Built Environment Factors Research Articles

Built environment, driving errors and violations, and crashes in naturalistic driving environment

Driving errors and violations are highly relevant to the safe systems approach as human errors tend to be a predominant cause of crash occurrence. In this study, we harness highly detailed pre-crash Naturalistic Driving Study (NDS) data 1) to understand errors and violations in crash, near-crash, and baseline (no event) driving situations, and 2) to explore pathways that lead to crashes in diverse built environments by applying rigorous modeling techniques. The “locality” factor in the NDS data provides information on various types of roadway and environmental surroundings that could influence traffic flow when a precipitating event is observed. Coded by the data reductionists, this variable is used to quantify the associations of diverse environments with crash outcomes both directly and indirectly through mediating driving errors and violations. While the most prevalent errors in crashes were recognition errors such as failing to recognize a situation (39 %) and decision errors such as not braking to avoid a hazard (34 %), performance errors such as poor lateral or longitudinal control or weak judgement (8 %) were most strongly correlated with crash occurrence. Path analysis uncovered direct and indirect relationships between key built-environment factors, errors and violations, and crash propensity. Possibly due to their complexity for drivers, urban environments are associated with higher chances of crashes (by 6.44 %). They can also induce more recognition errors which correlate with an even higher chances of crashes (by 2.16 % with the “total effect” amounting to 8.60 %). Similar statistically significant mediating contributions of recognition errors and decision errors near school zones, business or industrial areas, and moderate residential areas were also observed. From practical applications standpoint, multiple vehicle technologies (e.g., collision warning systems, cruise control, and lane tracking system) and built-environment (roadway) changes have the potential to reduce driving errors and violations which are discussed in the paper.

Built Environment Factors Influencing Prevalence of Hypertension at Community Level in China: The Case of Wuhan

This paper studies the correlation between built environment factors and the prevalence of hypertension in Wuhan, a typical city in central China. Data were obtained from a regional epidemiological database, which is the 2015 Epidemiological Survey of people under 65 years in 144 communities. The prevalence of hypertension was analyzed in five components based on the WHO framework (land use, transport, accessibility, green space, and socioeconomic status). Results indicated built environment factors have significant correlations with the prevalence of hypertension (p < 0.01). The road network density, gymnasium cost, income, medical facilities cost, walkability index, and land use mix (LUM) were statistically significant. Other indicators did not pass the significance test. The spatial models fit better than the multivariate linear model.

Examining the associations between perceived built environment and active travel, before and after the COVID-19 outbreak in Shiraz city, Iran

This study aims to evaluate the effects of the COVID-19 pandemic and policies to control the outbreak such as quarantine, jobs closures, and traffic restrictions on active travel of Central Businesses District (CBD) residents in comparison with the non-CBD residents of Shiraz, Iran; and examine the relationship between perceived built environment factors and active travel in the pre- and post-outbreak. The results indicate that the most effective individuals factors on active travel are bicycle and car ownership, and built environment characteristics are walkability, bikeability, security, aesthetics, traffic calming, intersections safety, land uses diversity and density, destination accessibility, street pattern, and bike-sharing infrastructures. Also, the average walking and cycling time of the CBD residents before and after the outbreak is more than that of the non-CBD residents, which is due to the quality of built environment factors in the CBD. A built environment with mixed, diverse, dense and accessible land uses, as well as safe and secure cycling and walking routes have major effects on active travel in the crisis. Hence, it is suggested that policymakers take action to make the environment more people-friendly to maintain citizens' mobility in the critical situation, when many travel modes have lost their efficiency.

Exploring the effect of the built environment, weather condition and departure time of travel on mode choice decision for different travel purposes: Evidence from Isfahan, Iran

A growing number of researchers have investigated the role of key factors that affect transport mode choice. Scant studies, however, have tried to incorporate the built environment factors at origin and destination, weather condition, departure time, and different trip purposes into mode choice models. To address these shortcomings, we developed four multinomial logit (MNL) models to analyze travel mode choice decision for different purposes in the context of a developing country, Iran. Travel data drawn from household travel survey conducted by Isfahan Municipality in 2015 and weather parameters were retrieved from five stations located inside the city. The results of models reveal some important insights. While entropy index and average block size stronglyinfluence transport mode decisions, other built environment factors have weak associations with transport modes. Further, low temperature and low relative humidity decrease the probability of transit, motorcycle and bicycle usage over automobile. The impact of weather condition on discretionary trips is stronger than that of work trips. Apart from mentioned variables, socio-demographic characteristics and departure time of travel are other important variables. Findings of this paper indicate that nonphysical strategies in tandem with land use policies should be considered based on local condition.

