Decomposing the impact of deprivation on child pedestrian casualties in England

The incidence of child injury due to road traffic accidents appears to be positively associated with socio-economic deprivation. However, the reasons behind this relationship have proven difficult to identify. In this paper, we present results from a cross-sectional analysis using generalized linear regression models of child pedestrian casualties for the wards of England. We find that there are distinct and substantial effects on casualty rates from characteristics associated with area deprivation across diverse environments. These associations exist over and above influences arising from local environmental characteristics. Distinct dimensions of deprivation appear to affect the incidence of pedestrian casualties to varying degrees and sometimes in different directions. The results identify a relationship between income deprivation and the incidence of child pedestrian casualties, but they also show that poverty is only one aspect of deprivation that matters. In particular, we find a consistent positive influence from crime related deprivation.     

The role of Body Mass Index in child pedestrian injury risk

The goal of the current investigation was to examine obesity as a potential risk factor for childhood pedestrian injury. A racially diverse sample of 7- and 8-year-old children completed a road-crossing task in a semi-immersive virtual environment and two pedestrian route selection tasks. Multiple linear regression analyses revealed that children with a higher Body Mass Index (BMI) waited less before crossing, had a smaller temporal buffer between themselves and oncoming traffic while crossing, and had more collisions with traffic. Girls were more cautious than boys when crossing the virtual roadway. Unlike the results from the virtual road-crossing task, BMI was not associated with risky route selection. Instead, race emerged as the strongest predictor, with African–American children selecting riskier routes for crossing. Together, these findings suggest overweight and obese children may be at increased risk for pedestrian injury. The discussion considers explanations for why obese children may exhibit riskier road-crossing behavior.     

A spatial and temporal analysis of child pedestrian crashes in Santiago,Chile

This paper presents a spatial and temporal analysis of child pedestrian crash data in Santiago, Chile during the period 2000–2008. First, this study identified seven critical areas with high child pedestrian crash risk employing kernel density estimation, and subsequently, statistically significant clusters of the main attributes associated to these crashes in each critical area were determined in a geographic information systems environment. Moran's I index test identified a positive spatial autocorrelation on crash contributing factors, time of day, straight road sections and intersections, and roads without traffic signs within the critical areas during the studied period, whereas a random spatial pattern was identified for crashes related to the age attribute. No statistical significance in the spatial relationship was obtained in child pedestrian crashes with respect to gender, weekday, and month of the year. The results from this research aid in determining the areas in which enhanced school-age child pedestrian safety is required by developing and implementing effective enforcement, educational, and engineering preventive measures.     

The effects of area deprivation on the incidence of child and adult pedestrian casualties in England

This paper analyses child pedestrian casualties in England, focusing on the influence of socio-economic deprivation. It develops an area-based model of pedestrian casualties and presents estimates based on data for the English wards. The results detect an association between increased deprivation and higher numbers of pedestrian casualties across England. The deprivation effect is strong both for all child casualties and for children killed or seriously injured. Estimates for adult casualties also reveal a positive and significant association with increasing deprivation, but the magnitude of the effect is smaller than for children. The paper concludes by outlining some of the implications of the research.     

The effects of mobile phone use on pedestrian crossing behaviour at signalized and unsignalized intersections

Research amongst drivers suggests that pedestrians using mobile telephones may behave riskily while crossing the road, and casual observation suggests concerning levels of pedestrian mobile-use. An observational field survey of 270 females and 276 males was conducted to compare the safety of crossing behaviours for pedestrians using, versus not using, a mobile phone. Amongst females, pedestrians who crossed while talking on a mobile phone crossed more slowly, and were less likely to look at traffic before starting to cross, to wait for traffic to stop, or to look at traffic while crossing, compared to matched controls. For males, pedestrians who crossed while talking on a mobile phone crossed more slowly at unsignalized crossings. These effects suggest that talking on a mobile phone is associated with cognitive distraction that may undermine pedestrian safety. Messages explicitly suggesting techniques for avoiding mobile-use while road crossing may benefit pedestrian safety.     

A spatially disaggregate analysis of road casualties in England

Spatially disaggregate ward level data for England is used in an analysis of various area-wide factors on road casualties. Data on 8414 wards was input into a geographic information system that contained data on land use types, road characteristics and road casualties. Demographic data on area-wide deprivation (the index of multiple deprivation) for each ward was also included. Negative binomial count data models were used to analyze the associations between these factors with traffic fatalities, serious injuries and slight injuries. Results suggest that urbanized areas are associated with fewer casualties (especially fatalities) while areas of higher employment density are associated with more casualties. More deprived areas tend to have higher levels of casualties, though not of motorized casualties (except slight injuries). The effect of road characteristics are less significant but there are some positive associations with the density of “A” and “B” level roads.     

