activity for over 30 min per day (OR = 2.148, 95% CI: 1.111-4.154) was identified as a protective factor. CONCLUSION:Integrating multi-source perceived and objective environmental data, this study found out neighborhood environment risk factors for falls among older adults in high-density urban communities. Our findings contribute to community-level interventions regarding neighborhood environment to reduce falls in older adults in urban areas of developing countries.

Promoting low-carbon travel is critical for decarbonizing the transportation sector. This study extends the Theory of Planned Behavior (TPB) by integrating subjective perceptions of the built environment and transportation policies to understand their influence on travelers' low-carbon travel intentions. Using structural equation modeling (SEM) based on survey data from Shanghai, China, the results show that traveler attitude has the strongest direct effect on intentions. Encouraging policies are found to be more effective than restrictive ones, underscoring the importance of positive incentives. To address population heterogeneity, machine learning models and SHapley Additive exPlanations (SHAP) analysis are applied to identify key demographic factors, including age, income, and fuel costs, for targeted policy design. The findings provide theoretical and practical insights for policymakers aiming to promote sustainable travel behavior through tailored strategies.

Short-wavelength infrared organic photodetectors (SWIR OPDs) have great potential for applications in health monitoring, night vision, optical communication, and image sensing. However, the development of SWIR OPDs is limited by challenges in achieving high responsivity (R) and detectivity (D*) due to the exponentially increased nonradiative recombination rate when decreasing the bandgap of conjugated polymers or molecules. In this study, we designed and synthesized a series of donor-acceptor (D-A) type ultralow bandgap (≤0.85 eV) polymers containing [1,2,5]thiadiazolo[3,4-g]quinoxaline (TQ) units as the A unit and selenophene units as the D unit, respectively. The solubility, molecular stacking, charge transport properties, and film morphology of the polymers were finely tuned by varying the side chain lengths on the substituted phenyl groups of TQ units. It was found that the polymer PTQOD with 2-octyldodecyl alkyl chains has lower nonradiative recombination losses and lower trap state density. After device optimization, the PTQOD-based device achieved higher R and lower dark current density (Jd), resulting in a D* of 1.06 × 1010 Jones at 1300 nm under 0 V bias, representing the highest value for OPDs using TQ-based polymers. This work highlights the importance of optimizing the alkyl chains of ultralow-band gap polymer donor materials and provides a promising approach for developing highly sensitive SWIR OPDs.

OBJECTIVE:To estimate the importance of risk factors on overweight/obesity among older adults by comparing different predictive model. METHODS:Survey data from 400 older individuals in China was employed to assess the impacts of four domains of risk factors (demographic, health status, physical activity and neighborhood environment) on overweight/obesity. Six machine learning algorithms were utilized for prediction, and SHapley Additive exPlanations (SHAP) was employed for model interpretation. RESULTS:The CatBoost model demonstrated the highest performance among the prediction models for overweight/obesity. Gender, transportation-related physical activity and road network density were top three important features. Other significant factors included falls, cardiovascular conditions, distance to the nearest bus stop and land use mixture. CONCLUSION:Insufficient physical activity, denser road network and incidents of falls increased the likelihood of older adults being overweight/obese. Strategies for preventing overweight/obesity should target transportation-related physical activity, neighborhood environments, and fall prevention specifically.

In this work, in an effort to study tunneling physics, we have performed extensive electrical characterization of two generations of top-down III–V vertical nanowire tunnel field-effect transistors (TFETs). The widely different MOS interface trap densities of two generations of TFETs and the availability of metal–oxide–semiconductor field-effect transistors fabricated simultaneously enable direct study of various effects of interface states on TFETs. Interface trap-assisted tunneling was suppressed in the second generation TFETs, and the residual temperature dependence of the second generation TFETs can be explained by a band-edge sharpness of 120 mV/dec.

Hybrid functional materials with inorganic and organic components have huge potential against cancer. However, there is still a lack of controllable structural engineering. Here, we report a facile route to fabricate bismuth oxide methacrylate hybrid nanobamboos with sizes and morphologies easily modified by changing precursor concentrations. The hybridization of bismuth-oxide and methacrylate endows nanobamboos with robust cytotoxicity and radiosensitization effects against MB49 mouse bladder cancer cells.

Fire accidents occurred in underground metro train resulted in extensive loss of life and damage to property. In this study, an immersive virtual reality experiment was designed and conducted to investigate passengers' evacuation performance inside the burning train carriage. The step-in-place technique was developed to make the player's operation more convenient and realistic. A total of 126 participants conducted the VR experiment. Statistical analysis methods including normality test, Mann-Whiney U test, discrete choice model, and Tukey post-hoc tests were conducted. The results showed that people with real evacuation experience tended to evacuate longer than people without evacuation experience since past experience made them more flustered. People with real evacuation experience preferred to escape to adjacent carriage instead of staying in burning carriage. Female were more cautious while male were more risky due to different biological structures and social roles. Most people concerned more about safety than evacuation time. Previous evacuation education experience had no statistical significance on evacuation time, speed, and exit choice. The findings of the research could provide guidance for management staff to understand evacuation behavior and improve rescue performance.

