Forecasting road accident casualties in Great Britain.

There is considerable interest in many countries in the way in which their annual road accident casualty totals have developed. This leads inevitably to the question of how these totals are likely to change in the future. This paper assesses national data for Great Britain from 1949-1989 and forecasts the casualty total in the year 2000 by extrapolating the long-term decline in the rate of casualties per hundred million vehicle-kilometres. This forecast is conditional on the traffic growth predicted by that year, but it is found that uncertainty over the casualty forecast derives more from the slight irregularities in the past decline in the casualty rate than from uncertainty over the traffic prediction. Despite the use of a forecasting method that is based on an unusually strong time-series model, there is still real uncertainty about forecasting casualties over a gap of only 11 years.     

Using multiple datasets to understand trends in serious road traffic casualties

Accurate information on the incidence of serious road traffic casualties is needed to plan and evaluate prevention strategies. Traditionally police reported collisions are the only data used. This study investigate the extent to which understanding of trends in serious road traffic injuries is aided by the use of multiple datasets. Health and police datasets covering all or part of Great Britain from 1996-2003 were analysed. There was a significantly decreasing trend in police reported serious casualties but not in the other datasets. Multiple data sources provide a more complete picture of road traffic casualty trends than any single dataset. Increasing availability of electronic health data with developments in anonymised data linkage should provide a better platform for monitoring trends in serious road traffic casualties.     

Child pedestrian casualties and deprivation

The existence of an association between child pedestrian accidents and socio-economic deprivation in Great Britain is well established. The factors driving this association are complex and difficult to isolate. This study uses accident prediction models to investigate the links between child pedestrian casualties and a range of environmental and socio-economic factors commonly linked to deprived areas and people. Separate models are constructed relating to the areas in which the children become casualties, and the areas in which the children reside. Significant socio-economic factors include: single-parenthood, reliance on income support, and crime; and environmental factors include domestic garden area, junction density and pedestrian and vehicular flow density. The study found that factors pertaining to the local environment were more prevalent in the models considering accident locations, whilst socio-economic factors were of greater influence in the residency model.     

The influence of urban land-use on non-motorised transport casualties

The relationship between non-motorised road traffic casualties and land-use was investigated in two zones of approximately 8 km² in Newcastle upon Tyne, England. Road traffic accidents are, more usually, analysed in relation to traffic flow, on the assumption that the latter can be derived from land-use data. Here, a direct relationship between primary functional land-use and non-motorised casualties is estimated.

We review past work in this area. A shortcoming of casualty data is that it does not record the origin and destination of the journeys being undertaken when the accident occurred. A method was established to identify zones within which most accidents could reasonably be expected to be related to the land-uses within that zone.

Generalised linear models were developed using non-motorised casualties as the response variable, with primary functional land-use, population density and junction density as explanatory variables. Separate models were constructed for each combination of cyclists and pedestrians, adults and children, working and non-working hours in city centre and suburban analysis zones.

In general, the study found that pedestrian casualties in the city centre zone are particularly associated with an increase in retail and community land-use during working hours. In the city centre zone, out of working hours, an increase in retail land-use (almost certainly clubs and bars) is also associated with an increase in pedestrian casualties. An increase in cyclist casualties during working hours (in the non-pedestrianised area) is associated with an increase in retail land-use.     

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.     

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 benefits of improved car secondary safety

The term 'secondary safety' refers to the protection that a vehicle provides its occupants when involved in an accident. This paper studies information from the British database of road accident reports between 1980 and 1998, to estimate the reduction in the number of occupant casualties over these years which may be attributed to improvements to secondary safety in cars.The paper shows that the proportion of driver casualties who are killed or seriously injured (KSI) is lower for modern cars than for older cars. The reduction of this proportion is used to assess the improvement in secondary safety. Statistical models are developed to represent the proportion with 'year of first registration' as one of the independent variables, although only an incomplete assessment of the benefits of improved secondary safety can be made with the available data. The assessment compares the number of casualties that would have been expected if secondary safety had remained at the level found in cars first registered in 1980 with the actual casualty numbers. It is estimated that improved secondary safety reduced the number of drivers KSI by at least 19.7% in 1998, in comparison with what might have occurred if all cars had had that lower level of secondary safety. This figure relates to all cars on the road in 1998, and rises to 33%, when confined to the most modern cars (those which were first registered in 1998).     