Modelling the hierarchical structure of road crash data--application to severity analysis

Road crashes have an unquestionably hierarchical crash-car-occupant structure. Multilevel models are used with correlated data, but their application to crash data can be difficult. The number of sub-clusters per cluster is small, with less than two cars per crash and less than two occupants per car, whereas the number of clusters can be high, with several hundred/thousand crashes. Application of the Monte-Carlo method on observed and simulated French road crash data between 1996 and 2000 allows comparing estimations produced by multilevel logistic models (MLM), Generalized Estimating Equation models (GEE) and logistic models (LM). On the strength of a bias study, MLM is the most efficient model while both GEE and LM underestimate parameters and confidence intervals. MLM is used as a marginal model and not as a random-effect model, i.e. only fixed effects are taken into account. Random effects allow adjusting risks on the hierarchical structure, conferring an interpretative advantage to MLM over GEE. Nevertheless, great care is needed for data coding and quite a high number of crashes are necessary in order to avoid problems and errors with estimates and estimate processes. On balance, MLM must be used when the number of vehicles per crash or the number of occupants per vehicle is high, when the LM results are questionable because they are not in line with the literature or finally when the p-values associated to risk measures are close to 5%. In other cases, LM remains a practical analytical tool for modelling crash data.     

Pedestrian self-reports of factors influencing the use of pedestrian bridges

The study was designed to find out factors that influence use/non-use of pedestrian bridges. The use rate of five pedestrian bridges was observed in the central business district (CBD) of Ankara. After the observations, a survey was conducted among pedestrians using those bridges and crossing contrary to safe practice under them at street level (n=408). In the present data, the use rate of pedestrian bridges varied from 6 to 63%. The frequent use of the bridge when crossing the road concerned, and seeing bridge use as time saving and safe in general were positively related to respondents' bridge use. Frequent visits to CBD decreased the likelihood of using the bridge. Other factors accounted only for a small proportion of variance in bridge use. The study suggests that bridge use or non-use is a habit and not coincidental behaviour. For increasing the pedestrians' bridge use, escalators seem to be a good solution, but traffic signals under a bridge may deteriorate the use rate. In addition, increasing the number of legs leading to the bridge may not increase the use rate. The use rate is likely to improve, if the safety benefits and convenience of using the bridge without considerable time loss are clearly visible to pedestrians.     

The influence of demographic factors,processing speed and short-term memory on Iranian children's pedestrian skills

Objectives

Young children, children from lower socioeconomic status and boys have the highest risk of pedestrian injury. This study examined the relationship between cognition and specific pedestrian skills of these groups of children in Iran.

Methods

180 Iranian children aged 7 and 11 years from lower- and higher-socioeconomic status backgrounds participated in the study. A task to identify safe and dangerous road crossing sites and to plan a safe route to cross a road was administered to measure pedestrian skills. Coding and Digit Span subscales of WISC-R were administered to assess processing speed and short-term memory.

Results

Identifying safe/dangerous road crossing-sites and safe route-construction abilities increased with age. Boys scored higher than girls when identifying road crossing sites but did not differ to girls in route-construction. Lower socioeconomic status children scored higher than higher socioeconomic status children on the route-construction task. Girls from lower socioeconomic status backgrounds scored lowest on the identifying safe/dangerous sites task and girls from higher socioeconomic status backgrounds scored lowest on the route construction task. Speed of processing was a significant predictor for identifying crossing sites and socioeconomic status was a significant predictor for route-construction.

Conclusions

Pedestrian skills are complex and influenced by age, gender, socioeconomic status and cognitive development. Results are discussed in relation to child pedestrian safety research in Iran and road safety education for children.     

