The effect of daily-activity patterns on crash involvement

The main purpose of this study is to analyze the effect of daily-activity and travel patterns on the risk of crash involvement. To this end, we develop a model that integrates daily-activity and travel choices in a single framework, recognizing that these variables affect the risk of crashes. This model can therefore provide predictions of the expected changes in risk levels from the implementation of measures that affect the daily-activity patterns and the socio-economic characteristics of the population.The empirical analysis makes use of data collected during a household survey that includes crash information and trip diaries. The model is applied in a case study of an Arab town in Israel to analyze various transportation policies. The results of this research show that in addition to individuals’ demographic and socio-economic characteristics, their daily-activity and travel patterns also have an impact on the risk of being involved in car crashes. The case study showed the potential of this framework for analyzing the effect of various social and transportation policies on road safety. To the best of our knowledge, this is the first time such relationships have been tested by using a disaggregate model and the first time activity-based models have been used to analyze exposure to the risk of road crashes.     

Pedestrian crash estimation models for signalized intersections

The focus of this paper is twofold: (1) to examine the non-linear relationship between pedestrian crashes and predictor variables such as demographic characteristics (population and household units), socio-economic characteristics (mean income and total employment), land use characteristics, road network characteristics (the number of lanes, speed limit, presence of median, and pedestrian and vehicular volume) and accessibility to public transit systems, and (2) to develop generalized linear pedestrian crash estimation models (based on negative binomial distribution to accommodate for over-dispersion of data) by the level of pedestrian activity and spatial proximity to extract site specific data at signalized intersections. Data for 176 randomly selected signalized intersections in the City of Charlotte, North Carolina were used to examine the non-linear relationships and develop pedestrian crash estimation models. The average number of pedestrian crashes per year within 200 feet of each intersection was considered as the dependent variable whereas the demographic characteristics, socio-economic characteristics, land use characteristics, road network characteristics and the number of transit stops were considered as the predictor variables. The Pearson correlation coefficient was used to eliminate predictor variables that were correlated to each other. Models were then developed separately for all signalized intersections, high pedestrian activity signalized intersections and low pedestrian activity signalized intersections. The use of 0.25 mile, 0.5 mile and 1 mile buffer widths to extract data and develop models was also evaluated.     

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.     

The influence of road curvature on fatal crashes in New Zealand

Bends in roads can cause crashes but a recent study in the UK found that areas with mostly curved roads had lower crash rates than areas with straighter roads. This present study aimed to replicate the previous research in a different country. Variations in the number of fatal road crashes occurring between 1996 and 2005 in 73 territorial local authorities across New Zealand were modelled against possible predictors. The predictors were traffic flow, population counts and characteristics, car use, socio-economic deprivation, climate, altitude and road characteristics including four measures of average road curvature. The best predictors of the number of fatal crashes on urban roads, rural state highways and other rural roads were traffic flow, speed limitation and socio-economic deprivation. Holding significant factors constant, there was no evidence that TLAs with the most curved roads had more crashes than elsewhere. Fatal crashes on urban roads were significantly and negatively related to two measures of road curvature: the ratio of road length to straight distance and the cumulative angle turned per kilometre. Weaker negative associations on rural state highways could have occurred by chance. These results offer limited support to the suggestion that frequently occurring road bends might be protective.     

Modelling the effects of land use and temporal factors on child pedestrian casualties

This study investigates the suitability of land use variables in predicting the number of child pedestrian casualties; a subject of concern in Great Britain despite sustained improvements in road safety over the past decade. The relationship between land use and transport is used to establish a link between land use and child pedestrian travel; trip attractors and generators are considered as variables that lead child pedestrians to exposure to high risk environments. Casualty records for Newcastle upon Tyne are analysed to reveal trends of temporal variation of child pedestrian casualty numbers. Land use data is combined with the casualty data using GIS techniques to generate relevant inputs for the analysis. Six Generalized Linear Models (GLMs) are developed to analyse the association of child pedestrian casualty numbers and trip attractor land use types. Two are the main models; the first investigates all types of casualty data including slight, serious and fatal events and the second uses only KSI (Killed or Seriously Injured) data in the analysis. The other four models are developed to investigate the temporal variation of child pedestrian KSI and slight casualties over the day (school time and non-school time) and week (weekday and weekend). The results show that secondary retail and high density residential land use types are associated with all child pedestrian casualties. In addition, educational sites, junction density, primary retail and low density residential land use types are also associated with child casualties at different time periods of the day and week. The study findings are found to concur with the current child road safety policies in Great Britain and will, in fact, provide some guidance for local authorities to deliver successful child road safety audits.     

