Search

This study aimed to determine the risk factors associated with unsafe events involving a motor vehicle, that occurred while group riding (cycling) in Perth, Western Australia. Naturalistic video footage was collected from 52 group riders and unsafe events identified. A case-crossover study was used to compare the road infrastructure and group behavioural characteristics of 108 case sites where unsafe events occurred to 216 control sites where no unsafe events occurred. After controlling for potential confounding factors, roundabouts increased the risk of an unsafe event compared to midblocks (odds ratio (OR): 3.63, 95% confidence interval (CI): 1.57-8.42, p = 0.003), priority control intersections (OR: 4.36, 95% CI: 1.49-12.76, 0.007) and traffic signal intersections (OR: 5.57, 95% CI: 1.42-21.79, p = 0.014). Raised traffic islands (OR: 2.30, 95% CI: 1.41-3.78, p = 0.001), posted speed limits of ≥60 km per hour (OR: 2.45, 95% CI: 1.55-3.86, p < 0.001) and group rider traffic violations (OR: 2.51, 95% CI: 1.14-5.53, p = 0.022) also significantly increased the risk of an unsafe event. Riding two abreast in the traffic lane (OR: 0.50, 95% CI: 0.32-0.76, p = 0.002) or having all riders in the bicycle lane (OR: 0.14, 95% CI: 0.04-0.51, p = 0.003), significantly reduced the risk of an unsafe event, compared to riding single file in the traffic lane. Simple road infrastructure treatments on popular group riding routes as well as education targeting both group riders and motorists, could reduce unsafe events and promote a safer, more inclusive shared road environment for group riders., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Roadway departure crashes contribute to a large proportion of fatal and injury crashes in the United States. These crash types are more likely to occur along horizontal curve sections of a roadway. Countermeasures that prevent vehicles from departing the roadway is one method to mitigate roadway departure crashes. Pennsylvania has deployed on-pavement horizontal curve warning markings in advance of horizontal curves on two-lane rural highways as a roadway departure crash reduction strategy. This study used an Empirical Bayes (EB) before-after study design to evaluate the safety effects of the horizontal curve warning pavement markings. A total of 263 treatment sites and more than 21,000 reference sites were included in the evaluation. Crash modification factors were developed for total, fatal plus injury, run-off-road, nighttime, nighttime run-off-road, and nighttime fatal plus injury crashes. The point estimates for each of these crashes ranged from 0.65 to 0.77 - the results were statistically significant for total and fatal plus injury crashes at the 95th-percentile confidence level., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

The estimation of the effect of contributors to crash injury severity and the prediction of crash injury severity outcomes suffer often from biases related to missing data in crash datasets that contain incomplete records. As both estimation and prediction would greatly improve if the missing values were recovered, this study proposes a sequential approach to handle incomplete crash datasets and rank contributors to the injury severity of crashes on mountainous freeways in China. The sequential approach consists of two parts: (i) multivariate imputation by chained equations imputes the missing values of independent variables; (ii) a random forest classifier analyses the correlation between the dependent and the independent variables. The first part considers different imputation methods in light of the independent variables being either binary, categorical or continuous, whereas the second part classifies the correlations according to the random forest classifier. The proposed method was applied to the case-study about mountainous freeways in China and compared to the analysis of the raw dataset to evaluate its effectiveness, and the results illustrate that the method improves significantly the classification accuracy when compared with existing methods. Moreover, the classifier ranked the contributors to the injury severity of traffic crashes on mountainous freeways: in order of importance vehicle type, crash type, road longitudinal gradient, crash cause, curve radius, and deflection angles. Interestingly, a lower importance was found for environmental factors., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

While computer vision techniques and big data of street-level imagery are getting increasing attention, a "black-box" model of deep learning hinders the active application of these techniques to the field of traffic safety research. To address this issue, we presented a semantic scene labeling approach that leverages wide-coverage street-level imagery for the purpose of exploring the association between built environment characteristics and perceived crash risk at 533 intersections. The environmental attributes were measured at eye-level using scene segmentation and object detection algorithms, and they were classified as one of four intersection typologies using the k-means clustering method. Data on perceived crash risk were collected from a questionnaire conducted on 799 children 10 to 12 years old. Our results showed that environmental features derived from deep learning algorithms were significantly associated with perceived crash risk among school-aged children. The results have revealed that some of the intersection characteristics including the proportional area of sky and roadway were significantly associated with the perceived crash risk among school-aged children. In particular, road width had dominant influence on risk perception. The findings provide information useful to providing appropriate and proactive interventions that may reduce the risk of crashes at intersections., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Importance: Emerging research suggests that factors associated with the built environment, including artificial light, air pollution, and noise, may adversely affect children's mental health, while living near green space may reduce stress. Little is known about the combined roles of these factors on children's stress., Objective: To investigate associations between components of the built environment with personal and home characteristics in a large cohort of children who were assessed for perceived stress., Design, Setting, and Participants: In this cohort study, a total of 2290 Southern California Children's Health Study participants residing in 8 densely populated urban communities responded to detailed questionnaires. Exposures of artificial light at night (ALAN) derived from satellite observations, near-roadway air pollution (NRP) determined from a dispersion model, noise estimated from the US Traffic Noise Model, and green space from satellite observations of the enhanced vegetation index were linked to each participant's geocoded residence., Main Outcomes and Measures: Children's stress was assessed at ages 13 to 14 years and 15 to 16 years using the 4-item Perceived Stress Scale (PSS-4), scaled from 0 to 16, with higher scores indicating greater perceived stress. Measurements were conducted in 2010 and 2012, and data were analyzed from February 6 to August 24, 2019. Multivariate mixed-effects models were used to examine multiple exposures; modification and mediation analyses were also conducted., Results: Among the 2290 children in this study, 1149 were girls (50%); mean (SD) age was 13.5 (0.6) years. Girls had significantly higher perceived stress measured by PSS-4 (mean [SD] score, 5.7 [3.4]) than boys (4.9 [3.2]). With increasing age (from 13.5 [0.6] to 15.3 [0.6] years), the mean PSS-4 score rose from 5.6 (3.3) to 6.0 (3.4) in girls but decreased for boys from 5.0 (3.2) to 4.7 (3.1). Multivariate mixed-effects models examining multiple exposures indicated that exposure to secondhand smoke in the home was associated with a 0.85 (95% CI, 0.46-1.24) increase in the PSS-4 score. Of the factors related to the physical environment, an interquartile range (IQR) increase in ALAN was associated with a 0.57 (95% CI, 0.05-1.09) unit increase in the PSS-4 score together with a 0.16 score increase per IQR increase of near-roadway air pollution (95% CI, 0.02-0.30) and a -0.24 score decrease per IQR increase of the enhanced vegetation index (95% CI, -0.45 to -0.04). Income modified the ALAN effect size estimate; participants in households earning less than $48 000 per year had significantly greater stress per IQR increase in ALAN. Sleep duration partially mediated the associations between stress and both enhanced vegetation index (17%) and ALAN (18%)., Conclusions and Relevance: In this cohort study, children's exposure to smoke at home in addition to residential exposure to ALAN and near-roadway air pollution were associated with increased perceived stress among young adolescent children. These associations appeared to be partially mitigated by more residential green space. The findings may support the promotion of increased residential green spaces to reduce pollution associated with the built environment, with possible mental health benefits for children.