Epidemiology of child pedestrian casualty rates: can we assume spatial independence?

Child pedestrian injuries are often investigated by means of ecological studies, yet are clearly part of a complex spatial phenomena. Spatial dependence within such ecological analyses have rarely been assessed, yet the validity of basic statistical techniques rely on a number of independence assumptions. Recent work from Canada has highlighted the potential for modelling spatial dependence within data that was aggregated in terms of the number of road casualties who were resident in a given geographical area. Other jurisdictions aggregate data in terms of the number of casualties in the geographical area in which the collision took place. This paper contrasts child pedestrian casualty data from Devon County UK, which has been aggregated by both methods. A simple ecological model, with minimally useful covaraties relating to measures of child deprivation, provides evidence that data aggregated in terms of the casualty's home location cannot be assumed to be spatially independent and that for analysis of these data to be valid there must be some accounting for spatial auto-correlation within the model structure. Conversely, data aggregated in terms of the collision location (as is usual in the UK) was found to be spatially independent. Whilst the spatial model is clearly more complex it provided a superior fit to that seen with either collision aggregated or non-spatial models. Of more importance, the ecological level association between deprivation and casualty rate is much lower once the spatial structure is accounted for, highlighting the importance using appropriately structured models.     

Estimating the human recovery costs of seriously injured road crash casualties

Road crashes result in substantial trauma and costs to societies around the world. Robust costing methods are an important tool to estimate costs associated with road trauma, and are key inputs into policy development and cost–benefit analysis for road safety programmes and infrastructure projects. With an expanding focus on seriously injured road crash casualties, in addition to the long standing focus on fatalities, methods for costing seriously injured casualties are becoming increasingly important. Some road safety agencies are defining a seriously injured casualty as an individual that was admitted to hospital following a road crash, and as a result, hospital separation data provide substantial potential for estimating the costs associated with seriously injured road crash casualties. The aim of this study is to establish techniques for estimating the human recovery costs of (non-fatal) seriously injured road crash casualties directly from hospital separation data. An individuals’ road crash-related hospitalisation record and their personal injury insurance claim were linked for road crashes that occurred in New South Wales, Australia. These records provided the means for estimating all of the costs to the casualty directly related to their recovery from their injuries. A total of 10,897 seriously injured road crash casualties were identified and four methods for estimating their recovery costs were examined, using either unit record or aggregated hospital separation data. The methods are shown to provide robust techniques for estimating the human recovery costs of seriously injured road crash casualties, that may prove useful for identifying, implementing and evaluating safety programmes intended to reduce the incidence of road crash-related serious injuries.     

Contributory factors to traffic crashes at signalized intersections in Hong Kong

Efficient geometric design and signal timing not only improve operational performance at signalized intersections by expanding capacity and reducing traffic delays, but also result in an appreciable reduction in traffic conflicts, and thus better road safety. Information on the incidence of crashes, traffic flow, geometric design, road environment, and traffic control at 262 signalized intersections in Hong Kong during 2002 and 2003 are incorporated into a crash prediction model. Poisson regression and negative binomial regression are used to quantify the influence of possible contributory factors on the incidence of killed and severe injury (KSI) crashes and slight injury crashes, respectively, while possible interventions by traffic flow are controlled. The results for the incidence of slight injury crashes reveal that the road environment, degree of curvature, and presence of tram stops are significant factors, and that traffic volume has a diminishing effect on the crash risk. The presence of tram stops, number of pedestrian streams, road environment, proportion of commercial vehicles, average lane width, and degree of curvature increase the risk of KSI crashes, but the effect of traffic volume is negligible.     