Exploring both home-based and work-based jobs-housing balance by distance decay effect

The relationship between built environment and jobs-housing balance has been discussed at either residence or workplace in literature. Less attention has been paid to both perspectives simultaneously. Built environment may have different relationships with home-based and work-based jobs-housing balance from a single perspective. However, the difference and to what extent is still not clear. It is significant in providing area-based urban planning suggestions. This study applied one-month cellular cellphone data in Guangzhou, China. It proposed a new jobs-housing balance measurement by estimating decay parameters of commuting distance distributions. Home-based and work-based jobs-housing balance was differentiated by aggregating commuting trips into departure and arrival locations. A linear regression model was applied to examine the correlation effect of built environment factors on home-based and work-based jobs-housing balance respectively, and a classification and regression trees (CART) model was applied to explore the combined effect of built environment on jobs-housing balance patterns. Results have shown that built environment has different correlations with home-based and work-based jobs-housing balance. For example, the POI mixture improves work-based jobs-housing balance but worsens home-based jobs-housing balance. Another important finding is that a moderate jobs-housing ratio does not ensure jobs-housing balance. In contrast, jobs-housing is imbalanced at well-developed communities.

Visualizing the Effect of Built Environment on COVID-19 Transmission: A Community-Level Study in Wuhan, China

With the outbreak of a novel respiratory coronavirus disease (COVID-19) over the world, restriction of human mobility plays an important role to reduce the potential for the spread of virus. People are restricted for limited activities in local communities where the built environment in communities serve as a mediated pathway for the virus transmission. Our study aims to examine to what extent built environment factors explain COVID-19 transmission at the community level. Drawing on primary data collected from 648 communities in Wuhan, China, we employed a geographically weighted regression and a 3D mapping approach to examine the relationship between COVID-19 confirmed cases and three built environment factors including green coverage, property price, and underground parking. We find that green coverage and property price explain COVID-19 transmission better in communities with a larger amount of confirmed cases

Effect of Built Environment Factors on Pedestrian Safety in Portuguese Urban Areas

This paper identifies and analyzes variables that influence pedestrian safety based on the definition of models of pedestrian crash frequency for urban areas in Portugal. It considers three groups of explanatory variables, namely: (i) built environment; (ii) pedestrian infrastructure, and (iii) road infrastructure, as well as exposure variables combining pedestrian and vehicular traffic volumes. Data on the 16 variables considered were gathered from locations in the counties of Braga and Guimarães. The inclusion of pedestrian infrastructure variables in studies of this type is an innovation that allows for measuring the impacts of the dimensions recommended for this type of infrastructure and assessing the implementation of policies to support the mobility of vulnerable users, especially pedestrians. Examples of such variables are unobstructed space for pedestrian mobility and the recommendable distance separating regulated crossings. Zero-Truncated Negative Binomial Regression Models (ZTNB) and Generalized Estimation Equations (GEE) are used to develop crash prediction models. Results show that in addition to the variables identified in similar studies such as carriageway width, other statistically significant variables like longitudinal slope and distance between crosswalks have a negative influence on pedestrian safety. On-street parking places, one-way streets, and the existence of raised medians have an opposite contribution to safety.