The development of 2H-MoS2 in supercapacitor is limited due to its poor conductivity and narrow interlayer spacing. The heterostructure assembly with conductive matrix is an effective means to improve conductivity, but the problem of narrow interlayer spacing still exists. Herein, by combining organic ion intercalation and heterostructure assembly, the narrow interlayer spacing and low conductivity of 2H-MoS2 are optimized simultaneously. The synergistic effects are thereby obtaining, and the electrochemical advantages of different components in MoS2-PPy@Ti3C2Tx are co-exerted. What's more, density functional theory (DFT) calculation is done to explore the kinetics storage mechanisms in MoS2 samples, which shows that the polarity of MoS2 has been enhanced for the introduction of Ti3C2Tx MXene. This facilitated the mightier interface energy and extra capacitance. The current study smooths the path for structuring heterostructures on 2H-MoS2 in the field of supercapacitors.

Recently, urban spatial equity has become a research hotspot, but research focuses on the equity of work commuting from different dimensions. This paper aims to determine the fairness difference of work commuting in Chengdu from three different dimensions by analyzing job accessibility in Chengdu. Firstly, population residence and employment data are obtained by using mobile phone signaling data, real-time travel data are obtained by using Amap API, and regional housing information is obtained from a real estate website. Secondly, the differences in time and cost of job accessibility in different regions are calculated under different time thresholds. Finally, the equity of job accessibility is evaluated by using the Theil index and the Gini coefficient from three new perspectives: transport mode, house price economy, and spatial region. The experimental results show that (1) when time threshold increases, public transport in Chengdu is more equitable, while car traffic is opposite; (2) regions with higher prices are generally fairer; and (3) Chengdu’s equality disparities are more between areas than within areas. In addition to proposing a new accessibility formula based on travel impedance, this study suggests a new method for analyzing equity differences in Chinese cities that can serve as a reference for future researchers. At the same time, the results provide a scientific basis for optimizing the social spatial distribution of public transport services in Chengdu.

IntroductionWalking plays a crucial role in promoting physical activity among older adults. Understanding how the built environment influences older adults’ walking behavior is vital for promoting physical activity and healthy aging. Among voluminous literature investigating the environmental correlates of walking behaviors of older adults, few have focused on walking duration across different age groups and life stages, let alone examined the potential nonlinearities and thresholds of the built environment.MethodsThis study employs travel diary from Zhongshan, China and the gradient boosting decision trees (GBDT) approach to disentangle the age and retirement status differences in the nonlinear and threshold effects of the built environment on older adults’ walking duration.ResultsThe results showed built environment attributes collectively contribute 57.37% for predicting older adults’ walking duration, with a higher predicting power for the old-old (70+ years) or the retired. The most influencing built environment attribute for the young-old (60–70 years) is bus stop density, whereas the relative importance of population density, bus stop density, and accessibility to green space or commercial facilities is close for the old-old. The retired tend to walk longer in denser-populated neighborhoods with better bus service, but the non-retired are more active in walking in mixed-developed environments with accessible commercial facilities. The thresholds of bus stop density to encourage walking among the young-old is 7.8 counts/km2, comparing to 6 counts/km2 among the old-old. Regarding the green space accessibility, the effective range for the non-retired (4 to 30%) is smaller than that of the retired (12 to 45%).DiscussionOverall, the findings provide nuanced and diverse interventions for creating walking-friendly neighborhoods to promote walking across different sub-groups of older adults.

Elevated metro is a typical type of urban rail transit that operates on viaducts. Due to its fast construction speed and low cost, it plays an important role in transportation systems with dense concentrations of people. Once a fire accident occurs in rush hour, it will cause high casualty and enormous injuries. In this regard, studying the characteristics of fire smoke spread and passengers’ evacuation is of profound significance. In this paper, Pyrosim and Pathfinder software was utilized to simulate metro fire and evacuation process. The variation of temperature, CO concentration, and visibility inside carriage and outside walkaway were studied to explore the characteristic of fire and smoke. Evacuation time, speed, density, and path were analyzed for six cases considering the influence of railing settings on evacuation walkaway and the use of track bed. The impact of smoke on movement speed reduction and temperature on the available door numbers were taken into consideration. Results revealed that the temperature of accident carriage rose to tenability limit after 50 s and the visibility decreased to 5 m within 150 s. CO concentration was below safety criteria due to sufficient oxygen. Temperature was the most important factor that leads to the risk of evacuation, especially inside the accident carriage. The length of evacuation walkaway could highly reduce evacuation time and density. Railings had a negative impact on evacuation while the usage of track bed had a positive effect. Besides, the impact of railings was not as significant as the usage of the track bed. Research results can provide decision-making reference for fire protection and evacuation design to alleviate congestion and improve safety management.

Understanding the spatially varying effect mechanism of intermodal connection on metro ridership helps policymakers develop differentiated interventions to promote metro usage, especially for megacities with multiple city sub-centers and ring roads. Using multiple datasets in Shanghai, this study combines Light Gradient Boosting Machine (LightGBM) with Shapley additive explanations (SHAP) to explore these effects with the consideration of the built environment and metro network topology. Results show that the collective impacts of intermodal connection are positive, not only within the main city but also alongside the main commuting corridors, while negative effects occur in the peripheral area. Specifically, bike sharing trips increase metro ridership within the inner ring of the city, while bus services lower metro usage at stations alongside the elevated ring roads. Parking facilities enable metro usage at city sub-centers, and the small pedestrian catchment area increases metro riders alongside the main commuting corridors. Empirical findings help policymakers understand the effect mechanism of intermodal connection for stations in different regions and prioritize customized planning strategies.