Comprehensive analysis of vehicle-pedestrian crashes at intersections in Florida

This study analyzes vehicle-pedestrian crashes at intersections in Florida over 4 years, 1999-2002. The study identifies the group of drivers and pedestrians, and traffic and environmental characteristics that are correlated with high pedestrian crashes using log-linear models. The study also estimates the likelihood of pedestrian injury severity when pedestrians are involved in crashes using an ordered probit model. To better reflect pedestrian crash risk, a logical measure of exposure is developed using the information on individual walking trips in the household travel survey. Lastly, the impact of average traffic volume on pedestrian crashes is examined. As a result of the analysis, it was found that pedestrian and driver demographic factors, and road geometric, traffic and environment conditions are closely related to the frequency and injury severity of pedestrian crashes. Higher average traffic volume at intersections increases the number of pedestrian crashes; however, the rate of increase is steeper at lower values of average traffic volume. Based on the findings in the analysis, some countermeasures are recommended to improve pedestrian safety.     

Hierarchical Bayesian random intercept model-based cross-level interaction decomposition for truck driver injury severity investigations

Traffic crashes occurring on rural roadways induce more severe injuries and fatalities than those in urban areas, especially when there are trucks involved. Truck drivers are found to suffer higher potential of crash injuries compared with other occupational labors. Besides, unobserved heterogeneity in crash data analysis is a critical issue that needs to be carefully addressed. In this study, a hierarchical Bayesian random intercept model decomposing cross-level interaction effects as unobserved heterogeneity is developed to examine the posterior probabilities of truck driver injuries in rural truck-involved crashes. The interaction effects contributing to truck driver injury outcomes are investigated based on two-year rural truck-involved crashes in New Mexico from 2010 to 2011. The analysis results indicate that the cross-level interaction effects play an important role in predicting truck driver injury severities, and the proposed model produces comparable performance with the traditional random intercept model and the mixed logit model even after penalization by high model complexity. It is revealed that factors including road grade, number of vehicles involved in a crash, maximum vehicle damage in a crash, vehicle actions, driver age, seatbelt use, and driver under alcohol or drug influence, as well as a portion of their cross-level interaction effects with other variables are significantly associated with truck driver incapacitating injuries and fatalities. These findings are helpful to understand the respective or joint impacts of these attributes on truck driver injury patterns in rural truck-involved crashes.     

The relative contribution of system failures and extreme behaviour in South Australian crashes

Within the road system, there are compliant road users who may make an error that leads to a crash, resulting in a ‘system failure’, and there are also road users who deliberately take risks and display dangerous or ‘extreme’ behaviours that lead to a crash. Crashes resulting from system failures can be addressed through improvements to road system design more readily than crashes resulting from extreme behaviours. The classification of crash causation in terms of system failures or extreme behaviour is important for determining the extent to which a Safe System approach (i.e. improvements to road system design to serve compliant road users) is capable of reducing the number of crashes. This study examined the relative contribution of system failures and extreme behaviour in South Australian crashes as identified from information in Coroner’s investigation files and in-depth crash investigations conducted by the Centre for Automotive Safety Research (CASR). The analysis of 189 fatal crashes, 272 non-fatal metropolitan injury crashes and 181 non-fatal rural crashes indicated that very few non-fatal crashes (3% metropolitan, 9% rural) involved extreme behaviour by road users and, even in fatal crashes, the majority (54%) were the result of system failures. Fatal crashes resulting from system failures were more likely than those resulting from extreme behaviour to occur during the day, on weekdays, in rural areas and on roads with high speed limits. Findings from the current study suggest that improvements to the road transport system (i.e. forgiving road infrastructure, appropriate speed limits, and safe vehicle design) can be expected to be much more effective in reducing crashes than concentrating on preventing extreme behaviours. Such a strategy could reduce the incidence and severity of a large proportion of crashes in South Australia.     