Analyzing and modeling risk exposure of pedestrian children to involvement in car crashes

This paper analyzes the various variables affecting pedestrian children road crashes, placing emphasis on the effect of daily activity patterns and the built environment, including the children's residential neighborhoods and the land use of the places where they conduct their activities. Two complementary data sources from the case study of an Arab town in northern Israel were used to provide a holistic picture of child-pedestrian road crashes: police files providing detailed analyzes of the reason for each crash, its location, and the characteristics of the driver involved; and a survey of 199 households with both involved and not involved children in road crashes, including a one-day travel diary. The study found that a combination of three groups of variables affects child-pedestrian road crashes: socio-economic status, travel patterns, and land use. Most vulnerable are boys from a low socio-economic group who live in areas of high density and mixed land use near a major road and who tend to walk to and from school and additional activities after school.     

Macro-level safety analysis of pedestrian crashes in Shanghai,China

Pedestrian safety has become one of the most important issues in the field of traffic safety. This study aims at investigating the association between pedestrian crash frequency and various predictor variables including roadway, socio-economic, and land-use features. The relationships were modeled using the data from 263 Traffic Analysis Zones (TAZs) within the urban area of Shanghai – the largest city in China. Since spatial correlation exists among the zonal-level data, Bayesian Conditional Autoregressive (CAR) models with seven different spatial weight features (i.e. (a) 0–1 first order, adjacency-based, (b) common boundary-length-based, (c) geometric centroid-distance-based, (d) crash-weighted centroid-distance-based, (e) land use type, adjacency-based, (f) land use intensity, adjacency-based, and (g) geometric centroid-distance-order) were developed to characterize the spatial correlations among TAZs. Model results indicated that the geometric centroid-distance-order spatial weight feature, which was introduced in macro-level safety analysis for the first time, outperformed all the other spatial weight features. Population was used as the surrogate for pedestrian exposure, and had a positive effect on pedestrian crashes. Other significant factors included length of major arterials, length of minor arterials, road density, average intersection spacing, percentage of 3-legged intersections, and area of TAZ. Pedestrian crashes were higher in TAZs with medium land use intensity than in TAZs with low and high land use intensity. Thus, higher priority should be given to TAZs with medium land use intensity to improve pedestrian safety. Overall, these findings can help transportation planners and managers understand the characteristics of pedestrian crashes and improve pedestrian safety.     

Under-reporting of road crash casualties in France

INTRODUCTION: Police crash data, which are the basis for safety research in most countries, are incomplete and biased. We focus here on the extent of under-reporting in France, and how it is related to casualty and crash characteristics. METHODS: Police data are compared with a road trauma registry, on the Rh?ne county, after record-linkage. The study covers the 1997-2001 period, totalling 59,714 casualties. A multivariate analysis is conducted, modelling the probability of being police-reported among casualties in the registry, as a function of crash and casualty characteristics. Results are expressed as relative risks (RR) and adjusted probabilities. RESULTS: Police reporting rate is 37.7%. Under-reporting varies mainly according to injury severity (RR=0.35 for slightly injured versus severely injured), to road user type and third party involvement (yes/no): comparing casualties with no third party versus those with one, RR=0.32 among motorcyclists whereas RR=0.78 among car occupants. Under-reporting also varies with road type, road environment (metropole/rural) and type of police force, all of which are structurally dependent. CONCLUSION: Any study based on police crash data may be quite misleading. We are therefore working on obtaining unbiased estimates of road casualties figures, by extrapolating the Rh?ne road trauma registry to the nation-wide level.     

Traffic signal phasing at intersections to improve safety for alcohol-affected pedestrians

Alcohol-affected pedestrians are among the highest-risk groups involved in pedestrian casualty crashes. This paper investigates the opportunities to use a modified form of traffic signal operation during high-risk periods and at high-risk locations to reduce alcohol-affected pedestrian crashes and the severity of injuries that might otherwise occur. The 'Dwell-on-Red' treatment involves displaying a red traffic signal to all vehicle directions during periods when no vehicular traffic is detected, so that drivers approach high-risk intersections at a lower speed than if a green signal were displayed. Vehicle speed data were collected before and after treatment activation at both a control and treatment site. Speed data were collected both 30 m prior to and at the intersection stop line. The treatment was associated with a reduction in mean vehicle speeds of 3.9 kph (9%) and 11.0 kph (28%) at 30 m and stop line collection points, respectively, and substantial reductions in the proportion of vehicles travelling at threatening speeds with regard to the severity of pedestrian injury. Other important road safety concerns may also benefit from this form of traffic signal modification, and it is recommended that other areas of application be explored, including the other severe trauma categories typically concentrated around signalised intersections.     