This study develops bicycle-vehicle safety performance functions (SPFs) for five facilities in the Highway Safety Manual (HSM). These are urban two-lane undivided segments (U2U), urban four-lane divided/undivided segments (U4DU), rural two-lane undivided segments (R2U), urban four-leg and three-leg signalized intersections (USG), and urban four-leg and three-leg stop-controlled intersections (UST). Two modeling techniques were explored, the Conway-Maxwell-Poisson (COM-Poisson) model (to accommodate bicycle-vehicle crash under-dispersion) and a machine learning technique, the multivariate adaptive regression splines (MARS). MARS is a non-black-box model and can effectively handle non-linear crash predictors and interactions. A total of 1,311 bicycle-vehicle crashes from 2011 through 2015 in Alabama were collected and their respective police reports were reviewed in details. Results from the SPFs for roadway segments using COM-Poisson showed that bicycle-vehicle crash frequencies were reduced along curved and downgrade/upgrade stretches and when having heavy traffic flow (along U2U segments). For urban signalized (USG) intersections, the absence of right-turn lanes on minor roads, the presence of bus stops, and the increase in the major road annual average daily traffic (AADT) were significant factors contributing to the increase in the number of bicycle-vehicle crashes. However, the presence of divided medians on major approaches was found to reduce bicycle-vehicle crashes at USG and UST intersections. MARS outperformed the corresponding COM-Poisson models for all five facilities based on mean absolute deviance (MAD), mean square prediction error (MSPE), and generalized R-square. MARS is recommended as a promising technique for effectively predicting bicycle-vehicle crashes on segments and intersections., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Background: This study examined the influence of built environmental factors in the community on active social network size as well as the moderating influence of different dimensions of social activity on this relationship., Methods: This study employed the cross-sectional and correlational approaches to construct a scale measuring key community-level built environment factors and test the moderating influence of social activity on the association between these factors and active social network size in older adults. The study population was all 3,211 older adults who were resident in Accra and were part of the database of the Social Security and National Insurance Trust. Participants were 515 older adults in Accra aged 60 years or more who met some inclusion criteria. A self-reported questionnaire, including a new scale measuring the built environment factors, was used to collect data. Principal component analysis with varimax rotation and confirmatory factor analysis were used to validate the measurement scales, whereas Pearson's correlation test and multiple linear regression analysis were used to test the associations of interest., Results: After controlling for relevant demographic variables, built environment factors in the community have no significant influence on active social network size. Volunteering, support for social ties, and group activity positively moderated the influence of built environment factors on active social network size at p  < .05 and β ≥ 0.2., Conclusion: The study concludes that social activity is essential to the utilization of available built environmental factors by older adults to make active social ties in the community.

Background: Literature focusing on youth has reported limited evidence and non-conclusive associations between neighborhood walkability measures and active commuting to and from school (ACS). Moreover, there is a lack of studies evaluating both macro- and micro-scale environmental factors of the neighborhood when ACS is analyzed. Likewise, most studies on built environment attributes and ACS focus on urban areas, whereas there is a lack of studies analyzing rural residential locations. Moreover, the relationship between built environment attributes and ACS may differ in children and adolescents. Hence, this study aimed to develop walkability indexes in relation to ACS for urban and rural children and adolescents, including both macro- and micro-scale school-neighborhood factors., Methods: A cross-sectional study of 4593 participants from Spain with a mean age of 12.2 (SD 3.6) years was carried out. Macro-scale environmental factors were evaluated using geographic information system data, and micro-scale factors were measured using observational procedures. Socio-demographic characteristics and ACS were assessed with a questionnaire. Several linear regression models were conducted, including all the possible combinations of six or less built environment factors in order to find the best walkability index., Results: Analyses showed that intersection density, number of four-way intersections, and residential density were positively related to ACS in urban participants, but negatively in rural participants. In rural children, positive streetscape characteristics, number of regulated crossings, traffic calming features, traffic lanes, and parking street buffers were also negatively related to ACS. In urban participants, other different factors were positively related to ACS: number of regulated crossings, positive streetscape characteristics, or crossing quality. Land use mix acted as a positive predictor only in urban adolescents. Distance to the school was a negative predictor on all the walkability indexes. However, aesthetic and social characteristics were not included in any of the indexes., Conclusions: Interventions focusing on improving built environments to increase ACS behavior need to have a better understanding of the walkability components that are specifically relevant to urban or rural samples.

Although tires maintain the only contact between the vehicle and the ground, tire failures are still underrepresented in traffic safety assessments. Vehicle stability and safety can deteriorate significantly by a sudden tire failure. The current body of literature on tire failure-related crashes is limited, and no previous study was found to extensively investigate the factors associated with tire failures and the corresponding injury severity. The contributions of this study include (i) investigating the factors affecting tire failures, (ii) assessing the impacts of tire failures on occupant injury severity, and (iii) demonstrating the necessity of statewide tire inspection regulations. An extensive exploratory analysis was performed using ten years (2007-2016) of historical crash data along I-80 in Wyoming. Binary logistic regression with the Bayesian inference approach was applied to develop two separate models: tire failure and injury severity model. The results from the tire failure model showed that vehicle speeds greater than 75 mph, commercial motor vehicles, summer season, daytime, the presence of rough surface, downgrades, and concrete pavement are all related to higher tire failure occurrences. On the other hand, the incidence of a tire failure in a crash significantly contributed to more severe injuries when combined with any of the following instances: fire or explosion, rollover, guardrail hits, runoff road, angle, rear-end, clear weather, speeding, downgrades, and curved segments. With the incorporation of the random intercept in the modeling procedure, the injury severity analysis found a strong presence (42 %) of intra-crash correlation (effects of the common crash-specific unobserved factors) in occupant injury severity within the same crash. Finally, based on the findings of the study, recommendations are provided to alleviate tire-related problems., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

In this study, two novel fuzzy decision approaches, where the fuzzy logic (FL) model was revised with the C4.5 decision tree (DT) algorithm, were applied to the classification of cyclist injury-severity in bicycle-vehicle accidents. The study aims to evaluate two main research topics. The first one is investigation of the effect of road infrastructure, road geometry, street, accident, atmospheric and cyclist related parameters on the classification of cyclist injury-severity similarly to other studies in the literature. The second one is examination of the performance of the new fuzzy decision approaches described in detail in this study for the classification of cyclist injury-severity. For this purpose, the data set containing bicycle-vehicle accidents in 2013-2017 was analyzed with the classic C4.5 algorithm and two different hybrid fuzzy decision mechanisms, namely DT-based converted FL (DT-CFL) and novel DT-based revised FL (DT-RFL). The model performances were compared according to their accuracy, precision, recall, and F-measure values. The results indicated that the parameters that have the greatest effect on the injury-severity in bicycle-vehicle accidents are gender, vehicle damage-extent, road-type as well as the highly effective parameters such as pavement type, accident type, and vehicle-movement. The most successful classification performance among the three models was achieved by the DT-RFL model with 72.0 % F-measure and 69.96 % Accuracy. With 59.22 % accuracy and %57.5 F-measure values, the DT-CFL model, rules of which were created according to the splitting criteria of C4.5 algorithm, gave worse results in the classification of the injury-severity in bicycle-vehicle accidents than the classical C4.5 algorithm. In light of these results, the use of fuzzy decision mechanism models presented in this study on more comprehensive datasets is recommended for further studies., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Hotspot identification (HSID) is one of the most important components in the highway safety management process. Previous research has found that hazardous sites identified with different methods are not consistent. It is therefore necessary to evaluate the performance of various HSID methods. The existing evaluation criteria are limited to two consecutive periods, and do not consider the temporal instability of crashes. In addition, one existing criterion does not precisely evaluate HSID method under given circumstances. This paper proposed three generalized criteria to evaluate the performance of HSID methods: (1) High Crashes Consistency Test (HCCT) is proposed to evaluate HSID methods in terms of their reliabilities of identifying sites with high crash counts; (2) Common Sites Consistency Test (CSCT) is proposed to gauge HSID methods in consistently identifying a set of common sites as hazardous sites; and, (3) Absolute Rank Differences Test (ARDT) is proposed to measure the consistency of HSID methods in measuring the absolute differences in rankings. Further, three commonly used HSID methods are applied to estimate crashes on Texas rural two-lane roadway segments with eight years of crash data. The performance of these three HSID methods were evaluated to validate the proposed criteria. Comparisons between the existing criteria and the generalized criteria revealed that: (1) the generalized criteria are capable of evaluating different HSID methods over multiple periods; and (2) the generalized criteria are enhanced with a consistent result and with less discrepancy in scores of the best identified HSID method., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