To e-bike or not to e-bike? A study of the impact of the built environment on commute mode choice in small Chinese city

The use of electric bikes (e-bikes) is attracting increasing attention from researchers and policymakers as a way to promote sustainable transportation. However, knowledge about the built environment factors that influence e-bike use is lacking. In China, most evidence on e-bikes and travel behavior stems from big cities; there is much less evidence concerning small cities and their adjacent rural areas. Using travel data collected in a small Chinese city (Ganyu), the present research explores the impact of the built environment around residential and work locations on individuals’ commute mode choice, with a particular focus on e-bike use. Consistent with the few previous studies on travel behavior in small Chinese cities, we find that most residents of Ganyu commute only short distances and that the e-bike is the primary mode for their daily commutes. The results of a nested logit model show that e-bike use is more popular among females and low-income groups, and that certain built environment characteristics at the work location promote e-bike use. Moreover, the built environment in different geographical contexts has different influences on commute mode choice. In particular, the presence of city/town roads without bike lanes at work locations promotes e-bike use among rural residents but much less so among urban residents.

The characteristics of leisure activities and the built environment influences in large-scale social housing communities in China: the case study of Shanghai and Nanjing

ABSTRACT In large-scale residential communities for social housing in China where low-income groups gather, public green space is the most dominant public leisure space for residents. Therefore, researches on the characteristics of leisure activities in these communities and the built environment factors that exert an impact on leisure activities would provide an important theoretical basis for future reform of the public space of large-scale social housing communities in China. The study uses the method of behavioral mapping to obtain the data of the residents’ leisure activities in two typical large-scale social housing communities in Nanjing and Shanghai. This study employs methods such as syntactic modeling and site investigation to collect data of environmental factors. This study processes them with the multi-factor linear regression model to explore the “environment-behavior” relationship. The results indicate that the leisure activities of middle and low-income groups in large-scale communities follow a certain pattern in terms of the changes in time axis, sex ratio, location, willingness to socialize. Integrating the impacts of spatial structure and site elements with three groups of variables – spatial configuration, planning conditions and site elements – can explain approximately 70% of the leisure activities distribution.

How Is Urban Greenness Spatially Associated with Dockless Bike Sharing Usage on Weekdays, Weekends, and Holidays?

Dockless bike sharing plays an important role in residents’ daily travel, traffic congestion, and air pollution. Recently, urban greenness has been proven to be associated with bike sharing usage around metro stations using a global model. However, their spatial associations and bike sharing usage on public holidays have seldom been explored in previous studies. In this study, urban greenness was obtained objectively using eye-level greenness with street-view images by deep learning segmentation and overhead view greenness from the normalized difference vegetation index (NDVI). Geographically weighted regression (GWR) was applied to fill the research gap by exploring the spatially varying association between dockless bike sharing usage on weekdays, weekends, and holidays, and urban greenness indicators as well as other built environment factors. The results showed that eye-level greenness was positively associated with bike sharing usage on weekdays, weekends, and holidays. Overhead-view greenness was found to be negatively related to bike usage on weekends and holidays, and insignificant on weekdays. Therefore, to promote bike sharing usage and build a cycling-friendly environment, the study suggests that the relevant urban planner should pay more attention to eye-level greenness exposure along secondary roads rather than the NDVI. Most importantly, planning implications varying across the study area during different days were proposed based on GWR results. For example, the improvement of eye-level greenness might effectively promote bike usage in northeastern and southern Futian districts and western Nanshan on weekdays. It also helps promote bike usage in Futian and Luohu districts on weekends, and in southern Futian and southeastern Nanshan districts on holidays.

Spatial heterogeneity in distance decay of using bike sharing: An empirical large-scale analysis in Shanghai

Distance decay is a vital aspect for modeling spatial interactions of human movements and an indispensable input for land use planning and travel demand prediction models. Although many studies have investigated the usage demand of bike-sharing systems in an area, research investigating the distance decay patterns of using dockless bike-sharing systems (DLBS) from a spatially heterogeneous perspective based on large-scale datasets is lacking. This study firstly utilizes massive transaction record data from DLBS in Shanghai of China and online map navigator Application Programming Interface to empirically estimate the distance decay patterns of using DLBS and reveal the spatial heterogeneity in distance decay of using DLBS across different urban contexts. Afterward, this study examines the mechanism of spatial heterogeneity in distance decay, leveraging multiple data resources including Point of Interest (POI) data, demographic data, and road network data. The associations among the distance decay of using DLBS with built environment factors are investigated by multiple linear regression. Results indicate that factors such as population density, land use entropy, branch road density, and metro station density are significantly related to larger distance decay of using DLBS, while factors such as commercial land use ratio, industrial land use ratio, and motorway density are significantly linked to smaller distance decay in Shanghai. Lastly, we further employ an adaptative geographically weighted regression to investigate the spatial divergences of the influences of built environment factors on distance decay. Results reveal notably distinct and even inverse influences of a built environment factor on the distance decay of using DLBS in different urban contexts. The findings provide insights into the distance decay patterns of using DLBS in different urban contexts and their interactions with the built environment, which can support accurate planning and management of sustainable DLBS as per specific urban characteristics.