Sustainable road safety: A new (?) neighbourhood road pattern that saves VRU lives

Both the UN (2007) and World Health Organizations (2004) have declared the enormous social and economic burden imposed on society by injuries due to road collisions as a major global problem. While the road safety problem is not new, this prominent global declaration sends an important signal of frustration regarding progress to date on reducing road collisions. It is clear that governments, communities, businesses and the public must discover ways of reducing this burden, especially as it relates to vulnerable road users (VRUs), typically meaning pedestrian and bicyclist road users. Recent comparisons of global VRU collisions statistics suggest that, in addition to mixed land use density, the layout of neighbourhood roads plays a vital role in the encouragement of walkable, safe and quiet, yet accessible and sustainable communities. The purpose of this paper was to: The Dutch Sustainable Road Safety (SRS) Program has produced a number of innovative land use and transportation initiatives for vehicular road users as well as non-vehicular VRUs. Following from the Dutch initiatives, these new 3-way offset, and fused grid neighbourhood patterns appear to not only have positive effects in encouraging mode split (i.e. increasing walking and bicycling, and transit), slowing traffic, and reducing energy consumption and GHG emissions; but also, to hold potential to improve road safety. To test the road safety hypothesis, UBCO researchers evaluated the level of road safety relative to five neighbourhood patterns - grid, culs-de-sac, and Dutch Sustainable Road Safety (SRS) (or limited access), 3-way offset, and fused grid networks. Analysis using standard transportation planning methodology revealed that they would maintain both mobility and accessibility. Analysis using standard road safety analysis methodology further revealed that these 3-way offset, and fused grid patterns would significantly improve road safety levels by as much as 60% compared to prevalent patterns (i.e. grid and culs-de-sac). It is important to note that these results ignore the road safety effects of providing convenient off-road trails along trip desire lines to shift mode choice from auto to non-auto VRU modes. Subject to further research, it is intuitive that shifting trips from auto to pedestrian/bike modes will lead to reduced auto collisions. Hence, these initial results should be considered as conservative estimates, subject to further research. In before and after studies to date, researchers have shown that increasing bicycle use does not lead to a commensurate increase in bicycle collisions, but no predictive relationship has been found in the literature. Therefore, the next steps in this research are to develop collision prediction models that provide insight on VRU mode split and overall road safety.     

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.     

Modelling area-wide count outcomes with spatial correlation and heterogeneity: an analysis of London crash data

Count models such as negative binomial (NB) regression models are normally employed to establish a relationship between area-wide traffic crashes and the contributing factors. Since crash data are collected with reference to location measured as points in space, spatial dependence exists among the area-level crash observations. Although NB models can take account of the effect of unobserved heterogeneity (due to omitted variables in the model) among neighbourhoods, such models may not account for spatial correlation areas. It is then essential to adopt an econometric model that takes account of both spatial dependence and uncorrelated heterogeneity simultaneously among neighbouring units. In studying the spatial pattern of traffic crashes, two types of spatial models may be employed: (i) classical spatial models for higher levels of spatial aggregation such as states, counties, etc. and (ii) Bayesian hierarchical models for all spatial units, especially for smaller scale area-aggregations. Therefore, the primary objectives of this paper is to develop a series of relationships between area-wide different traffic casualties and the contributing factors associated with ward characteristics using both non-spatial models (such as NB models) and spatial models and to identify the similarities and differences among these relationships. The spatial units of the analysis are the 633 census wards from the Greater London metropolitan area. Ward-level casualty data are disaggregated by severity of the casualty (such as fatalities, serious injuries, and slight injuries) and by severity of the casualty related to various road users. The analysis implies that different ward-level factors affect traffic casualties differently. The results also suggest that Bayesian hierarchical models are more appropriate in developing a relationship between area-wide traffic crashes and the contributing factors associated with the road infrastructure, socioeconomic and traffic conditions of the area. This is because Bayesian models accurately take account of both spatial dependence and uncorrelated heterogeneity.     