The propensity score matching method has been used to estimate safety countermeasure (treatment) effects from observational crash data. Within the counterfactual framework, propensity score matching is used to balance the covariates between treatment and control groups. Recent studies in traffic safety research have demonstrated the strength of this method in reducing the bias caused by treatment site selection. However, several general issues associated with safety effect estimates may still influence the effectiveness and robustness of this method. In the present study, Bayesian methods were integrated into the propensity score matching method. Bayesian models are known for their ability to capture heterogeneity and modeling uncertainty. This may help mitigate unobserved variable effects in the roadway and crash data. Furthermore, the sampling-based algorithm used for Bayesian estimation yields more consistent estimates in small region analysis than estimates from frequentist modeling. In this study, a dataset that was used to evaluate the safety effects of the dual application of shoulder and centerline rumble strips on two-lane rural highways was acquired. Only data from the before treatment period were used in a no-treatment effect analysis in order to compare the results of a Bayesian propensity score analysis to a frequentist propensity score analysis. Because no treatment was applied during the analysis period, it was assumed that there would be no treatment effect, or a crash modification factor equal to 1.0. The Bayesian propensity score matching method nominally outperformed the frequentist propensity score matching method in the largest sample and produced near-identical results in the medium sample, but neither method closely approximated the assumed, true crash modification factor in the small sample analysis. A simulation study is recommended to further study the effects of sample size and confounding factors when comparing the Bayesian and frequentist propensity score matching methods., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

This study estimates the effects of lane and shoulder widths on occurrence of head-on and single-vehicle accidents on rural two-lane undivided roads in Norway while considering the differences between winter and non-winter accidents and their severity levels. A matched case-control method was applied to calculate the odds ratios for lane and shoulder width categories, while controlling for the effects of AADT and adjusting for the effects of region, speed limit, segment length, share of long vehicles in AADT and horizontal alignment. The study used a sample of 71,999 roadway segments identified in GIS and 1886 related accidents recorded by the police in five-year period. The results suggest that it is relevant to consider winter and non-winter accidents as well as severe and slight accidents separately when studying the effects of lane and shoulder widths on the occurrence of head-on and single-vehicle accidents. When examining lane and shoulder widths for all related accidents, the lane widths 1.50-2.50 m and shoulder widths 0.50-0.75 m were relatively safer than other categories on Norwegian two-lane rural undivided roads., Competing Interests: Declaration of Competing Interest None., (Copyright © 2020 The Authors. Published by Elsevier Ltd.. All rights reserved.)

This paper explores patterns of increased/ decreased physical activity, sedentary and sleep behaviours among Canadian children and youth aged 5-17 years during the COVID-19 pandemic, and examines how these changes are associated with the built environment near residential locations. A cluster analysis identified two groups who were primarily distinguished by the changes in outdoor activities. Compliance to 24-hour movement guidelines was low among both groups. For children, houses (versus apartments) was correlated with increased outdoor activities; proximity to major roads was a barrier. For youth, low dwelling density, and access to parks in high-density neighbourhoods, increased the odds of increased outdoor activities during the pandemic. Our findings can inform future urban and health crisis planning practices by providing new insights into the desirable public health messaging and characteristics of healthy and resilient communities., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Past roadside safety studies mostly evaluated the impact of traffic barrier geometric features using simulation tools or by conducting field crash tests. While past simulation and field crash tests could present important findings for upgrading the geometric design of traffic barriers, there is still a gap regarding conducting an actual data analysis on side traffic barriers crashes with regards to their geometric dimensions. This paper aims at filling this gap by combining a statewide dataset of side traffic barrier geometric features with historical crashes on interstate roads in Wyoming. Therefore, geometric features including system height, post-spacing, lateral offset (from the edge of pavement), and side-slope of over 150 miles of side traffic barriers were inventoried by conducting a field survey on interstate roads in Wyoming. For the statistical analysis, a random-parameters ordered logit model was utilized to investigate variables impacting crash severity of side traffic barriers. It was found that system height could significantly impact the crash severity of side box beam barriers. Box beam barriers with a system height between 25 and 31 in. were identified to be less severe in comparison to other height categories, while showing minimum risks of severe crashes in the system height of 29-31 in.. On the other hand, box beam barriers with a height taller than 31 in. may increase crash severity., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Objectives: Some studies reveal that socio-economic status, behavioural factors, accessibility to supermarket or food store, are associated with the prevalence of obesity and overweight. In this study, we aimed to examine to what extent socio-economic, behavioural and built environment characteristics can contribute to spatial disparities in adult obesity., Study Design: The spatial analysis was undertaken to understand the association of spatial disparities in adult obesity and spatial disparities in socio-economic, behavioural and built environment characteristics., Methods: A spatial regression model which can remove the impact of auto-correlation in the residuals of conventionally regression models was applied to modelling local-scale rate of adult obesity (N = 59)., Results: Owing to the presence of residual spatial auto-correlation in the non-spatial regression model estimated, a spatial regression model was set up successfully to model local-scale rate of adult obesity across New York City (R 2  = 0.8353, N = 59). Compared with socio-economic and built environment factors, behavioural factors make statistically significant contributions to spatial disparities in the prevalence of adult obesity (POAO). Particularly, two behavioural factors ('sugary drinks consumption' and 'fruits and vegetable consumption') can explain more than 70% of the variance of POAO (adjusted R 2  = 0.7323, N = 59). Surprisingly, physical activity prevalence (percent of physically active adults) makes no statistically significant contributions., Conclusions: The results further suggest that the reduction of adult obesity prevalence could benefit more from decreasing intake of sugary drinks than increasing physical activity. The local government and policy are advised to prioritise decreasing exposure of residents to sugary drinks through restricting advertising or increasing taxes rather than increasing neighbourhoods' walkability through urban planning., (Copyright © 2020 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.)

Transportation agencies utilize Active traffic management (ATM) systems to dynamically manage recurrent and non-recurrent congestion based on real-time conditions. While these systems have been shown to have some safety benefits, their impact on injury severity outcomes is currently uncertain. This paper used full Bayesian mixed logit models to quantify the impact that ATM deployment had on crash severities. The estimation results revealed lower severities with ATM deployment. Marginal effects for ATM deployments that featured hard shoulder running (HSR) revealed lower likelihoods for severe and moderate injury crashes of 15.9 % and for minor injury crashes of 10.1 %. The likelihood of severe and moderate injury crashes and minor injury crashes reduced by 12.4 % and 8.33 % with ATM without HSR. The models were observed to be temporally transferable and had forecast error of 0.301 and 0.304 for the two models, revealing better performance with validation data. These results have implications for improving freeway crash risk at critical locations., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