Multilevel Risk Factors for Weight Change after Breast Cancer Diagnosis Among Black Women

Abstract Background: Weight gain after breast cancer diagnosis increases the risk of mortality. African American/Black breast cancer survivors are more likely to have excess body weight than their White counterparts, which may contribute to their higher mortality rate. Emerging evidence suggests that post-diagnosis weight gain may result from multilevel determinants. However, no study has investigated the multilevel characteristics among Black breast cancer survivors. Objective: To evaluate associations between individual-level factors and neighborhood social and built environment factors with weight change after breast cancer diagnosis among Black women. Methods: We evaluated associations of interest among 785 women enrolled in the Women's Circle of Health Follow-Up Study (WCHFS), a longitudinal study of Black breast cancer survivors in New Jersey. Weight change was primarily based on measurements at baseline and follow-up visits (Median: 10.3 and 23.2 mo. since diagnosis, respectively). Participants were grouped into categories of stable weight (52.4%), ≥3% weight loss (20.0%), and ≥3% weight gain (27.6%). Using multivariate- adjusted multinomial logistic regression and multilevel multinomial logistic regression, we evaluated relative risk ratios (RRRs) for associations between multilevel factors and post-diagnosis weight change category. Results: Black breast cancer survivors who were older at diagnosis, had higher household income, post-menopausal status, and higher baseline BMI were less likely to gain weight compared to women with stable weight. Former smoking, higher tumor stage, and chemotherapy were associated with increased relative risk of weight gain (e.g. RRR-chemo: 1.45, 95% CI: 1.01, 2.08). Black women residing in neighborhoods in the highest tertile for density of walkable destinations had a decreased relative risk of post- diagnosis weight gain (e.g. RRR-T3 highest density vs. T1 lowest: 0.39, 95% CI: 0.20, 0.75), while those residing in neighborhoods with higher density of fast food restaurants had increased relative risk of weight gain (RRR-T3 highest density vs. T1 lowest: 1.94, 95% CI: 1.23, 3.05). Conclusion: Both individual and neighborhood factors may influence the risk of weight gain among Black women after breast cancer diagnosis.

Towards low carbon cities: A machine learning method for predicting urban blocks carbon emissions (UBCE) based on built environment factors (BEF) in Changxing City, China

Predicting urban blocks carbon emissions (UBCE) accurately based on built environment factors (BEF) is an effective way to reduce UBCE and alleviate urban heat islands (UHI) from the perspective of urban planning. At an urban level, this study collected various sources of data and proposed a machine learning method (Back Propagation Neural Network - BPNN) to predict different functions of UBCE by BEF in Changxing, a representative small city in China. The study found that UBCE can be significantly affected by BEF such as density, function, and morphology. The BPNN has a good prediction performance on different functions of UBCE, and the mean absolute percentage error (MAPE) is stable within 10 %–20 %. The prediction results showed that the average value of different functions of UBCE presented an obvious variation. The carbon emission map showed that the high UBCE are mainly clustered in the middle-east and south of the central city of Changxing. By comparison with other studies, the accuracy of this method was proved. This method could be applied to predict carbon emissions of the urban planning scheme. Optimizing the BEF of urban blocks can play key roles in the reduction of carbon emissions and then alleviation of the UHI.