Exploring factors affecting pedestrians’ red-light running behaviors at intersections in China

Pedestrians’ Red-light running behavior is one of the most critical factors for pedestrian involved traffic crashes at intersections in China. The primary objective of this study is to explore how various factors affect pedestrians’ red-light running behaviors at intersection areas, using the data collected from Hefei, China. A questionnaire was well designed aiming at collecting pedestrians’ socio-economic characteristics, trip related features, and attribute variables in different crossing facilities. Based on 631 valid samples, a binomial logistic model was established to evaluate the impacts of contributing factors on pedestrians’ red-light running behavior. The modeling results show that four variables significantly affect the probability of pedestrians’ red-light running behavior, which are the trip purpose, time period in one day, pedestrian’s attitude towards whether to run a red light when in hurry, and pedestrian’s attitude towards whether quality of road facility affects crossing behavior. With those variables, the probability of pedestrians’ red-light running behavior at intersections could be predicted. Findings of this study can help understand why pedestrians in China run red-lights and identify which pedestrian groups and intersections are more likely to have such behaviors. This study can also help propose countermeasures more efficiently to reduce pedestrian-related crashes at intersections in China.     

Investigating driver injury severity patterns in rollover crashes using support vector machine models

Rollover crash is one of the major types of traffic crashes that induce fatal injuries. It is important to investigate the factors that affect rollover crashes and their influence on driver injury severity outcomes. This study employs support vector machine (SVM) models to investigate driver injury severity patterns in rollover crashes based on two-year crash data gathered in New Mexico. The impacts of various explanatory variables are examined in terms of crash and environmental information, vehicle features, and driver demographics and behavior characteristics. A classification and regression tree (CART) model is utilized to identify significant variables and SVM models with polynomial and Gaussian radius basis function (RBF) kernels are used for model performance evaluation. It is shown that the SVM models produce reasonable prediction performance and the polynomial kernel outperforms the Gaussian RBF kernel. Variable impact analysis reveals that factors including comfortable driving environment conditions, driver alcohol or drug involvement, seatbelt use, number of travel lanes, driver demographic features, maximum vehicle damages in crashes, crash time, and crash location are significantly associated with driver incapacitating injuries and fatalities. These findings provide insights for better understanding rollover crash causes and the impacts of various explanatory factors on driver injury severity patterns.     

Explaining two-lane highway crash rates using land use and hourly exposure

This paper describes the estimation of Poisson regression models for predicting both single and multi-vehicle highway crash rates as a function of traffic density and land use, as well as ambient light conditions and time of day. The study focuses on seventeen rural, two-lane highway segments, each one-half mile in length with varying land use patterns and where actual hourly exposure values are available in the form of observed traffic counts. Land-use effects are represented by the number of driveways of various types on each segment. Hourly exposure is represented for single-vehicle crashes as the total vehicle miles traveled and volume/capacity ratio; for multi-vehicle crashes it is the product of the hourly volumes on the main highway and the roads intersecting it along the study segment. For single-vehicle crashes, the following variables were found to be significant, with a positive or negative effect as noted: daytime (06:00–19:00 h, negative effect), the natural log of the segment volume/capacity ratio (negative), percent of the segment with no passing zones (positive), shoulder width (positive), number of intersections (negative), and driveways (mixed effects by type). Good multi-vehicle crash prediction models had quite different variables: daylight conditions from 10:00–15:00 and 15:00–19:00 h (positive), number of intersections (negative), and driveways (positive for all types). The results show that traffic intensity explains differences in crash rates even when controlling for time of day and light conditions, and that these effects are quite different for single and multi-vehicle crashes. Suggestions for future research are also given.     

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.     

Exploring the effects of roadway characteristics on the frequency and severity of head-on crashes: Case studies from Malaysian Federal Roads

Head-on crashes are among the most severe collision types and of great concern to road safety authorities. Therefore, it justifies more efforts to reduce both the frequency and severity of this collision type. To this end, it is necessary to first identify factors associating with the crash occurrence. This can be done by developing crash prediction models that relate crash outcomes to a set of contributing factors. This study intends to identify the factors affecting both the frequency and severity of head-on crashes that occurred on 448 segments of five federal roads in Malaysia. Data on road characteristics and crash history were collected on the study segments during a 4-year period between 2007 and 2010. The frequency of head-on crashes were fitted by developing and comparing seven count-data models including Poisson, standard negative binomial (NB), random-effect negative binomial, hurdle Poisson, hurdle negative binomial, zero-inflated Poisson, and zero-inflated negative binomial models. To model crash severity, a random-effect generalized ordered probit model (REGOPM) was used given a head-on crash had occurred. With respect to the crash frequency, the random-effect negative binomial (RENB) model was found to outperform the other models according to goodness of fit measures. Based on the results of the model, the variables horizontal curvature, terrain type, heavy-vehicle traffic, and access points were found to be positively related to the frequency of head-on crashes, while posted speed limit and shoulder width decreased the crash frequency. With regard to the crash severity, the results of REGOPM showed that horizontal curvature, paved shoulder width, terrain type, and side friction were associated with more severe crashes, whereas land use, access points, and presence of median reduced the probability of severe crashes. Based on the results of this study, some potential countermeasures were proposed to minimize the risk of head-on crashes.     