The present study has investigated the relationship between traffic volume and crash numbers by means of meta-analysis, based on 521 crash prediction models from 118 studies. The weighted pooled volume coefficient for all crashes and all levels of crash severity (excluding fatal crashes) is 0.875. The most important moderator variable is crash type. Pooled volume coefficients are systematically greater for multi vehicle crashes (1.210) than for single vehicle crashes (0.552). Regarding crash severity, the results indicate that volume coefficients are smaller for more fatal crashes (0.777 for all fatal crashes) than for injury crashes but no systematic differences were found between volume coefficients for injury and property-damage-only crashes. At higher levels of volume and on divided roads, volume coefficients tend to be greater than at lower levels of volume and on undivided roads. This is consistent with the finding that freeways on average have greater volume coefficients than other types of road and that two-lane roads are the road type with the smallest average volume coefficients. The results indicate that results from crash prediction models are likely to be more precise when crashes are disaggregated by crash type, crash severity, and road type. Disaggregating models by volume level and distinguishing between divided and undivided roads may also improve the precision of the results. The results indicate further that crash prediction models may be misleading if they are used to predict crash numbers on roads that differ from those that were used for model development with respect to composition of crash types, share of fatal or serious injury crashes, road types, and volume levels., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Numerous studies have developed intersection crash prediction models to identify crash hotspots and evaluate safety countermeasures. These studies largely considered only micro-level crash contributing factors such as traffic volume, traffic signals, etc. Some recent studies, however, have attempted to include macro-level crash contributing factors, such as population per zone, to predict the number of crashes at intersections. As many intersections are located between multiple zones and thus affected by factors from the multiple zones, the inclusion of macro-level factors requires boundary problems to be resolved. In this study, we introduce an advanced multilevel model, the multiple membership multilevel model (MMMM), for intersection crash analysis. Our objective was to reduce heterogeneity issues between zones in crash prediction model while avoiding misspecification of the model structure. We used five years of intersection crash data (2009-2013) for the City of Regina, Saskatchewan, Canada and identified micro- and macro-level factors that most affected intersection crashes. We compared the fitting performance of the MMMM with that of two existing models, a traditional single model (SM) and a conventional multilevel model (CMM). The MMMM outperformed the SM and CMM in terms of fitting capability. We found that the MMMM avoided both the underestimation of macro-level variance and the type I statistical error that tend to occur when the crash data are analyzed using a SM or CMM. Statistically significant micro-level and macro-level crash contributing factors in Regina included major roadway AADT, four legs, traffic signals, speed, young drivers, and different types of land use., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Bicycle wrong way riding (WWR) is a dangerous and often neglected behavior that engenders threats to traffic safety. Owing to the lack of exposure data, the detection of WWR and its relationship with the built environment (BE) factors remain unclear. Accordingly, this study fills the research gaps by proposing a WWR detection framework based on bike-sharing trajectories collected from Chengdu, China. Moreover, this study adopts Negative Binomial-based Additive Decision Tree to investigate the impacts of built environment on WWR frequencies. Results reveal that (1) WWR distribution is unaffected by different periods in a day; (2) road length is more influential than road level and road direction in WWR occurrence; (3) company, bus stop, subway station, residence, and catering facility are primary contributors affecting WWR behavior during peak hours, whereas education becomes an emerging influential variable during nonpeak hours; and most importantly, (4) these variables clearly present non-linear effects on the WWR frequencies. Therefore, geographically differentiated policies should be adopted for bicycle safety improvement., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Korea's elderly population is growing rapidly, as is attention to elderly pedestrian safety. Despite a consensus that the elderly are vulnerable to pedestrian safety issues, our understanding of the determinants of elderly pedestrian crashes is limited. This study explores which attributes of the built environment affect the risk of pedestrian accidents among the elderly, particularly with respect to injury severity, in Seoul, Korea. We compare the impacts of various determinants on pedestrian crashes to specify how the associations between various built environments and pedestrian accidents differ by pedestrian age. We also examine how the associations vary by neighborhood economic attributes. Our findings provide policy implications for identifying various attributes of the built environment that increase the risk of elderly pedestrian crashes and improving the safety of elderly pedestrian by neighborhood economic status., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Both crash count and severity are thought to quantify crash risk at defined transport network locations (e.g. intersections, a particulate section of highway, etc.). Crash count is a measure of the likelihood of occurring a potential harmful event, whereas crash severity is a measure of the societal impact and harm to the society. As the majority of safety improvement programs are focused on preventing fatal and serious injury crashes, identification of high-risk sites-or blackspots-should ideally account for both severity and frequency of crashes. Past research efforts to incorporate crash severity into the identification of high-risk sites include multivariate crash count models, equivalent property damage only models and two-stage mixed models. These models, however, often require suitable distributional assumptions for computational efficiency, neglect the ordinal nature of crash severity, and are inadequate for capturing unobserved heterogeneity arising from possible correlations between crash counts of different severity levels. These limitations can ultimately lead to inefficient allocation of resources and misidentification of sites with high risk of fatal and serious injury crashes. Moreover, the implication of these models in blackspot identification is an important, unanswered question. While a joint econometric model of crash count and crash severity has the flexibility to account for the limitations mentioned previously, its ability to identify high-risk sites also needs to be examined. This study aims to fill this research gap by employing the joint model for blackspot identification. Using data from state-controlled roads in Queensland, Australia, a new risk score is developed based on predicted crash counts by severity, weighted by the cost ratio of severity levels. This weighted risk score is then used for identifying road segments with high risk of fatal and injury crashes. Results show that the joint model of crash count and crash severity has substantially improved prediction accuracy compared to the traditional count models. The correlation between crash counts of different severity levels captures the unobserved heterogeneity caused by the extra-variation in total crash counts and moderates the parameters in the joint model. In comparison with the traditional approaches, the proposed weighted risk score approach with the joint model of crash count and crash severity leads to the identification of a higher number of fatal and serious injury crashes in the top ranked sites flagged for safety improvements., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Although the fatal rate of passenger vehicle-involved crashes has decreased in the United States, the fatal rate of truck-involved crashes has increased. This has, in recent years, become a more severe problem than that caused by passenger vehicle-involved crashes. More studies need to be conducted in order to investigate factors that impact the severity of truck-involved crashes within specific scenarios. This study identifies and evaluates the factors that affect the severity of the truck-involved crashes at cross and T-intersections in North Carolina from 2005 to 2017. A latent class clustering for data segmentation is implemented to mitigate unobserved heterogeneity inherent in the crash data. Four partial proportional odds models, which include fixed and unfixed parameters, are developed considering the heterogeneous and ordinal nature inherent in severities. Estimated parameters and marginal effects are further investigated for better interpreting the impacts. Results show heterogeneous explanatory variables and associated coefficients for different classes and severity levels, which indicate the superiority of this combined approach to obtaining more specific factors and accurate coefficients that are estimated in different scenarios. Many factors are found to contribute to the severities, and crossroad scenarios are found to be more severe than T-intersections. The top five driving behaviors at intersections that contribute to the severity include disregarded signs, improper lane use, followed too closely, ignored signals, and failure to yield. These behaviors arouse a necessity to amend the traffic laws and strengthen drivers' education while giving further insights to engineering practitioners and researchers., Competing Interests: Declaration of Competing Interest The author declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Objective: To explore the associations between perceived built environment attributes and adults' leisure-time physical activity in four cities of China. Methods: Multistage cluster random sampling method was used to select adults aged 25 to 64 in Hangzhou, Suzhou, Chengdu, and Qingdao. Data were collected from June 2017 to July 2018. The perception of the urban built environment was assessed by the neighborhood environment walkability scale-abbreviated (NEWS-A), and the physical activity was assessed by the International Physical Activity Questionnaire. Generalized linear mixed models were used to explore the relationship between the perceived built environment and leisure-time physical activities. Results: A total of 3 789 participants were included in the analysis. After adjusting for potential confounders, better access to public services ( OR =1.34, 95% CI : 1.02-1.75) and higher aesthetic quality ( OR =1.37, 95% CI : 1.09-1.73) were positively associated with the possibility of engaging in leisure-time physical activity in the past week. Similarly, these two attributes were positively associated with leisure-time walking. Higher scores on the perception of street connectivity were positively associated with leisure-time walking [ exp ( β )=1.09, 95% CI : 1.00-1.19]. Higher residential density [ exp ( β )=1.000 4, 95% CI :1.000 0-1.000 8], better access to physical activity destinations[ exp ( β )=1.09, 95% CI : 1.00-1.19], and better aesthetics [ exp ( β )=1.11, 95% CI :1.00-1.22] were associated with higher leisure-time physical activity. Similarly, these three attributes were positively associated with the possibility of meeting the WHO recommendations. Conclusion: Changing some urban built environment attributes may increase leisure-time physical activity.

Objective: Research indicates that natural features surrounding schools are associated with better academic achievement, enhanced focus, and reduced stress in students. However, few studies have examined the associations between school landscape elements and anxiety, depression, and behavioral concerns in students. The purpose of this study was to determine whether greenery and impervious surfaces surrounding schools are associated with student internalizing and externalizing problems., Method: Fifth-grade students (n = 21,378) reported internalizing and externalizing behaviors on the 2016 Minnesota Student Survey, an anonymous school-based survey administered statewide. Geographic information system landscape data were available for 268 schools in metropolitan areas. We calculated the percentages of tree canopy, grass and shrub cover, and impervious surfaces within 300- and 500-m radius areas around each school building. Multilevel regression models evaluated the associations between student-level internalizing and externalizing behaviors and school-level landscape percentages, controlling for sex, race, and free/reduced-price lunch., Results: Students attending metropolitan schools with a higher percentage of impervious surfaces were more likely to report externalizing behaviors and marginally more internalizing symptoms than students in schools with a lower percentage of impervious surfaces. Higher percentages of tree canopy and grass and shrub cover surrounding schools were not associated with internalizing or externalizing behaviors., Conclusion: Increased impervious surfaces around schools in metropolitan areas were related to increased student externalizing behaviors. Further studies are necessary to understand the significance of the built environment, types of greenery, impact of greenery on student adjustment in nonmetropolitan settings, and varying areas surrounding schools. These results could inform initiatives to improve school landscapes.