Child pedestrian casualties with special reference to casualties on the journey to or from school in Manchester and Salford, England

There are two separate facets of the casualty rates for child pedestrians in Great Britain that require explanation. (1) The casualty rate for child pedestrians aged 11-14 years is higher than the rate for those aged 5-10 years. In Manchester and Salford the casualty rate for the older age group is higher for children on the journey to or from school but at nonschool times the casualty rate is lower for those aged 11-14 years than for those aged 5-10 years. It is suggested that the high casualty rate for older children is connected with the school journey, which is longer to secondary than to primary schools. (2) The casualty rate for the older age group has increased in recent years. Comparing the casualty rates at school and nonschool times in Manchester and Salford in 1969 and 1987 suggests that this increase is due to an increase in accidents on the school journey.     

Multilevel models for evaluating the risk of pedestrian–motor vehicle collisions at intersections and mid-blocks

Walking is a popular form of physical activity associated with clear health benefits. Promoting safe walking for pedestrians requires evaluating the risk of pedestrian–motor vehicle collisions at specific roadway locations in order to identify where road improvements and other interventions may be needed. The objective of this analysis was to estimate the risk of pedestrian collisions at intersections and mid-blocks in Seattle, WA. The study used 2007–2013 pedestrian–motor vehicle collision data from police reports and detailed characteristics of the microenvironment and macroenvironment at intersection and mid-block locations. The primary outcome was the number of pedestrian–motor vehicle collisions over time at each location (incident rate ratio [IRR] and 95% confidence interval [95% CI]). Multilevel mixed effects Poisson models accounted for correlation within and between locations and census blocks over time. Analysis accounted for pedestrian and vehicle activity (e.g., residential density and road classification). In the final multivariable model, intersections with 4 segments or 5 or more segments had higher pedestrian collision rates compared to mid-blocks. Non-residential roads had significantly higher rates than residential roads, with principal arterials having the highest collision rate. The pedestrian collision rate was higher by 9% per 10 feet of street width. Locations with traffic signals had twice the collision rate of locations without a signal and those with marked crosswalks also had a higher rate. Locations with a marked crosswalk also had higher risk of collision. Locations with a one-way road or those with signs encouraging motorists to cede the right-of-way to pedestrians had fewer pedestrian collisions. Collision rates were higher in locations that encourage greater pedestrian activity (more bus use, more fast food restaurants, higher employment, residential, and population densities). Locations with higher intersection density had a lower rate of collisions as did those in areas with higher residential property values. The novel spatiotemporal approach used that integrates road/crossing characteristics with surrounding neighborhood characteristics should help city agencies better identify high-risk locations for further study and analysis. Improving roads and making them safer for pedestrians achieves the public health goals of reducing pedestrian collisions and promoting physical activity.     

Traffic analysis zone level crash estimation models based on land use characteristics

The objective of this paper is to develop crash estimation models at traffic analysis zone (TAZ) level as a function of land use characteristics. Crash data and land use data for the City of Charlotte, Mecklenburg County, North Carolina were used to illustrate the development of TAZ level crash estimation models. Negative binomial count models (with log-link) were developed as data was observed to be over-dispersed. Demographic/socio-economic characteristics such as population, the number of household units and employment, traffic indicators such as trip productions and attractions, and, on-network characteristics such as center-lane miles by speed limit were observed to be correlated to land use characteristics, and, hence were not considered in the development of TAZ level crash estimation models. Urban residential commercial, rural district and mixed use district land use variables were observed to be correlated to other land use variables and were also not considered in the development of the models. Results obtained indicate that land use characteristics such as mixed use development, urban residential, single-family residential, multi-family residential, business and, office district are strongly associated and play a statistically significant role in estimating TAZ level crashes. The coefficient for single-family residential area was observed to be negative, indicating a decrease in the number of crashes with an increase in single-family residential area. Models were also developed to estimate these crashes by severity (injury and property damage only crashes). The outcomes can be used in safety conscious planning, land use decisions, long range transportation plans, and, to proactively apply safety treatments in high risk TAZs.