Field use patterns and performance of child restraints secured by lower anchors and tethers for children (LATCH)

A revised attachment system for child restraint systems (CRS), Lower Anchors and Tethers for Children (LATCH), was introduced to address issues of misuse of attachment of the CRS to the vehicle. This study provides the first real-world experience of this new restraint system. Relevant cases were selected from the partners for child passenger safety (PCPS) child-specific crash surveillance system based in the United States. Vehicles qualifying for the PCPS study were those model year 1990 or newer involved in a crash with at least one passenger 相似文献   

Modelling the factors related to the seatbelt use by the young drivers of Athens

Road traffic accidents in Greece are one of the major problems of the public health sector and the first cause of death in the ages 18–24. However, there are no records available for defining the determinants of road accidents and seatbelt wearing rates. The main objective of this study is to determine and clarify the relationship between young drivers’ intentions (motivation to use/non use seatbelt) and their behaviour (self-reported use). Additionally, the purpose of this study is to evaluate the seatbelt wearing rates among young drivers in relation to their trip-type. The sample consisted of 200 young Greek drivers of both sexes. The statistical analysis included factor analysis and multiple regression analysis. The seatbelt use was measured in relation with seven trip-types. Through factor analysis, a seven factor scale of seatbelt use and a four factor scale of seatbelt non use were created which included Greek young drivers’ basic motivations for wearing or not wearing a seatbelt. A model, constructed by the multiple regression analysis, revealed the factors related with the seatbelt use. The factors positively related were ‘imitation’, ‘self-protection’, and ‘legality’. The factor of ‘discomfort’ is negatively associated with the seatbelt use. Furthermore, mileage was negatively related with seatbelt use. Finally, some preliminary suggestions on how prevention strategies should be implemented in Greece are discussed.     

An empirical tool to evaluate the safety of cyclists: Community based,macro-level collision prediction models using negative binomial regression

Today, North American governments are more willing to consider compact neighborhoods with increased use of sustainable transportation modes. Bicycling, one of the most effective modes for short trips with distances less than 5 km is being encouraged. However, as vulnerable road users (VRUs), cyclists are more likely to be injured when involved in collisions. In order to create a safe road environment for them, evaluating cyclists’ road safety at a macro level in a proactive way is necessary. In this paper, different generalized linear regression methods for collision prediction model (CPM) development are reviewed and previous studies on micro-level and macro-level bicycle-related CPMs are summarized. On the basis of insights gained in the exploration stage, this paper also reports on efforts to develop negative binomial models for bicycle–auto collisions at a community-based, macro-level. Data came from the Central Okanagan Regional District (CORD), of British Columbia, Canada. The model results revealed two types of statistical associations between collisions and each explanatory variable: (1) An increase in bicycle–auto collisions is associated with an increase in total lane kilometers (TLKM), bicycle lane kilometers (BLKM), bus stops (BS), traffic signals (SIG), intersection density (INTD), and arterial–local intersection percentage (IALP). (2) A decrease in bicycle collisions was found to be associated with an increase in the number of drive commuters (DRIVE), and in the percentage of drive commuters (DRP). These results support our hypothesis that in North America, with its current low levels of bicycle use (<4%), we can initially expect to see an increase in bicycle collisions as cycle mode share increases. However, as bicycle mode share increases beyond some unknown ‘critical’ level, our hypothesis also predicts a net safety improvement. To test this hypothesis and to further explore the statistical relationships between bicycle mode split and overall road safety, future research needs to pursue further development and application of community-based, macro-level CPMs.     

Dynamic compositional modeling of pedestrian crash counts on urban roads in Connecticut

Uncovering the temporal trend in crash counts provides a good understanding of the context for pedestrian safety. With a rareness of pedestrian crashes it is impossible to investigate monthly temporal effects with an individual segment/intersection level data, thus the time dependence should be derived from the aggregated level data. Most previous studies have used annual data to investigate the differences in pedestrian crashes between different regions or countries in a given year, and/or to look at time trends of fatal pedestrian injuries annually. Use of annual data unfortunately does not provide sufficient information on patterns in time trends or seasonal effects. This paper describes statistical methods uncovering patterns in monthly pedestrian crashes aggregated on urban roads in Connecticut from January 1995 to December 2009. We investigate the temporal behavior of injury severity levels, including fatal (K), severe injury (A), evident minor injury (B), and non-evident possible injury and property damage only (C and O), as proportions of all pedestrian crashes in each month, taking into consideration effects of time trend, seasonal variations and VMT (vehicle miles traveled). This type of dependent multivariate data is characterized by positive components which sum to one, and occurs in several applications in science and engineering. We describe a dynamic framework with vector autoregressions (VAR) for modeling and predicting compositional time series. Combining these predictions with predictions from a univariate statistical model for total crash counts will then enable us to predict pedestrian crash counts with the different injury severity levels. We compare these predictions with those obtained from fitting separate univariate models to time series of crash counts at each injury severity level. We also show that the dynamic models perform better than the corresponding static models. We implement the Integrated Nested Laplace Approximation (INLA) approach to enable fast Bayesian posterior computation.     

