Built Environment Characteristics Research Articles

Boosting Winter Green Travel: Prioritizing Built Environment Enhancements for Shared Bike Users Accessing Public Transit in the First/Last Mile Using Machine Learning and Grounded Theory

Shared bikes are widely used in Chinese cities as a green and healthy solution to address the First/Last Mile issue in public transit access. However, usage declines in cold regions during winter due to harsh weather conditions. While climate factors cannot be changed, enhancing the built environment can promote green travel even in winter. This study uses data from Shenyang, China, to investigate how built environment attributes impact the travel satisfaction of shared bike users who utilize bikes as a First/Last Mile solution to access public transit in winter cities. By employing machine learning algorithms combined with Asymmetric Impact-Performance Analysis (AIPA) and grounded theory, we systematically identify the key attributes and rank them based on their asymmetric impact and urgency of improvement. The analysis revealed 19 key attributes, 17 of which are related to the built environment, underscoring its profound influence on travel satisfaction. Notably, factors such as the profile design of cycling paths and safety facilities along routes were identified as high priorities for improvement due to their significant potential to enhance satisfaction. Meanwhile, features like barrier-free access along paths and street greenery offer substantial opportunities for improvement with more modest efforts. Our research provides critical insights into the nuanced relationship between built environment features and travel satisfaction for First/Last Mile shared bike users. By highlighting priority improvements, we offer urban planners and policymakers a framework for creating livable, sustainable environments that support green travel even in harsh winter conditions.

Exploring the built environment impacts on Online Car-hailing waiting time: An empirical study in Beijing

Waiting time is a critical metric for evaluating the quality of online car-hailing services, with studies indicating a close association between Online Car-hailing waiting time (CWT) and the urban built environment (BE). Points of interest (POI) data is widely utilized to characterize the built environment. However, quantifying the proper combination or spatial relationships between different types of POIs to capture the functional features of the built environment within a region poses a challenge. This paper proposes a framework for analyzing built environment characteristics based on a semantic probabilistic topic model utilizing Latent Dirichlet Allocation, demonstrating that integrating the building area of POIs along with the travel activity intensity at their respective locations enables a more precise identification of regional functions. Moreover, while some studies have explored the correlation between the built environment and waiting time, few have evaluated the nonlinear interactions between them. On this foundation, employing the machine learning technique XGBoost model in conjunction with online car-hailing order data, we probe the relationship between the built environment and waiting time. The research indicates that CWT is comprehensively affected by multiple factors. Taking weekday evening peak period as an example, the CWT of Central-urban area, where has dense commercial and office land use, positively correlated with commercial and office topics, while negatively correlated with educational, residential, leisure and healthcare topics, leading to a longer CWT in the Central-urban. In addition, the interaction between BEs can weakens their individual effects on CWT. A higher degree of job-residence balance can mitigate the negative impact of residential and office topics on increasing CWT, particularly in Intermediate-urban areas. Additionally, BE topics may also suppress the positive effect of road density on reducing CWT. The relationship between the BE and CWT exhibits threshold effects and a V-shaped relationship, indicating that the BE is significantly associated with CWT only within specific ranges. This correlation also exhibits a gradient pattern from the Central-urban to the Sub-urban, especially concerning office and residential topics. These outcomes elucidate the salient ranges of the built environment that exert substantial impacts on waiting time, informing strategic planning for online car-hailing dispatch and urban development to augment passenger travel satisfaction.