Evaluation of Iowa's graduated driver's licensing program

The effectiveness of Iowa's graduated driver's licensing (GDL) program was evaluated for a 4-year period before and after implementation in 1999. Since some changes had occurred in the crash reporting format, changes in crash rates for younger drivers were compared to those for 35-44-year-old drivers (middle-age group of drivers) who were used as a control group. After implementation of GDL, the 14-, 16- and 17-year-old age groups experienced a greater decrease in crash rate than the middle-age control group while 15-year-old experienced a smaller decrease. This suggests that the crash rate for 15-year-old drivers may actually have increased when downward trends were adjusted for. Iowa's GDL program allows holders of the instruction permit to travel unaccompanied to and from school and school-endorsed activities after obtaining a minor school license. Fifteen-year-old with minor school licenses account for up to 26.7% of 15-year-old license holders yet represent up to 74.8% of 15-year-old drivers involved in crashes (depending on the year) from 1998 to 2004. As a result, 15-year-old drivers with minor school licenses are involved in 7.2-8.9 times more crashes, are 7.7 times more likely to have one or more sanctions, and are 4.8 times more likely to receive one or more moving convictions than their peers with a regular instruction permit. This help may explain why 15-year-old drivers did not seem to benefit from implementation of the GDL program in Iowa.     

Understanding crash mechanism on urban expressways using high-resolution traffic data

Urban expressways play a vital role in the modern mega cities by serving peak hour traffic alongside reducing travel time for moderate to long distance intra-city trips. Thus, ensuring safety on these roads holds high priority. Little knowledge has been acquired till date regarding crash mechanism on these roads. This study uses high-resolution traffic data collected from the detectors to identify factors influencing crash. It also identifies traffic patterns associated with different types of crashes and explains crash phenomena thereby. Unlike most of the previous studies on conventional expressways, the research separately investigates the basic freeway segments (BFS) and the ramp areas. The study employs random multinomial logit, a random forest of logit models, to rank the variables; expectation maximization clustering algorithm to identify crash prone traffic patterns and classification and regression trees to explain crash phenomena. As accentuated by the study outcome, crash mechanism is not generic throughout the expressway and it varies from the BFS to the ramp vicinities. The level of congestion and speed difference between upstream and downstream traffic best explains crashes and their types for the BFS, whereas, the ramp flow has the highest influence in determining the types of crashes within the ramp vicinities. The paper also discusses about the applicability of different countermeasures, such as, variable speed limits, temporary restriction on lane changing, posting warnings, etc., to attenuate different patterns of hazardous traffic conditions. The study outcome can be utilized in designing location and traffic condition specific proactive road safety management systems for urban expressways.     

A pattern analysis of traffic crashes fatal to older drivers

A synthesis of the various crash circumstances in which older drivers die is lacking. This study is based on data from Sweden's national archive of fatal RTCs, and focuses on crashes in which the deceased driver was aged 65+ (2002-2004; n = 152). Crash patterns were identified by means of cluster analysis using a sub-set of 12 variables describing both driver and crash event characteristics. Crashes where the driver had died of natural causes prior to crash made up 19.7% of the cases (30 crashes) and were mainly single crashes. Four additional clusters were also identified. Two involved making left turns at intersections, one over-represented among men, occurring typically at weekends, in low-speed areas (30.6%), and the second one, over-represented among women, consisting of crashes in dry road conditions, and on intermediate-speed roads (21.5%). A third cluster included head-on and single-vehicle crashes occurring in dry road conditions but on high-speed roads (29.8%). The last cluster consisted of crashes occurring during the winter and on high-speed roads (18.2%). Older drivers die in traffic in various circumstances, sometimes prior to crashing. Some circumstances cannot be easily alleviated but others could, e.g., through modifications of the road traffic environment and car active safety measures that can help compensate for age-related shortcomings.     

Does street network design affect traffic safety?