Direct and indirect effects of background variables on active commuting: Mediating roles of satisfaction and attitudes

IntroductionUnderstanding the importance of factors influencing the choice of active transportation modes (i.e. walking/cycling) may be misleading if we only concentrate on the direct and independent impacts of background variables (i.e. demographic, social environment and built environment factors). We hypothesised that background variables not only have a direct effect but also indirectly (through mediating roles of satisfaction with active travel and attitudes) associate with the choice of active mode. Previous studies have overlooked such indirect effects. Our contribution to the literature is to investigate direct and indirect effects of background variables on active commuting in university trips through developing an integrated structural model. MethodThe data were collected through a questionnaire and in-person interviews with 682 students of Isfahan University, Iran. Structural equation modelling (SEM) was applied to test the hypothesised model. ResultsThe results of SEM showed that the important psychological aspects of travel (satisfaction and attitudes) mediated the link between background variables and active commuting. SEM-analysis highlighted that students who reported more positive attitudes and were satisfied with walking and cycling were more likely to commute by active modes. Female and working students were more stressed and hurried when commute by active modes, and demonstrated unfavourable attitudes towards walking and cycling, which was associated with diminished choice of active modes. Besides, students who travelled long distances or owned a private car were less likely to walk and ride bicycles. ConclusionsPolicymakers could promote the culture of active travel in the university campus through training and advertising in the mass media or launching attitude campaigns at the university scale to mitigate the current restrictions. Policymakers could also strengthen infrastructure for cycling and walking to improve student satisfaction and attitudes towards active travel modes in the study area.

Older Adults’ Views of the Connections between Neighbourhood Built Environment and Health in Singapore

Increasing life expectancy in cities has heightened the urgency to understand how built environments affect the various health dimensions of the ageing population. This study explores community-dwelling older adults’ (n = 80; aged 52 and above) perceptions of the links between neighbourhood built environment and their physical, social and mental health in Singapore. Content analysis of focus group discussions reveals that Singaporean older adults associate different but overlapping built environment factors with the physical, social and mental dimensions of health. Safety, amenities, pedestrian-friendly spaces, transport infrastructure, and social and public spaces are most frequently identified with older adults’ health. In particular, safety and pedestrian-friendly spaces are considered most important to older adults’ physical health while safety and amenities are most important for social health, and aesthetics and wayfinding for mental health. These findings underscore the diversities and complexities of everchanging person-environment fit that must be considered while planning, designing and/or retrofitting neighbourhoods and outdoor spaces in high-density Asian cities.

A national examination of neighborhood socio-economic disparities in built environment correlates of youth physical activity.

Adolescents in the U.S. do not meet current physical activity guidelines. Ecological models of physical activity posit that factors across multiple levels may support physical activity by promoting walkability, such as the neighborhood built environment and neighborhood socioeconomic status (nSES). We examined associations between neighborhood built environment factors and adolescent moderate-to-vigorous physical activity (MVPA), and whether nSES moderated associations. Data were drawn from a national sample of adolescents (12-17years, N=1295) surveyed in 2014. MVPA (minutes/week) were estimated from self-report validated by accelerometer data. Adolescents' home addresses were geocoded and linked to Census data from which a nSES Index and home neighborhood factors were derived using factor analysis (high density, older homes, short auto commutes). Multiple linear regression models examined associations between neighborhood factors and MVPA, and tested interactions between quintiles of nSES and each neighborhood factor, adjusting for socio-demographics. Living in higher density neighborhoods (B(SE): 9.22 (2.78), p=0.001) and neighborhoods with more older homes (4.42 (1.85), p=0.02) were positively associated with adolescent MVPA. Living in neighborhoods with shorter commute times was negatively associated with MVPA (-5.11 (2.34), p=0.03). Positive associations were found between MVPA and the high density and older homes neighborhood factors, though associations were not consistent across quintiles. In conclusion, living in neighborhoods with walkable attributes was associated with greater adolescent MVPA, though the effects were not distributed equally across nSES. Adolescents living in lower SES neighborhoods may benefit more from physical activity interventions and environmental supports that provide opportunities to be active beyond neighborhood walkability.