This multicountry study used Demographic and Health Surveys (DHS) data from 138,097 women to explore the relationship between non-partner violence (NPV) and sanitation, water, and urbanization in sub-Saharan Africa (SSA). One out of 15 women reports having experienced physical and/or sexual violence by a non-partner during the previous 12 months; within the region, prevalence ranges from 2.3-11.3%. Explanatory models of NPV improve in 11 of the 20 countries when the built environment variables are included. The results suggest that sanitation and water access are associated with risk of NPV in a number of countries in the region, particularly in urban settings.

Although crashes involving hazardous material (HAZMAT) vehicles on expressways do not occur frequently compared with other types of vehicles, the number of lives lost and social damage is very high when a HAZMAT vehicle-involved crash occurs. Therefore, it is essential to identify the leading causes of crashes involving HAZMAT vehicles and make specific countermeasures to improve the safety of expressways. This study aims to employ the association rules mining (ARM) approach to discover the contributory crash-risk factors of HAZMAT vehicle-involved crashes on expressways. A case study is conducted using crash data obtained from the Korea Expressway Corporation crash database from 2008 to 2017. ARM was conducted using the Apriori algorithm, and a total of 855 interesting rules were generated. With appropriate support, confidence, and lift values, we found hidden patterns in the HAZMAT crash characteristics. The results indicate that HAZMAT vehicle-involved crashes are highly associated with male drivers, single vehicle-involved crashes, clear weather conditions, daytime, and mainline segments. Also, we found that HAZMAT tank-lorry and cargo truck crashes, single vehicle-involved crashes, and crashes on mainline segments of expressways had independent and unique association rules. The finding from this study demonstrates that ARM is a plausible data mining technique that can be employed to draw relationships between HAZMAT vehicle-involved crashes and significant crash-risk factors, and has the potential of providing more easy-to-understand results and relevant insights for the safety improvement of expressways., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Background: Concerns about loss of greenspace with urbanisation motivate much research on nature and health; however, contingency of greenspace-health associations on the character of community change remains understudied., Methods: With aggregate data from governmental sources for 1432 Swedish parishes, we used negative binomial regression to estimate incidence rate ratios (IRRs) for all-cause and cardiovascular disease (CVD) mortality during 2000-2008 in relation to percentage area (in 2000) of urban residential greenspace, urban parks and rural greenspace, looking across parishes with decrease, stability or increase in population density. We also assessed interactions between land use and population change., Results: Parishes with ≥1 decile increase in population density had lower incidence of all-cause (IRR=0.91, 95% CI 0.87 to 0.95) and CVD mortality (IRR=0.89, 95% CI 0.84 to 0.94) compared with parishes with stable populations. In stable parishes, all-cause mortality was lower with higher percentages of urban green (IRR=0.998, 95% CI 0.996 to 1.000) and rural green land uses (IRR=0.997, 95% CI 0.996 to 0.999). These results were inverted in densifying parishes; higher all-cause mortality attended higher initial percentages of urban (IRR=1.081, 95% CI 1.037 to 1.127) and rural greenspace (IRR=1.042, 95% CI 1.007 to 1.079) as measured in 2000. Similar associations held for CVD mortality., Conclusions: More greenspace was associated with lower all-cause and CVD mortality in communities with relatively stable populations. In densifying communities, population growth per se may reduce mortality, but it may also entail harm through reductions in amount per capita and/or quality of greenspace., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: Effective place-based interventions for childhood obesity call for the recognition of the high-risk neighbourhoods and an understanding of the determinants present locally. However, such an approach is uncommon. In this study, we identified neighbourhoods with elevated prevalence of childhood obesity ('hotspots') in the Porto Metropolitan Area and investigated to what extent the socio-economic and built environment characteristics of the neighbourhoods explained such hotspots., Methods: We used data on 5203 7-year-old children from a population-based birth cohort, Generation XXI. To identify hotspots, we estimated local obesity odds ratios (OR) and 95% confidence intervals (95%CI) using generalized additive models with a non-parametric smooth for location. Measures of the socio-economic and built environment were determined using a Geographic Information System. Associations between obesity and neighbourhood characteristics were expressed as OR and 95%CI after accounting for individual-level variables., Results: At 7 years of age, 803 (15.4%) children were obese. The prevalence of obesity varied across neighbourhoods and two hotspots were identified, partially explained by individual-level variables. Adjustment for neighbourhood characteristics attenuated the ORs and further explained the geographic variation. This model revealed an association between neighbourhood socio-economic deprivation score and obesity (OR = 1.014, 95%CI 1.004-1.025), as well as with the presence of fast-food restaurants at a walkable distance from the residence (OR = 1.37, 1.06-1.77)., Conclusions: In our geographic area it was possible to identify neighbourhoods with elevated prevalence of childhood obesity and to suggest that targeting such high-priority neighbourhoods and their environmental characteristics may help reduce childhood obesity., (© The Author(s) 2019. Published by Oxford University Press on behalf of the International Epidemiological Association.)

Adolescents have the potential to be active in multiple places across weekdays and weekends. While the built environment features around home are known to be important for adolescents' physical activity, specific built environment features that facilitate physical activity outside the home neighbourhood are not well explored. Given that adolescents spend most days of the week at school, the built environment surrounding school may be particularly important for supporting their physical activity. This study examined cross-sectional associations of objective built environments around home and school at multiple spatial scales with accelerometer assessed moderate-to-vigorous physical activity (MVPA) across the week. Moderating effects of adolescents' age were further explored. Data from up to 417 adolescents aged 12-20 years from 18 schools in Melbourne, Australia were used. Features of the built environment around school were positively associated with MVPA on weekdays; and a wide range of built environment features around home were found to be important for MVPA during weekends. Age was found to moderate the relationship between some built environment features around both home and school and MVPA across the week. Understanding the contribution of the built environment features around both home and school with MVPA could inform policy recommendations and public health interventions for each setting., Competing Interests: Declaration of competing interest None., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Safety performance function (SPF) has been a vital tool in traffic safety evaluation including finding contributing factors to crashes, identifying hotspots, and assessing safety effects of countermeasures. In the United States (U.S.), the Highway Safety Manual provides a number of SPFs for a variety of road facilities. Due to the limited availability of traffic data in many regions, the transferability of SPFs has been an important topic in traffic safety analysis and has been evaluated by several studies. Nevertheless, the international transferability of freeway SPFs and the applicability of transferred SPFs on hotspot identification has been rarely investigated. Based on data from two Chinese cities, Shanghai and Suzhou, and three U.S. states, Texas, New York and Florida, this study analyzes the transferability of freeway SPFs between Chinese and U.S. regions. These SPFs are then transferred to the other country and their performance on hotspot identification is investigated. SPFs were developed in the frameworks of Poisson, Poisson-lognormal and negative binomial regressions for the five localities separately, and were calibrated using the calibration functions before being transferred. Without calibration, the poor model transferability was found between the two countries, while after calibration, the transferred SPFs between Shanghai/Suzhou and Texas/New York showed satisfactory performance on both model fitting and hotspot identification. However, the transferability of SPFs between Florida and the Chinese cities turned out to be unsatisfactory regardless of whether being calibrated or not, which was attributable to the considerable difference in traffic flow. The findings of this study are expected to be a good reference for researchers and practitioners who want to understand the transferability and applicability of SPFs in the international context., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