Assessing Non-linear Influences of Urban Layout on Driving Travel Distance: A Mumbai Case Study

Introduction This study investigates the impact of built environment factors on travel behavior in Mumbai, India, focusing on the non-linear effects on driving distances. The rapid urbanization and complex urban of Mumbai constitute present challenges for sustainable transportation, necessitating a deeper understanding of how urban planning influences travel behavior. While demographic factors have often been highlighted in travel studies, this research prioritizes the role of built environment factors. Materials and Methods The study utilized Gradient Boosting Decision Trees (GBDT) to analyze household travel survey data from Mumbai, capturing the non-linear relationships between built environment variables and driving distances. Partial dependence plots were used to visualize these effects, and the relative importance of each variable was assessed to identify key determinants of travel behavior. Results The analysis identified trip time as the most influential factor in determining driving distances, followed by built environment characteristics, such as proximity to commercial areas and intersection density. Socio-demographic factors were found to have a comparatively low impact. Non-linear relationships were observed, such as the stabilization of driving distances beyond certain thresholds of block density and proximity to bus stops. Discussion The findings challenge the traditional emphasis on demographic factors in explaining travel behavior, highlighting the significant role of urban form. The study reveals that specific built environment factors, such as accessibility and connectivity, play a crucial role in shaping driving behavior in a rapidly urbanizing city like Mumbai. These results suggest that urban planning strategies should prioritize these factors to reduce car dependency. Conclusion This study underscores the importance of the built environment in influencing travel behavior in Mumbai, particularly in reducing driving distances. The insights gained offer valuable guidance for urban planners and policymakers aiming to promote sustainable mobility in rapidly developing cities. Further research is recommended to validate and expand upon these findings in other urban contexts.

Exploring the effects of multi-dimensional geographic environment on daily sleep and physical activity based on the Actigraph data

Understanding the interplay between people’s daily sleep and physical activity and how geographic environment influences them are important for developing healthy cities. However, such research has been limited. This study aims to explore the bidirectional and nonlinear relationship between daily sleep and physical activity, and further investigate the comprehensive influences of multi-dimensional geographic environment on these health behaviors. Based on the objective data on sleep and physical activity over seven consecutive days using wrist-based accelerometers in Beijing, China, we developed a series of models to analyze the mutual influences between people’s daily sleep and physical activity, and employed the generalized additive model (GAM) to examine their potential nonlinear relationships and how geographic environment – including meteorological conditions, built environment, and social environment – influences them. The results show that sleep and physical activity exhibit notable bidirectional relationship. Moderate-to-vigorous physical activity (MVPA) is observed to improve sleep quality, but it decreases sleep duration. In contrast, total sleep time (TST) exhibits an inverted U-shaped pattern with both MVPA and total step counts, with the optimal sleep duration at 5 hours. Furthermore, meteorological factors, built environment characteristics, and social environment have significant linear or nonlinear effects on people’s daily sleep and physical activity. The outcomes of this study offer valuable insights for enhancing residents’ health and developing healthy cities.

Built environmental characteristics, physical activity in neighbourhoods of Accra, Ghana

Abstract There is increasing evidence that characteristics of the built environment can influence residents’ lifestyles, physical activity thereby influencing the development of obesity and non-communicable diseases even in low- and middle-income countries. The aim of this study is to describe the characteristics of the built and the social environment of neighbourhoods of Accra, Ghana, and to explore association between neighbourhood characteristics and the physical activity of the residents. A cross-sectional study was conducted in Accra, 12 neighbourhoods were randomly selected based on high and low values of residential density and socioeconomic status. Sociodemographic, household and neighbourhood information were collected with an adapted questionnaire of NEWS (Neighbourhood Environment Walkability Scale) for Africa. Physical activity was assessed by WHO Global Physical Activity Questionnaire and social cohesion by the Perceived Neighbourhood Social Cohesion Questionnaire. Differences between variables by neighbourhood types were analysed using Chi square test and Kruskal Wallis test. Generalized linear model was performed to determine the association between neighbourhood characteristics and residents’ physical activity. The built environmental characteristics of different neighbourhood types and the physical activity of the residents showed significant differences. The associations indicated that individuals living in high residential density neighbourhoods had increased levels of engaging in physical activities. Land use mix, street connectivity, safety, aesthetics and social cohesion of neighbourhoods showed correlation with physical activity of residents. The built environment and social characteristics of neighbourhoods can influence the lifestyle and physical activity of residents in Accra, Ghana. Key messages • High residential density neighbourhoods correlated with higher level of physical activity. • Land use, street connectivity, safety and aesthetic of neighbourhoods and social cohesion showed correlation with physical activity.