Equitable? Exploring ridesourcing waiting time and its determinants

Waiting time (WT) is an important measure that can reflect accessibility to ridesourcing service. Previous studies explored the effects of built environment factors on WT based on estimated WT but did not control for trip-level characteristics, which may lead to biased parameter estimation. Thus, we further study this topic by using the actual WT recorded by the RideAustrin platform and considering trip-level variables. The single-level and multilevel proportional hazards models are constructed, and model comparison shows that the multilevel model performs better. We find that waiting time is positively correlated with trip-level characteristics such as traffic conditions, surge multiplier, and rainy weather. Regarding built environment factors, WT is positively related to distance to CBD and negatively related to road density, transit stop density, and land-use entropy. WT is also higher in areas with a high fraction of Hispanic/Latino and Black residents but lower in areas of low income.

Exploring nonlinear effects of the built environment on ridesplitting: Evidence from Chengdu

Ridesplitting, a form of ridesourcing services that matches riders with similar routes to the same driver, is a high occupancy travel mode that can bring considerable benefits. However, the current ratio of ridesplitting in the ridesourcing services is relatively low and its influencing factors remain unrevealed. Therefore, this paper uses a machine learning method, gradient boosting decision tree (GBDT) model, to explore the nonlinear effects of built environment on the ridesplitting ratio of origin–destination pairs (census tract to census tract). The GBDT model also provides the relative importance ranking of all the built environment factors. The results indicate that distance to city center, land use diversity and road density are the key influencing factors of ridesplitting ratio. In addition, the non-linear thresholds of built environment factors are identified based on partial dependence plots, which could provide policy implications for the government and transportation network companies to promote ridesplitting.

Environmental and social benefits, and their coupling coordination in urban wetland parks

As important and typical urban blue-green spaces, urban wetland parks (UWPs) have a variety of environmental and social benefits. However, existing studies usually focus on a particular UWP benefit and factors affecting it, so there is no comprehensive evaluation method and framework for evaluating UWP benefits and guiding their development. Through field survey, geographic information system (GIS), text analysis and an AHP-entropy method, environmental and social benefits of 38 UWPs were determined in Wuhan, China. Eight geographically weighted regression (GWR) models and three coupling coordination models were constructed to explore park attributes and built environment factors that affect UWP benefits, and to evaluate UWP coupling coordination.GWR models explained UWP environmental (Adj. R2 = 0.493∼0.777) and social (Adj. R2 = 0.419∼0.709) benefits well. Environmental benefits were more affected by park attributes, while social benefits were more affected by the built environment. Most UWP coupling coordination was medium (with CCD from 0.55 to 0.75, and an average of 0.630), which is caused by insufficient environmental benefits or uncoordinated environmental and social benefits. We built a framework for diagnosis, analysis, and improvement that can quickly evaluate UWP benefits, put forward development suggestions to promote UWP coordinated development so that UWPs will bring more well-being to residents. In the future, UWP benefit evaluation frameworks can be further enriched by more potential benefits and and promoted to improve more extensive urban blue-green spaces.

Quantifying economic benefits from free-floating bike-sharing systems: A trip-level inference approach and city-scale analysis

Despite many qualitative discussions about the benefits of free-floating bike-sharing systems (FFBS), high-resolution and quantitative assessments about the economic benefits of FFBS for users are absent. This study proposes an innovative trip-level inference approach for quantifying the economic benefits of FFBS, leveraging massive FFBS transaction data, the emerging multimodal routing Application Programming Interface from online navigators and travel choice modeling. The proposed approach is able to analyze the economic benefit for every single bike-sharing trip and investigate the spatiotemporal heterogeneity in the economic benefits from FFBS. An empirical analysis in Shanghai is conducted using the proposed approach. The estimated saved travel time, cost, and economic benefit due to using FFBS per trip are estimated to be 9.95 min, 3.64 CNY, and 8.68 CNY-eq, respectively. The annual saved travel time, cost, and economic benefits from FFBS in Shanghai are estimated to be 17.665 billion min, 6.463 billion CNY, and 15.410 billion CNY-eq, respectively. The relationships between economic benefits from FFBS and built environment factors in different urban contexts are quantitatively examined using Multiple Linear Regression to explain the spatial heterogeneity in the economic benefits of FFBS. The outcomes provide a useful tool for evaluating the benefits of shared mobility systems, insights into the users’ economic benefit from using FFBS from per-trip, aggregated and spatial perspective, as well as its influencing factors. The results could efficiently support the scientific planning, operation and policy making concerning FFBS in different urban contexts.