As active commuting levels continue to decline among primary schoolchildren, evidence about which built environmental characteristics influence walking or cycling to school remains inconclusive and is strongly context-dependent. This study aimed to identify the objective built environmental drivers of, and barriers to, active commuting to school for a multi-ethnic sample of 1,889 healthy primary schoolchildren (aged 5-11) in London, UK. Using cross-sectional multilevel ordered logistic regression modelling, supported by the spatial exploration of built environmental characteristics through cartography, the objective built environment was shown to be strongly implicated in children's commuting behaviour. In line with earlier research, proximity to school emerged as the prime variable associated with the choice for active commuting. However, other elements of the urban form were also significantly associated with children's use of active or passive modes of transport. High levels of accidents, crime and air pollution along the route to school were independently correlated with a lower likelihood of children walking or cycling to school. Higher average and minimum walkability and higher average densities of convenience stores along the way were independently linked to higher odds of active commuting. The significance of the relations for crime, air pollution and walkability disappeared in the fully-adjusted model including all built environmental variables. In contrast, relationships with proximity, traffic danger and the food environment were maintained in this comprehensive model. Black children, pupils with obesity, younger participants and those from high socioeconomic families were less likely to actively commute to school. There is thus a particular need to ensure that roads with high volumes of actively commuting children are kept safe and clean, and children's exposure to unhealthy food options along the way is limited. Moreover, as short commuting distances are strongly correlated with walking or cycling, providing high-quality education near residential areas might incite active transport to school., Competing Interests: The authors have declared that no competing interests exist.

This qualitative study explored African Americans' perceived behavioral control (PBC) beliefs about how social determinants of health (SDoH) impact their physical activity behavior. Thirty semi-structured interviews were conducted with African Americans aged 30-50 in Oklahoma. Interviews were audio recorded, transcribed, coded, and analyzed for themes by two independent coders. Economic stability themes represented beliefs that financial status facilitates physical activity, yet, employment duties impede physical activity. SES barriers to physical activity did not emerge within this relatively high SES sample. Neighborhood and built environment themes represented beliefs that accessibility facilitates physical activity, while inclement weather impedes physical activity. Although not a part of the question path, motivation emerged as a theme and represented beliefs about how motivation impacts physically active behavior in the absence of other SDoH barriers. This research can inform interventions developed to promote physical activity among African American adults in the central US.

Background: Built environment characteristics in the neighborhood are of utmost priority for a healthy lifestyle in the fast-urbanizing countries. These characteristics are closely linked to the disease burden and challenges in low- and middle-income countries (LMICs), which have been unexplored using open-source data. The present technology offers online resources and open source software that enable researchers to explore built environment characteristics with health and allied phenomena., Objectives: This article intends to delineate methods to capture available and accessible objective built environment variables for a state in India and determine their distribution across the state., Methods: Built environment variables such as population density and residential density were collated from the Census of India. Safety from crime and traffic were captured as crime rates and pedestrian accident rates, respectively, acquired from State Crime Records Bureau. Greenness, built-up density, and land slope were gathered from open-source satellite imagery repository. Road intersection density was derived from OpenStreetMap. Processing and analysis differed for each dataset depending on its source and nature., Results: Each variable showed a distinct pattern across the state. Population and residential density were found to be closely related to each other across both districts and subdistricts. They were both positively related to crime rates, pedestrian accident rates, built-up density, and intersection density, whereas negatively related to land slope and greenness across the subdistricts., Conclusion: Delineating the distribution of built environment variables using available and open-source data in resource-poor settings is a first in public health research among LMICs. Cost-effectiveness and reproducible nature of open-source solutions could equip researchers in resource-poor settings to identify built environment characteristics and patterns across regions., Competing Interests: None

Introduction: Natural experiments are considered a priority for examining causal associations between the built environment (BE) and physical activity (PA) because the randomised controlled trial design is rarely feasible. Few natural experiments have examined the effects of walking and cycling infrastructure on PA and active transport in adults, and none have examined the effects of such changes on PA and active transport to school among adolescents. We conducted the Built Environment and Active Transport to School (BEATS) Study in Dunedin city, New Zealand, in 2014-2017. Since 2014, on-road and off-road cycling infrastructure construction has occurred in some Dunedin neighbourhoods, including the neighbourhoods of 6 out of 12 secondary schools. Pedestrian-related infrastructure changes began in 2018. As an extension of the BEATS Study, the BEATS Natural Experiment (BEATS-NE) (2019-2022) will examine the effects of BE changes on adolescents' active transport to school in Dunedin, New Zealand., Methods and Analysis: The BEATS-NE Study will employ contemporary ecological models for active transport that account for individual, social, environmental and policy factors. The published BEATS Study methodology (surveys, accelerometers, mapping, Geographic Information Science analysis and focus groups) and novel methods (environmental scan of school neighbourhoods and participatory mapping) will be used. A core component continues to be the community-based participatory approach with the sustained involvement of key stakeholders to generate locally relevant data, and facilitate knowledge translation into evidence-based policy and planning., Ethics and Dissemination: The BEATS-NE Study has been approved by the University of Otago Ethics Committee (reference: 17/188). The results will be disseminated through scientific publications and symposia, and reports and presentations to stakeholders., Trial Registration Number: ACTRN12619001335189., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

School neighbourhood built environments (SN-BE) can influence adolescents' active transport to school habits. Typically, SN-BE assessment has involved micro-scale (i.e., environmental audits) or macro-scale (Geographic Information Systems (GIS)) assessment tools. However, existing environmental audits are time/resource-intensive and not specific to school neighbourhoods, while GIS databases are not generally purposed to include micro-scale data. This study evaluated the inter-rater reliability and feasibility of using a modified audit tool and protocol (Microscale Audit of Pedestrian Streetscapes Global-School Neighbourhood (MAPS Global-SN)) to assess the SN-BE of twelve secondary schools in Dunedin, New Zealand. Correlations between MAPS Global-SN and GIS measures of the SN-BE were also examined. Specifically, MAPS Global-SN audit and GIS spatial analysis (intersection density, residential density, land use mix, walkability) was conducted within a 0.5 km street-network buffer-zone around all twelve schools. Based on investigator and expert consultation, MAPS Global-SN included eight modifications to both auditing processes and items. Inter-rater reliability data was collected from two independent auditors across two schools. The feasibility of a condensed audit protocol (auditing one side of each street segment in the neighbourhood, compared to both sides) was also assessed. Results indicated the modified MAPS Global-SN tool had good to excellent inter-rater reliability and the condensed MAPS Global-SN audit protocol appeared to sufficiently represent the micro-scale SN-BE. Results also highlighted the complementary nature of micro- and macro-scale assessments. Further recommendations for SN-BE assessment are discussed., Competing Interests: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Insufficient physical activity (PA) is the fourth major risk factor for many non-communicable diseases and premature mortality worldwide. Features of the built environment (BE) play a considerable role in determining population PA behaviors. The majority of evidence for PA-BE relationships comes from high-income countries and may not be generalizable to low- and middle-income countries (LMICs). We aim to systematically review the literature and assess the associations between perceived and/or objective BE characteristics and PA domains in LMICs. This review adopted a systematic search strategy for English language articles published between January 2000 and June 2019 from four electronic databases-Medline, Embase, Web of Science and PubMed-adhering to the PRISMA guidelines. Studies addressing the associations between self-reported and/or objective BE and PA were only included if they were conducted in LMICs, according to the World Bank classification list. Articles investigating PA-BE relationships across any age groups were included, and all study designs were eligible, except for qualitative studies and reviews. Thirty-three studies were included for evidence synthesis. Cross-sectional studies were the most prevailing study design (97%), revealing a notable gap in longitudinal PA-BE research in LMICs. A majority of the BE factors were not associated with different PA domains while others (e.g., density, proximity to services, aesthetics) exhibited an inconsistent association. Land-use mix diversity was positively associated with transport PA and the presence of recreation facilities resulted in an increase in PA during leisure-time. Increased safety from crime at night consistently increased total PA and walking levels. Research exploring the associations between BE attributes and PA behaviors in LMICs appears to be limited and is primarily cross-sectional. Longitudinal research studies with objective measures are needed for inferring well-grounded PA-BE causal relationships and informing the design of evidence-based environmental interventions for increasing PA levels in LMICs., Competing Interests: The authors have declared that no competing interests exist.