Navigating the definition of urban flooding: a conceptual and systematic review of the literature

ABSTRACT Urban flooding is a significant global challenge, posing risks to urban planners, policymakers, and communities. This paper reviews the literature to analyze how urban flooding is defined across scientific disciplines. Our objectives are to uncover the elements used to define urban flooding and evaluate how these elements can impact future research and practice. A key difficulty is the lack of a consistent, comprehensive definition that captures both physical and social dimensions of urban flooding. Current definitions often focus solely on physical aspects (e.g., rainfall, infrastructure) or social impacts, rarely integrating both. This fragmentation hinders effective flood risk management and interdisciplinary collaboration. Our contribution is a multifaceted definition incorporating spatial and social concerns, including water origins, built environment characteristics, and local community aspects. We introduce the "Urban Water Transect" concept to illustrate the continuum of flood risk across urban zones, addressing a gap in the literature. The analysis reveals that many papers discuss flooding causes without providing an explicit definition. Urban flooding is predominantly defined based on water source, imperviousness, and drainage infrastructure. Future research should adopt an interdisciplinary perspective considering both physical and social aspects, potentially transforming urban flood risk management.

Understanding the Influence of Built Environment Indicators on Transit-Oriented Development Performance According to the Literature from 2000 to 2023

The transit-oriented development (TOD) model provides benefits to urban areas in terms of transportation, the economy, society, and the environment. Given the complexity and limitations arising from the various backgrounds, objectives, and other aspects of previous studies, empirical research on specific cases has often failed to fully analyze the influence of built environment indicators on TOD performance. This paper systematically reviews and analyzes related empirical studies conducted worldwide up until June 2023. The correlations between built environment indicators and their impacts on TOD performance indicators are visualized and measured. General patterns of the influences of built environment indicators on TOD performance are summarized using social network analysis (SNA), and the key indicators are identified via cluster analysis. Finally, by analyzing the key indicators such as diversity, density, design, and distance to transit stations, as well as their associated built environment indicators, the built environment characteristics that ensure TOD performance are thoroughly examined. These insights provide a better understanding of the influences of the built environment on TOD performance, thus offering guidance for the development and application of the TOD model.

Understanding the interplay among urban public transport modes: Spatial variation and built environment effects

Research on public transport systems is continually driven by the societal benefits of transport services. However, the nuanced effects of the built environment on public transport mode shares across different modes have received little attention. Using multi-source travel data from Shenzhen, this study presents an analytical framework, which integrates the light gradient boosting machine and Shapley additive explanations, to delve into the complex interplay among public transport modes. The results reveal that most origin-destination pairs with available metro services exhibit a metro share exceeding 50%, underscoring the high attractiveness of the metro. Travel distance emerges as the primary determinant of mode share, with transport-related characteristics proving more influential than land use modifications. This study identifies nonlinear effects of the built environment on mode share, with specific thresholds for built environment characteristics suggesting fine-tuned planning strategies. Metro station densities of 2 counts/km2 correlates with increased metro share, while bus stop densities of 15 counts/km2 are associated with higher bus share. Areas within 2 km of a metro station or with bus stop densities below 15 counts/km2 tend to have higher bike shares. Specific land use ratios and high levels of land use mix are linked to increased shares of certain modes. The findings suggest optimizing bus services in relation to metro availability and offer guidance for balanced planning in promoting metro and shared mobility to achieve a sustainable transport system.

Barriers and Boundaries: How Residents Make Meaning of Segregating Built Environments

Built environments have structured urban space in racist and classist ways, contributing to residential segregation and related inequalities. Previous research has examined how perceivable built environment features become symbolic boundaries that structure both socio-spatial division and meaning-making, but little research has examined how physical barriers—features that carry symbolic meaning and limit connectivity between nearby areas—may promote both symbolic “othering” and inhibit contact between residents on either side. Drawing on ethnographic fieldwork, we examine how residents interpret and experience physical barriers and symbolic boundaries in race-, income-, and race-and-income distinct areas in Houston, Texas. We describe three typologies of barriers/boundaries (unremarkable, intrusive, and hyper-salient), demonstrating how the same barrier or boundary can have different meanings for people on either side. For physical barriers, these meanings were often mediated by residents’ actions. Our findings extend theorization of how the built environment shapes interpretations and experiences of segregation, and vice versa.

