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.

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.

IntroductionThe accelerated motorization has brought a series of environmental concerns and damaged public environmental health by causing severe air and noise pollution. The advocate of urban rail transit system such as metro is effective to reduce the private car dependence and alleviate associated environmental outcomes. Meanwhile, the increased metro usage can also benefit public and individual health by facilitating physical activities such as walking or cycling to the metro station. Therefore, promoting metro usage by discovering the nonlinear associations between the built environment and metro ridership is critical for the government to benefit public health, while most studies ignored the non-linear and threshold effects of built environment on weekend metro usage.MethodUsing multi-source datasets in Shanghai, this study applies Gradient Boosting Decision Trees (GBDT), a nonlinear machine learning approach to estimate the non-linear and threshold effects of the built environment on weekend metro ridership.ResultsResults show that land use mixture, distance to CBD, number of bus line, employment density and rooftop density are top five most important variables by both relative importance analysis and Shapley additive explanations (SHAP) values. Employment density and distance to city center are top five important variables by feature importance. According to the Partial Dependence Plots (PDPs), every built environment variable shows non-linear impacts on weekend metro ridership, while most of them have certain effective ranges to facilitate the metro usage. Maximum weekend ridership occurs when land use mixture entropy index is less than 0.7, number of bus lines reaches 35, rooftop density reaches 0.25, and number of bus stops reaches 10.ImplicationResearch findings can not only help government the non-linear and threshold effects of the built environment in planning practice, but also benefit public health by providing practical guidance for policymakers to increase weekend metro usage with station-level built environment optimization.

Public transport improves mobility and well-being for the rapidly aging population. However, few planning interventions have addressed the urban–rural disparity in bus usage among older adults. Using data from Zhongshan, China, this study adopts the eXtreme Gradient Boosting (XGBoost) model to examine urban–rural differences in the nonlinear relationship between built environment and daily bus usage among elderly adults. The results indicate nonlinearities across all built environment variables and stronger effects of the built environment in rural areas. Distance to transit contributes the most in urban neighborhoods but least in rural ones. Furthermore, dwelling unit density and green space accessibility play the biggest roles in the rural context. Additionally, the most effective ranges of intersection density, land use mixture, and CBD accessibility are greater in rural areas. The findings facilitate fine-grained and diversified planning interventions to facilitate bus usage among older adults in both urban and rural areas.

Social interaction, such as voluntary employment, can promote well-being and mental health for older people. Since walking and public transit are two major commuting modes for older adults, understanding the determinants of older employment behavior near metro stations is critical for the government and urban planners to encourage older employment. Using the mobile signaling data of 1,640,145 older employees and other multi-source spatiotemporal datasets in Shanghai, the Light Gradient Boosting Machine (LightGBM) is employed in this study to explore the nonlinear effects of the built environment on older employment near 333 metro stations. Results show that density, diversity, and design variables have a significant contribution on older employment, while distance to the city center, employment density among all age groups, and the number of older residents are the top three important variables. Partial dependence plots reveal that all independent variables have irregular nonlinear impacts on older employment. Each variable needs to reach an associated threshold to maximize older employment, and their nonlinear impacts are only effective when they are within certain ranges. Research findings can promote older employment and benefit mental health among older people by helping the government prioritize urban planning policies or interventions.

Objectives: To examine whether the associations of neighborhood environment and body mass index (BMI) of community-dwelling older adults aged 70 and above were mediated by transport-related physical activity (TRPA).Methods: A bootstrap method was employed to test the mediation model with multisource data from Chongqing, China.Results: Neighborhood walkability (effect: 0.030, 95% CI [0.001-0.160]) and shopping facility accessibility (effect: 0.002, 95 % CI [0.001 -0.101]) exhibited positive effects on BMI indirectly through decreasing TRPA duration. Negative indirect effects of sports facility accessibility (effect:-0.004, 95 % CI [-0.112 --0.003])and transit accessibility (effect:-0.044, 95 % CI [-0.074 --0.002])on BMI were observed through increasing TRPA duration. Park accessibility showed both direct (effect:-0.242, p < 0.05) and indirect (effect:-0.036, 95 % CI [-0.061 --0.005])negative correlations with BMI. Conclusion: Our findings facilitate neighborhood environment interventions regarding obesity among older adults in developing countries.(c) 2023 Elsevier Inc. All rights reserved.