Municipalities in the United States often encourage bicycling for the health, economic, and environmental benefits by implementing new and innovative bicycle infrastructure treatments. Unfortunately, many treatments are unfamiliar to or misunderstood by drivers, especially when lacking explicit rules (e.g., shared lanes). To date, research has largely investigated bicycle infrastructure from a bicyclist's perspective, but with little research from the driver's perspective. The objective of this research is to utilize a driving simulator to investigate driver behavior towards different bicycle infrastructure treatments when driver behavior is not provoked by an interaction with bicyclists. More specifically, this research intends to investigate the impact of bicycling frequency and treatment familiarity, as well as the combined effect of the two, on driver behavior at each treatment type. The treatments investigated are shared lane markings called "sharrows", standard bike lanes, bike boxes, and merge lanes. The results show that bicycling frequency significantly affects the proportion of drivers making eye glances at treatments. In addition, drivers more familiar with bike boxes stopped significantly further back from bike boxes, and drivers more familiar with merge lanes performed the merge maneuver significantly earlier. Furthermore, driver speed and lane positioning at bike lanes was significantly affected by the combination of bike lane familiarity and bicycling frequency, but not individually. This research is a first step towards understanding driver behavior and expectation of bicyclists; an essential understanding for infrastructure treatments that do not provide physical barriers between bicycles and automobiles, and instead rely on driver behavior for safety., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Faced with the current growth and change to Western Australia's road network as well as the promotion and increased uptake of cycling, further investigation into crash, injury and road infrastructure characteristics is necessary. An in-depth study was conducted of 100 cyclists who were injured due to involvement in a crash that occurred on-road and resulted in an admission to a hospital. Information collected included a researcher-administered questionnaire, crash details from the Integrated Road Information System (IRIS), injury information from the State Trauma Registry and a virtual on-line site inspection. Overall, 42 % of crashes involved a motor vehicle and 58 % did not involve a motor vehicle. Twenty-one percent of all crashes involved cyclist loss of control, 18% were crashes with another cyclist, 18% involved hitting an object and 1% involved a pedestrian. . Bicycle crashes were severely under-reported with only 40 percent reported to the Police. Approximately half of crashes occurred at intersections (51 %) and half at midblock (non-intersection) sites (49 %). Fifty-seven percent of crashes that occurred at intersections involved a motor vehicle, whereas only 27% of crashes that occurred at midblocks involved a motor vehicle. The majority of cyclists' injuries were classified as minor according to the Injury Severity Score with the mean number of body regions injured being 4.5 (SD = 2.2). The mean number of days in hospital care was 5.2 days (SD = 5.6, range: 1-33). These findings can be used to guide road infrastructure treatments that reduce the risk of bicycle crashes in Western Australia and insights may inform action in other jurisdictions where cycling is increasing in popularity., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Background: Assessing aspects of intersections that may affect the risk of pedestrian injury is critical to developing child pedestrian injury prevention strategies, but visiting intersections to inspect them is costly and time-consuming. Several research teams have validated the use of Google Street View to conduct virtual neighborhood audits that remove the need for field teams to conduct in-person audits., Methods: We developed a 38-item virtual audit instrument to assess intersections for pedestrian injury risk and tested it on intersections within 700 m of 26 schools in New York City using the Computer-assisted Neighborhood Visual Assessment System (CANVAS) with Google Street View imagery., Results: Six trained auditors tested this instrument for inter-rater reliability on 111 randomly selected intersections and for test-retest reliability on 264 other intersections. Inter-rater kappa scores ranged from -0.01 to 0.92, with nearly half falling above 0.41, the conventional threshold for moderate agreement. Test-retest kappa scores were slightly higher than but highly correlated with inter-rater scores (Spearman rho = 0.83). Items that were highly reliable included the presence of a pedestrian signal (K = 0.92), presence of an overhead structure such as an elevated train or a highway (K = 0.81), and intersection complexity (K = 0.76)., Conclusions: Built environment features of intersections relevant to pedestrian safety can be reliably measured using a virtual audit protocol implemented via CANVAS and Google Street View.

Background: The built environment is a structural determinant of health and has been shown to influence health expenditures, behaviors, and outcomes. Traditional methods of assessing built environment characteristics are time-consuming and difficult to combine or compare. Google Street View (GSV) images represent a large, publicly available data source that can be used to create indicators of characteristics of the physical environment with machine learning techniques. The aim of this study is to use GSV images to measure the association of built environment features with health-related behaviors and outcomes at the census tract level., Methods: We used computer vision techniques to derive built environment indicators from approximately 31 million GSV images at 7.8 million intersections. Associations between derived indicators and health-related behaviors and outcomes on the census-tract level were assessed using multivariate regression models, controlling for demographic factors and socioeconomic position. Statistical significance was assessed at the α = 0.05 level., Results: Single lane roads were associated with increased diabetes and obesity, while non-single-family home buildings were associated with decreased obesity, diabetes and inactivity. Street greenness was associated with decreased prevalence of physical and mental distress, as well as decreased binge drinking, but with increased obesity. Socioeconomic disadvantage was negatively associated with binge drinking prevalence and positively associated with all other health-related behaviors and outcomes., Conclusions: Structural determinants of health such as the built environment can influence population health. Our study suggests that higher levels of urban development have mixed effects on health and adds further evidence that socioeconomic distress has adverse impacts on multiple physical and mental health outcomes.

We explored associations between residential preferences and sociodemographic characteristics, the concordance between current neighborhood characteristics and residential preferences, and heterogeneity in concordance by income and race/ethnicity. Data came from a cross-sectional phone and mail survey of 3668 residents of New York City, Baltimore, Chicago, Los Angeles, St. Paul, and Winston Salem in 2011-12. Scales characterized residential preferences and neighborhood characteristics. Stronger preferences were associated with being older, female, non-White/non-Hispanic, and lower education. There was significant positive but weak concordance between current neighborhood characteristics and residential preferences (after controlling sociodemographic characteristics). Concordance was stronger for persons with higher income and for Whites, suggesting that residential self-selection effects are strongest for populations that are more advantaged.

Toll plazas with both Electronic Toll Collection (ETC) lane(s) and Manual Toll Collection (MTC) lane(s) could increase crash risks especially at upstream diverging areas because of frequency lane-change behaviors. This study develops the logistic regression (LR) model and five typical non-parametric models including, K-Nearest Neighbor (KNN), Artificial Neural Networks (ANN), Support Vector Machines (SVM), Decision Trees (DT), and Random Forest (RF) to examine the relationship between influencing factors and vehicle collision risk. Based on the vehicle trajectory data extracted from unmanned aerial vehicle (UAV) videos using an automated video analysis system, the unconstrained vehicle motion's collision risk can be evaluated by the extended time to collision (ETTC). Results of model performance comparison indicate that not all non-parametric models have a better prediction performance than the LR model. Specifically, the KNN, SVM, DT and RF models have better model performance than LR model in model training, while the ANN model has the worst model performance. In model prediction, the accuracy of LR model is higher than that of other five non-parametric models under various ETTC thresholds conditions. The LR model implies a pretty good performance and its results also indicate that vehicle yields the higher collision risk when it drives on the left side of toll plaza diverging area and more dangerous situations could be found for an ETC vehicle. Moreover, the vehicle collision risks are positively associated with the speed of the following vehicle and the angle between the leading vehicle speed vector and X axis. Furthermore, the results of DT model show that three factors play important roles in classifying vehicle collision risk and the effects of them on collision risk are consistent with the results of LR model. These findings provide valuable information for accurate assessment of collision risk, which is a key step toward improving safety performance of the toll plaza diverging area., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