Land Use, Travel Patterns and Gender in Barcelona: A Sequence Analysis Approach

Transport systems are essential for the path toward sustainable urbanisation and the transition to more sustainable living. Recently, European cities have undergone substantial changes, and suburbanisation is posing new challenges. Suburban areas are often more affordable in terms of housing, but these neighbourhoods tend to be car-oriented. This leads to higher commuter costs, immobility, transport and time poverty, pollution, higher accident rates and a lack of social interactions. To offer sustainable mobility options to citizens, we must comprehensively understand, together with their individual characteristics, their specific mobility practices and the built environment where they live. This study is centred on the Barcelona Metropolitan Region, which has a public transport network that covers its entire area. The aim of this study is to examine the relationships between travel behaviour, transport mode use, individual characteristics and built environment characteristics in the place of residence using detailed information sources. Herein, we used data from the 2018 to 2021 annual travel survey conducted in the Barcelona region, together with land use and sociodemographic information. Our findings suggest that transport policies have encouraged sustainable mobility practices, particularly in the centre of Barcelona. Despite the positive results, considerable disparities exist between the inner and outer city, with a notable decline in sustainable mobility practices in the latter, due to the uneven distribution of basic services and uneven provision of public transport, together with lower density areas. Our results demonstrate that this uneven distribution reduces the available sequence profiles of inhabitants. In conclusion, the promotion of sustainable mobility policies necessitates further advances in transport, city and land-use planning that consider equity, gender, the socioeconomic profiles of citizens and mixed urban planning.

How did COVID-19 case distribution associate with the urban built environment? A community-level exploration in Shanghai focusing on non-linear relationship.

Several associations between the built environment and COVID-19 case distribution have been identified in previous studies. However, few studies have explored the non-linear associations between the built environment and COVID-19 at the community level. This study employed the March 2022 Shanghai COVID-19 pandemic as a case study to examine the association between built-environment characteristics and the incidence of COVID-19. A non-linear modeling approach, namely the boosted regression tree model, was used to investigate this relationship. A multi-scale study was conducted at the community level based on buffers of 5-minute, 10-minute, and 15-minute walking distances. The main findings are as follows: (1) Relationships between built environment variables and COVID-19 case distribution vary across scales of analysis at the neighborhood level. (2) Significant non-linear associations exist between built-environment characteristics and COVID-19 case distribution at different scales. Population, housing price, normalized difference vegetation index, Shannon's diversity index, number of bus stops, floor-area ratio, and distance from the city center played important roles at different scales. These non-linear results provide a more refined reference for pandemic responses at different scales from an urban planning perspective and offer useful recommendations for a sustainable COVID-19 post-pandemic response.

Addressing ‘difficulty in dining’ among older adults: optimizing community senior dining halls from external and internal built environments

Many developed and developing countries, including China, are facing the challenges of the aging population. One such challenge is the difficulty for some older adults to access nutritious hot meals to maintain their health and well-being. Community-based senior dining halls (CSDHs) providing low-cost meals for older adults are a widespread model across China and neighboring countries, which aim to cater to the dining needs of older adults within local communities. However, most CSDHs face financial difficulties due to low dining volume. Hence, understanding how built environment characteristics are associated with the dining volume can potentially increase the dining volume and financial sustainability of CSDHs in the long run. This study analyzed the association between the dining volume and both the external and internal built environment of 50 CSDHs in Guangzhou, China. By using multiple linear regression models with three different buffer sizes (400 m, 600 m, and 800 m), we found that 1) among the external built environment characteristics, floor area ratio (FAR) and distance to the nearest subway station are negatively linked to the dining volume; 2) among the internal built environment characteristics, floor level is negatively associated with dining volume, whereas availability of dine-in seating and water dispenser, and number of seats are positively associated with it. Hence, a well-designed external and internal built environment of CSDHs may increase dining volume, enhance social interaction among older adults, and support community-based senior care.