After a fire breaks out, pedestrians simultaneously move towards the exit and quickly form a crowded area near the exit. With the intensification of pedestrians’ tendencies towards unfair competition, there is an increase in pushing and collisions within the crowd. The possibility of stampedes within the crowd also gradually increases. Analyzing the causes and psychological tendencies behind pedestrian pushing and collisions has a positive effect on reducing crowd instability and improving evacuation efficiency. This research proposes a modified social force model considering the unfair competition tendency of pedestrians. The model considers factors such as the gap between pedestrians’ actual and maximum achievable speed, effective radius, and their distance from the exit. In order to overcome the shortage of “deadlock” in the classical social force model in a high-density environment, this research introduces the feature of variable pedestrian effective radius. The effective radius of pedestrians dynamically changes according to the density of the surrounding crowd and queuing time. Through validation, the evacuation efficiency of this model aligns well with the actual situation and effectively reflects pedestrians’ pushing and squeezing behaviors in high-density environments. This research also analyzes how to strategically arrange obstacles to mitigate the exacerbating effect of unfair pedestrian competition on exit congestion. Five experiments were conducted to analyze how the relative position of obstacles and exits, the number of evacuation paths, and the size of the obstacle-free area before the exit affect evacuation efficiency in the presence of unfair pedestrian competition. The results show that evacuation efficiency can be improved when obstacles play a role in guiding or reducing the interaction of pedestrians in different queues. However, when obstacles hinder pedestrians, the evacuation efficiency is reduced to a certain extent.

In developing countries, aging is rapid and new towns in suburban and rural districts are emerging. However, the spatial accessibility and equity of healthcare services for older adults in new towns is rarely examined. This study is among the earliest attempts to evaluate the spatial accessibility and equity of public hospitals for older adults, using data from Songjiang District, Shanghai, China. A modified Gaussian Huff-based three-step floating catchment area (GH3SFCA) method was adopted based on the real-time travel costs of public transit, driving, cycling, and walking. The Gini coefficient and Bivariate Moran’s Index were integrated to estimate spatial equity. The results showed that the spatial accessibility of high-tier hospitals decreases from the central areas to the outskirts for older adults in Songjiang. Meanwhile, the accessibility of low-tier hospitals varies substantially across areas. Although the low-tier hospitals are distributed evenly, their Gini coefficient showed less equitable spatial accessibility than the high-tier hospitals. Furthermore, driving and cycling lead to more equitable spatial accessibility than public transit or walking. Finally, communities with a low-supply–high-demand mismatch for public hospitals were suggested to be improved preferentially. These findings will facilitate planning strategies for public hospitals for older adults in developing new towns.

Understanding the effects of intermodal connectivity on metro ridership is essential for policymakers and urban planners to facilitate metro usage. Although many studies have examined the associations between the built environment and metro ridership, few investigated the effects of first/last-mile facilities. Using smartcard data and multi-source big data in Shanghai, this study applies LightGBM approach to estimate the nonlinear, threshold and synergistic effects of last-mile facilities on metro ridership, while controlling for built environment characteristics and metro network attributes. Results show that last-mile facility variables collectively contribute 43.57% for predicting metro ridership. Nonlinear associations illustrate that most last-mile facilities need to reach certain thresholds to maximize metro ridership. Furthermore, bus supply and built environment factors may moderate the effects of last-mile facilities on metro ridership in a multiplex way. These research findings help urban planners identify effective ranges of different last-mile facilities and prioritize intermodal connectivity strategies in planning practice.

The prevention and control of COVID-19 in megacities is under large pressure because of tens of millions and high-density populations. The majority of epidemic prevention and control policies implemented focused on travel restrictions, which severely affected urban mobility during the epidemic. Considering the impacts of epidemic and associated control policies, this study analyzes the relationship between COVID-19, travel of residents, Point of Interest (POI), and social activities from the perspective of taxi travel. First, changes in the characteristics of taxi trips at different periods were analyzed. Next, the relationship between POIs and taxi travels was established by the Geographic Information System (GIS) method, and the spatial lag model (SLM) was introduced to explore the changes in taxi travel driving force. Then, a social activities recovery level evaluation model was proposed based on the taxi travel datasets to evaluate the recovery of social activities. The results demonstrated that the number of taxi trips dropped sharply, and the travel speed, travel time, and spatial distribution of taxi trips had been significantly influenced during the epidemic period. The spatial correlation between taxi trips was gradually weakened after the outbreak of the epidemic, and the consumption travel demand of people significantly decreased while the travel demand for community life increased dramatically. The evaluation score of social activity is increased from 8.12 to 74.43 during the post-epidemic period, which may take 3–6 months to be fully recovered as a normal period. Results and models proposed in this study may provide references for the optimization of epidemic control policies and recovery of public transport in megacities during the post-epidemic period.