The health of Indigenous Australians is dramatically poorer than that of the non-Indigenous population. Amelioration of these differences has proven difficult. In part, this is attributable to a conceptualisation which approaches health disparities from the perspective of individual-level health behaviours, less so the environmental conditions that shape collective health behaviours. This ecological study investigated associations between the built environment and cardiometabolic mortality and morbidity in 123 remote Indigenous communities representing 104 Indigenous locations (ILOC) as defined by the Australian Bureau of Statistics. The presence of infrastructure and/or community buildings was used to create a cumulative exposure score (CES). Records of cardiometabolic-related deaths and health service interactions for the period 2010-2015 were sourced from government department records. A quasi-Poisson regression model was used to assess the associations between built environment "healthfulness" (CES, dichotomised) and cardiometabolic-related outcomes. Low relative to high CES was associated with greater rates of cardiometabolic-related morbidity for two of three morbidity measures (relative risk (RR) 2.41-2.54). Cardiometabolic-related mortality was markedly greater (RR 4.56, 95% confidence interval (CI), 1.74-11.93) for low-CES ILOCs. A lesser extent of "healthful" building types and infrastructure is associated with greater cardiometabolic-related morbidity and mortality in remote Indigenous locations. Attention to environments stands to improve remote Indigenous health., Competing Interests: The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

This cross-sectional study examines built and natural environmental correlates of parental safety concerns for children's active travel to school (ATS), controlling for socio-demographic, attitudinal, and social factors. Questionnaire surveys ( n = 3291) completed by parents who had 1st-6th grade children were collected in 2011 from 20 elementary schools in Austin, Texas. Objectively-measured built and natural environmental data were derived from two software programs: Geographic Information Systems (GIS) and Environment for Visualizing Images (ENVI). Ordinal least square regressions were used for statistical analyses in this study. Results from the fully adjusted final model showed that bike lanes, the presence of highway and railroads, the presence of sex offenders, and steep slopes along the home-to-school route were associated with increased parental safety concerns, while greater intersection density and greater tree canopy coverage along the route were associated with decreased parental safety concerns. Natural elements and walking-friendly elements of the built environment appear important in reducing parental safety concerns, which is a necessary step toward promoting children's ATS., Competing Interests: The authors declare no conflict of interest.

A severely cold climate has a significant impact on cardiovascular health, involving temperature, air environment, exercise and diet. Existing studies have revealed that green space, as an important health resource, may play a positive role in promoting cardiovascular health through the air environment and exercise. Studies focusing on the correlation between green space and cardiovascular health are rarely carried out in winter cities. The purpose of this paper is to take a winter city in China as an empirical case to explore the correlation between green space in a neighbourhood and cardiovascular health in a representative sample at the neighbourhood level, combining the results with Urban Residential Area Planning and Design Standards (GB50180-2018) in China and the existing research. The results showed that green space characteristics of a neighbourhood were related to cardiovascular disease and some of its risk factors. In neighbourhoods with a Green Space Ratio lower than 28%, residents had a higher risk of physical inactivity, overweight or obesity, hypertension and stroke. In neighbourhoods with a Green View Index lower than 15%, residents had a higher risk of physical inactivity, overweight/obesity, hypertension, dyslipidemia and stroke. A correlation was found between evergreen tree configuration type and the prevalence of overweight/obesity and hypertension. No correlation was found between the type of sports field and cardiovascular disease and its risk factors, except for hypertension. Residents' cardiovascular health scores also showed significant differences among neighbourhoods with different green space characteristics. Intervention efforts may benefit from emphasising the importance of improving the Green Space Ratio and Green View Index effectively in a neighbourhood to reduce the risk of cardiovascular disease.

Objective: With every change in a roadway to improve the network operational performance, the roadway safety behavior is impacted. Part-time shoulder use (PTSU) is one strategy that significantly impacts the operational performance of a roadway for several hours each workday. This study evaluates the impact of PTSU on safety behavior and the possibility of crash prevention., Method: Current predictive safety measures do not account for the part-time dimensional changes of PTSU effectively. Using Vissim-based microsimulation trajectory data in the Surrogate Safety Assessment Model (SSAM) program, developed by the Federal Highway Administration, this study gauged the safety disposition of Interstate 476 (I-476) in southeast Pennsylvania (PA). The two key safety evaluation factors include the number of conflicts and the time-to-collision (TTC) of each conflict. The number of conflicts represents unsafe scenarios that could become crashes if neither possibly impacted vehicles adjusts their route. TTC is the minimum time for the conflict to become a crash if either vehicle's route was not adjusted to avoid the collision. Both factors were averaged across six 20-min periods during the 2-h commuting period simulated., Results: Due to the improved operational performance, the TTC values for the PTSU scenarios decreased, reducing safety; however, the number of conflicts occurring reduced significantly, counterbalancing the reduced safety from the TTC reduction., Conclusions: PTSU has great operational improvement effects which create a faster-moving transportation network. The higher speeds caused by the enhanced operations increases the risk of a crash during unsafe situations, but the number of unsafe situations occurring will significantly reduce. The reduced number of unsafe situations indicates an increased probability of crash avoidance but does not indicate a definitive reduction in crashes.

Objective: Factors related to the driver-vehicle-environment system have a significant influence on a driver's decision to perform evasive maneuvers, especially the decision of steering direction (DSD) in critical situations. However, few studies have systematically investigated the relationships between these factors and DSD. The objective of this study is to analyze and model drivers' DSD in critical situations. Methods: Data from the NASS-CDS from 1995 to 2015 were utilized in this study. The decision tree (DT) classifier was utilized to model a driver's DSD for both intersection-related and non-intersection-related subsets, combined with a 10-fold cross-validation technique and grid search approach to evaluate and optimize the model. An analysis of variable importance was also conducted. Results: Two separate DT models of drivers' DSD were obtained based on the optimized hyperparameters, with test accuracies of 84.6% (intersection-related) and 79.2% (non-intersection-related). The variable DIFFANGLE (angle difference between 2 vehicles) ranked as the most important factor influencing drivers' DSD in both models. The variables, in order of importance, were SPEED (travel speed of the subject vehicle) and AGE (driver's age) for the intersection-related model and SPEED, PREMOVE (pre-event movement), TRAFFLOW (trafficway flow), and AGE for the non-intersection-related model. Moreover, an interesting same direction pattern was observed in both DT models. Conclusions: This study employed NASS-CDS data and DT classifiers to analyze and model drivers' DSD behavior. The test accuracies for both classifiers were acceptable. Potential variables influencing drivers' DSD were explored, which improves the research on evasive behavior in lateral movement and promotes further applications for intelligent vehicles using the constructed models.

Objective: To identify driving characteristics of fit-, unfit-, and conditionally fit-to-drive neurological patient populations using a driving simulator with three high-risk scenarios comprising rural, highway, and urban environments. Methods: The study included 91 neurological patients undergoing a multidisciplinary assessment for driver's license revalidation, consisting of a clinical, neuropsychological, functional, and on-road evaluation. The groups drove through three independent driving scenarios, during which a variety of measures describing reaction time, vehicular control, and traffic rule compliance were performed. One-way analysis of variance (ANOVA) with Bonferroni correction was used for group comparison, independently for each driving scenario, and Pearson correlations were calculated between simulator variables and neuropsychological test scores. Results: The fit- and unfit-to-drive population significantly differed ( p < .05 ) in reaction times, regardless of the scenario. No significant differences in traffic rule compliance or vehicular control parameters were observed in the rural environment ( p > .05 ). On the highway, the unfit group exhibited greater variability of steering wheel angle, higher steering reversal rate, and a higher rate of turn signal errors. In the urban environment, the unfit group oversped more, had more collisions, and exhibited greater lane position variability. The latter, along with reaction times in the rural and highway scenarios, was also shown to significantly differ between the conditional and unfit group ( p < .05 ). No significant differences were observed between the fit and the conditional group ( p > .05 ). Weak to moderate associations ( range: -0.5 to 0.29 ) between neuropsychological tests and various simulator variables were also observed. Conclusions : Our results show that driving simulators are able to capture differences between (fit- and unfit-to-drive) neurological patient populations and therefore bear the potential for being used as a deficit-independent screening, assessment, or rehabilitation tool. The conditionally-fit-to-drive group exhibited less discriminative features, which points to greater importance of human judgment for this population. The observation that differences in most of the parameters were environment-dependent suggests that developers of future driver simulation tools should carefully design scenarios in order to fully exploit their assessment potential.