Built Environment Influences on Emotional State Loneliness among Young Adults during Daily Activities: An Experience Sampling Approach

Loneliness among young adults is a growing concern worldwide, posing serious health risks. While the human ecological framework explains how various factors such as socio-demographic, social, and built environment characteristics can affect this feeling, still, relatively little is known about the effect of built environment characteristics on the feelings of loneliness that young people experience in their daily life activities. This research investigates the relationship between built environment characteristics and emotional state loneliness in young adults (aged 18–25) during their daily activities. Leveraging the Experience Sampling Method, we collected data from 43 participants for 393 personal experiences during daily activities across different environmental settings. The findings of a mixed-effects regression model reveal that built environment features significantly impact emotional state loneliness. Notably, activity location accessibility, social company during activities, and walking activities all contribute to reducing loneliness. These findings can inform urban planners and municipalities to implement interventions that support youngsters’ activities and positive experiences to enhance well-being and alleviate feelings of loneliness in young adults. Specific recommendations regarding the built environment are (1) to create spaces that are accessible, (2) create spaces that are especially accessible by foot, and (3) provide housing with shared facilities for young adults rather than apartments/studios.

A specialized inclusive road dataset with elevation profiles for realistic pedestrian navigation using open geospatial data and deep learning

Built environment characteristics can greatly influence pedestrians' route choices with factors beyond distance, such as accessibility, convenience, safety, and aesthetics, playing crucial roles. Although current navigation apps, such as Google Maps and Waze, have successfully provided driving directions, their navigation services are insufficient and sometimes unrealistic for addressing pedestrians' needs, largely due to the lack of dedicated pedestrian networks and the associated navigation algorithms. To address the research gaps, this paper proposes a novel approach that integrates freely available geospatial data and computer vision technology to create a specialized inclusive network dataset for outdoor pedestrian navigation. Moreover, a pedestrian navigation algorithm is developed to generate more practical “shortest” and “alternative” paths by incorporating various sidewalk attributes. We applied the method to create a pedestrian navigation network in Las Vegas. SpaceNet's open imagery dataset was used to extract Las Vegas's road networks. A virtual audit process assessed the visual and operational properties of the sidewalk networks using Google street-level images, evaluating factors including sidewalk presence, widths, surface types and conditions, missing curb ramps, greenery, protection from weather conditions, and lighting. Google Earth's open elevation data were used to analyze road elevation profiles as meaningful 3D indicators of sidewalk accessibility for wheelchair users. Further, additional geometric properties of the network, including road curviness, proximity to road intersections, and directional changes, were detected and analyzed. A navigation experiment conducted with individuals having varying mobility abilities, including regular pedestrians, older adults, and wheelchair users demonstrated the effectiveness of the newly developed network and algorithm in meeting the diverse needs of pedestrians.

Expanding the associations between built environment characteristics and residential mobility in high-density neighborhood unit

Global trend of urbanization has led to frequent residential mobility and urban shrinkage issues. Planners and policy makers advocate for enhancing built environment quality of neighbourhood units to address these issues. Although the impact factors and degree of the influence of the built environment on residential mobility have been studied, the nonlinear effects at the neighbourhood level, and the relative importance when considered alongside other factors, remain unclear. In this study, the central area of Nanjing using mobile phone signalling data, the 5Ds framework, machine learning algorithms, and interpretable model Shapley Additive exPlanations (SHAP) are examined. The finding shows that (1) economy and neighbourhood ties are still key drivers of relocation. (2) Optimizing highly accessible road network for short-distance travel and developing low-density urban mode has become significant methods to attract relocators, interacting with other factors to influence residential mobility. (3) High-quality neighbourhood design, diverse amenities, and proximity to natural landscapes increase willingness to relocate, (4) while religion, socio-demographics, and large-scale transportation accessibility have minimal impact. The study offers four urban development recommendations to help municipal planners and policy makers create liveable cities and cohesive communities, providing essential insights for early or renewal stage urban planning stages.