The use of videos in road safety training: Cognitive and emotional effects

The aim of the study was to evaluate the use of videos in educational programs for improving road safety. Particularly, we evaluated the cognitive and emotional effects of viewing videos taken from cameras placed along Italian highways and showing car crashes or traffic flow images.     

The influence of the infrastructure characteristics in urban road accidents occurrence

This paper summarizes the result of a study regarding the creation of tools that can be used in intervention methods in the planning and management of urban road networks in Portugal.     

An area-level model of vehicle-pedestrian injury collisions with implications for land use and transportation planning

There is growing awareness among urban planning, public health, and transportation professionals that design decisions and investments that promote walking can be beneficial for human and ecological health. Planners need practical tools to consider the impact of development on pedestrian safety, a key requirement for the promotion of walking. Simple bivariate models have been used to predict changes in vehicle-pedestrian injury collisions based on changes in traffic volume. We describe the development of a multivariate, area-level regression model of vehicle-pedestrian injury collisions based on environmental and population data in 176 San Francisco, California census tracts. Predictor variables examined included street, land use, and population characteristics, including commute behaviors. The final model explained approximately 72% of the systematic variation in census-tract vehicle-pedestrian injury collisions and included measures of traffic volume, arterial streets without transit, land area, proportion of land area zoned for neighborhood commercial and residential-neighborhood commercial uses, employee and resident populations, proportion of people living in poverty and proportion aged 65 and older. We have begun to apply this model to predict area-level change in vehicle-pedestrian injury collisions associated with land use development and transportation planning decisions.     

Modelling fatigue and the use of fatigue models in work settings

In recent years, theoretical models of the sleep and circadian system developed in laboratory settings have been adapted to predict fatigue and, by inference, performance. This is typically done using the timing of prior sleep and waking or working hours as the primary input and the time course of the predicted variables as the primary output. The aim of these models is to provide employers, unions and regulators with quantitative information on the likely average level of fatigue, or risk, associated with a given pattern of work and sleep with the goal of better managing the risk of fatigue-related errors and accidents/incidents. The first part of this review summarises the variables known to influence workplace fatigue and draws attention to the considerable variability attributable to individual and task variables not included in current models. The second part reviews the current fatigue models described in the scientific and technical literature and classifies them according to whether they predict fatigue directly by using the timing of prior sleep and wake (one-step models) or indirectly by using work schedules to infer an average sleep-wake pattern that is then used to predict fatigue (two-step models). The third part of the review looks at the current use of fatigue models in field settings by organizations and regulators. Given their limitations it is suggested that the current generation of models may be appropriate for use as one element in a fatigue risk management system. The final section of the review looks at the future of these models and recommends a standardised approach for their use as an element of the ‘defenses-in-depth’ approach to fatigue risk management.     

Modelling the effects of the Austrian inpatient reimbursement system on length-of-stay distributions

Abstract. This paper analyses the effects of the new Austrian performance-oriented inpatient payment system on discharge strategies of hospitals by investigating length-of-stay (LOS) distributions. Using generalised linear models applied to data from 1998, we calculate the impact of day and month of admission as well as types of admission and discharge on the LOS of inpatients with major diagnoses. Hereby, we prove significant interdependencies among these variables and reveal hospital behaviour such as 1) premature discharges of inpatients or “unbundling” and 2) “patient splitting”. Hence, our findings illustrate that hospitals react to incentives set by the new system in order to maximise their revenues. Hence, the current reimbursement system still has potential for cost reduction while maintaining high quality of health care. Our strategic policy model supports decision makers in disclosing these effects and provides policy implications to close exploitable gaps within the new reimbursement system. RID="We are grateful to experts of the federal funds and the Ministry of Labour, Health and Social Affairs for providing us with detailed information on the Austrian reimbursement system for inpatients and for approving our data request. In addition, we especially thank Dr. Wilhelm Frank of the Austrian Federal Institute for Health Care for making the needed inpatient data available for us. Special thanks are due to Professor Arjan Shahani, Paul Harper and Sally Brailsford (University of Southampton, United Kingdom), Professor Lucas Delesie (Catholic University of Leuven, Belgium), Professor Bernhard Schwarz (University of Vienna, Austria) and Professor Engelbert Dockner (University of Vienna, Austria) for valuable comments on this investigation. Correspondence to:M. Rauner