Negative binomial regression models were used to assess the effect of street and street network characteristics on total crashes, severe injury crashes, and fatal crashes. Data from over 230,000 crashes taking place over 11 years in 24 California cities was analyzed at the U.S. Census Block Group level of geography. In our analysis we controlled for variables such as vehicle volumes, income levels, and proximity to limited access highways and to the downtown area. Street network characteristics that were considered in the analysis included street network density and street connectivity along with street network pattern.Our findings suggest that for all levels of crash severity, street network characteristics correlate with road safety outcomes. Denser street networks with higher intersection counts per area are associated with fewer crashes across all severity levels. Conversely, increased street connectivity as well as additional travel lanes along the major streets correlated with more crashes. Our results suggest that in assessing safety, it is important to move beyond the traditional approach of just looking at the characteristics of the street itself and examine how the interrelated factors of street network characteristics, patterns, and individual street designs interact to affect crash frequency and severity.     

Head injuries to bicyclists and the New Zealand bicycle helmet law

The purpose of this study was to examine the effect of helmet wearing and the New Zealand helmet wearing law on serious head injury for cyclists involved in on-road motor vehicle and non-motor vehicle crashes. The study population consisted of three age groups of cyclists (primary school children (ages 5-12 years), secondary school children (ages 13-18 years), and adults (19+ years)) admitted to public hospitals between 1988 and 1996. Data were disaggregated by diagnosis and analysed using negative binomial regression models. Results indicated that there was a positive effect of helmet wearing upon head injury and this effect was relatively consistent across age groups and head injury (diagnosis) types. We conclude that the helmet law has been an effective road safety intervention that has lead to a 19% (90% CI: 14, 23%) reduction in head injury to cyclists over its first 3 years.     

Road crashes involving animals in Australia

Each year in Australia many thousands of collisions occur between motor vehicles and animals, resulting in considerable vehicle repair costs, injury to persons, and loss of animal life. This paper reviews animal-related road crashes in Australia and presents data from the in-depth Rural and Remote Road Safety Study in North Queensland for serious casualties (n = 33) resulting from direct impact with an animal or swerving to avoid an animal on public roads. These crash types accounted for 5.5% of all eligible on-road serious casualties in the study and, hence, are considered to be an important issue that requires particular attention within rural and remote areas. Kangaroos and wallabies were the predominant species involved in these crashes (44.8%). Consistent with international studies, night-time travel was found to be a significant risk factor when comparing animal-related crashes to other serious injury crashes in the study. There were also a significantly higher proportion of motorcyclists (51.7%) than other vehicle occupants involved in animal-related serious crashes compared to all other serious injury crashes. Data matching to official Government records found underreporting of animal-related crashes to be an issue of concern. These findings are discussed in terms of countermeasures suitable for the Australian context and the need for consistent crash reporting across jurisdictions.     

Social costs of road crashes: An international analysis

This paper provides an international overview of the most recent estimates of the social costs of road crashes: total costs, value per casualty and breakdown in cost components. The analysis is based on publications about the national costs of road crashes of 17 countries, of which ten high income countries (HICs) and seven low and middle income countries (LMICs). Costs are expressed as a proportion of the gross domestic product (GDP). Differences between countries are described and explained. These are partly a consequence of differences in the road safety level, but there are also methodological explanations. Countries may or may not correct for underreporting of road crashes, they may or may not use the internationally recommended willingness to pay (WTP)-method for estimating human costs, and there are methodological differences regarding the calculation of some other cost components.The analysis shows that the social costs of road crashes in HICs range from 0.5% to 6.0% of the GDP with an average of 2.7%. Excluding countries that do not use a WTP- method for estimating human costs and countries that do not correct for underreporting, results in average costs of 3.3% of GDP. For LMICs that do correct for underreporting the share in GDP ranges from 1.1% to 2.9%. However, none of the LMICs included has performed a WTP study of the human costs.A major part of the costs is related to injuries: an average share of 50% for both HICs and LMICs. The average share of fatalities in the costs is 23% and 30% respectively. Prevention of injuries is thus important to bring down the socio-economic burden of road crashes.The paper shows that there are methodological differences between countries regarding cost components that are taken into account and regarding the methods used to estimate specific cost components. In order to be able to make sound comparisons of the costs of road crashes across countries, (further) harmonization of cost studies is recommended. This can be achieved by updating and improving international guidelines and applying them in future cost studies. The information regarding some cost components, particularly human costs and property damage, is poor and more research into these cost components is recommended.