How do urban block built environments affect older adults' walking activities and health effects: a case study in Nanjing, China.

Research on the relationship between microscale built environments and physical activity among older adults in densely populated old urban areas with high aging rates is scarce. Particularly, the relationship between urban block land-use pattern and older adults' walking activities have not yet been completely understood. This study examined the daily walking habits and socioeconomic attributes of older adults in 17 blocks with different built environment characteristics in Nanjing City, China. A multi-layer linear model was used to quantitatively analyse the mechanism underlying the effects of various factors on the health of older adults. The result shows significant positive correlation between neighborhood walkability and the enhancement of walking frequency and activity. For every 10% increase in pedestrian road connectivity and land use, the walking activity volume increased by 22.4 and 12%, and the BMI increased by 9.1 and 7.3% toward the standard range, respectively. For every 10% increase in distance between residence and plaza and park, the walking activity volume decreased by 5.4 and 3.2%, and BMI decreased by 9.9 and 6.3%, respectively. For every 10.4% increase in land mixed-use rate and per capita green area, BMI increased by 19.4%. Furthermore, higher household income and number of family members have significant correlation with walking activities and health effects. Moreover, the block-scale built environment and walking activities jointly affected health, with a cross-functional relationship between multiple factors.

WebMRT: An online tool to predict summertime mean radiant temperature using machine learning

Mean Radiant Temperature (Tmrt) is the most critical atmospheric variable influencing outdoor human thermal exposure and comfort in hot, dry environments. However, accurately quantifying Tmrt requires time-consuming field measurements with expensive equipment or complex, resource-intensive computations. We introduce WebMRT, an online tool to predict Tmrt using a data-driven approach. It features an intuitive interface using air temperature, shading status, and built environment features as predictors of Tmrt for a user-selected summer day, time, and location. Utilizing a tree-based ensemble model, WebMRT is trained on state-of-the-art human-biometeorological data collected by MaRTy using LightGBM after evaluating its performance against several candidate machine learning regressors. Feature engineering was applied to the day and time input, and two additional temporal features were derived: ‘Solar Altitude’ and ‘Minutes-from-Sunrise’. These inputs are integrated into the user interface, emphasizing simplicity and easy access for users at the frontend. After training the regressor on MaRTy datasets and employing k-fold cross-validation with ten folds, the model demonstrated strong predictive power (R2=0.92) with acceptable error (RMSE=3.43, MAPE=5.33) and bias (MBE=0.20). WebMRT also features optional fisheye photo uploads, processed using transfer learning techniques for image segmentation, further enhancing the tool's predictive accuracy, user experience, and applications towards climate action decision-making processes.

Assessing the built environment through photographs and its association with obesity in 21 countries: the PURE Study

The built environment can influence human health, but the available evidence is modest and almost entirely from urban communities in high-income countries. Here we aimed to analyse built environment characteristics and their associations with obesity in urban and rural communities in 21 countries at different development levels participating in the Prospective Urban and Rural Epidemiology (PURE) Study. Photographs were acquired with a standardised approach. We used the previously validated Environmental Profile of a Community's Health photo instrument to evaluate photos for safety, walkability, neighbourhood beautification, and community disorder. An integrated built environment score (ie, a minimum of 0 and a maximum of 20) was used to summarise this evaluation across built environment domains. Associations between built environment characteristics, separately and combined in the integrated built environment score, and obesity (ie, a BMI >30kg/m2) were assessed using multilevel regression models, adjusting for individual, household, and community confounding factors. Attenuation in the associations due to walking was examined. Analyses include 143 338 participants from 530 communities. The mean integrated built environment score was higher in high-income countries (13·3, SD 2·8) compared with other regions (10·1, 2·5) and urban communities (11·2, 3·0). More than 60% of high-income country communities had pedestrian safety features (eg, crosswalks, sidewalks, and traffic signals). Urban communities outside high-income countries had higher rates of sidewalks (176 [84%] of 209) than rural communities (59 [28%] of 209). 15 (5%) of 290 urban communities had bike lanes. Litter and graffiti were present in 372 (70%) of 530 communities, and poorly maintained buildings were present in 103 (19%) of 530. The integrated built environment score was significantly associated with reduced obesity overall (relative risk [RR] 0·58, 95% CI 0·35-0·93; p=0·025) for high compared with low scores and for increasing trend (0·85, 0·78-0·91; p<0·0001). The trends were statistically significant in urban (0·85, 0·77-0·93; p=0·0007) and rural (0·87, 0·78-0·97; p=0·015) communities. Some built environment features were associated with a lower prevalence of obesity: community beautification RR 0·75 (95% CI 0·61-0·92; p=0·0066); bike lanes RR 0·58 (0·45-0·73; p<0·0001); pedestrian safety RR 0·75 (0·62-0·90; p=0·0018); and traffic signals RR 0·68 (0·52-0·89; p=0·0055). Community disorder was associated with a higher prevalence of obesity (RR 1·48, 95% CI 1·17-1·86; p=0·0010). Community built environment features recorded in photographs, including bike lanes, pedestrian safety measures, beautification, traffic density, and disorder, were related to obesity after adjusting for confounders, and stronger associations were found in urban than rural communities. The method presents a novel way of assessing the built environment's potential effect on health. Population Health Research Institute, Hamilton Health Sciences Research Institute, Heart and Stroke Foundation of Ontario, Canadian Institutes of Health Research's Strategy for Patient Oriented Research, Ontario Support Unit, Ontario Ministry of Health and Long-Term Care, AstraZeneca, Sanofi-Aventis, Boehringer Ingelheim, Servier, and GlaxoSmithKline.

Estimating casualties from urban fires: A focus on building and urban environment information

This study developed two prediction models for urban fire occurrence and related casualties via a fire accident dataset from Seoul, South Korea, from 2017 to 2021. Our models exhibit improved predictive performance by incorporating built environment features, such as building characteristics and the urban context, alongside weather and demographic data. This approach showed improved predictive performance suitable for public health implementation. Compared with the weather- and demographic-only models, our models had an 18.1 % greater fire occurrence prediction accuracy and a 10.4 % greater casualty prediction accuracy. Major variables affecting fire occurrence include building characteristics, e.g., the floor area ratio (FAR), building age, and commercial building number. Important features affecting casualty occurrence include demographic aspects, e.g., income level and weather, and network-based features, e.g., road connectivity and fire station proximity. These findings suggest that fire prevention strategies and fire casualty prevention strategies may need to differ. Furthermore, we identify high-risk zones by conducting spatial analysis and fire risk and casualty prediction on all buildings by applying our models to Seoul's Gangnam District. These contributions can promote safe and healthy urban environments by improving fire risk prediction accuracy and providing important insights into urban planning for appropriate urban fire accident response and prevention.

Exploring the effect of built environment on spatiotemporal evolution of traffic congestion using a novel GTWR model: a case study of Hefei, China

ABSTRACT Understanding the impact of the built environment on traffic congestion can provide reliable references for alleviating traffic congestion. Previous research has explored traffic congestion evolution but often overlooks two key aspects: the frequency of congestion state updates and the spatial-temporal impact of built environment features. Based on data from Hefei, China, we propose a novel geographically and temporally weighted regression (GTWR) model that integrates temporal variables and spatial grids into the traditional GWR model. The empirical results show that public facilities most significantly impact congestion during morning peak hours, scenic spots during afternoon peaks, and motorcycle services in the evening. The study also reveals the rules for the spatiotemporal impact of the built environment on traffic congestion. Finally, the comparison of models shows that the GTWR model outperforms the OLS and GWR models. The findings can guide traffic managers in creating targeted strategies to enhance transportation system efficiency.