Spatial analysis of fatal and injury crashes in Pennsylvania

Using injury and fatal crash data for Pennsylvania for 1996-2000, full Bayes (FB) hierarchical models (with spatial and temporal effects and space-time interactions) are compared to traditional negative binomial (NB) estimates of annual county-level crash frequency. Covariates include socio-demographics, weather conditions, transportation infrastructure and amount of travel. FB hierarchical models are generally consistent with the NB estimates. Counties with a higher percentage of the population under poverty level, higher percentage of their population in age groups 0-14, 15-24, and over 64 and those with increased road mileage and road density have significantly increased crash risk. Total precipitation is significant and positive in the NB models, but not significant with FB. Spatial correlation, time trend, and space-time interactions are significant in the FB injury crash models. County-level FB models reveal the existence of spatial correlation in crash data and provide a mechanism to quantify, and reduce the effect of, this correlation. Addressing spatial correlation is likely to be even more important in road segment and intersection-level crash models, where spatial correlation is likely to be even more pronounced.     

Predicting motor vehicle crashes using Support Vector Machine models

Crash prediction models have been very popular in highway safety analyses. However, in highway safety research, the prediction of outcomes is seldom, if ever, the only research objective when estimating crash prediction models. Only very few existing methods can be used to efficiently predict motor vehicle crashes. Thus, there is a need to examine new methods for better predicting motor vehicle crashes. The objective of this study is to evaluate the application of Support Vector Machine (SVM) models for predicting motor vehicle crashes. SVM models, which are based on the statistical learning theory, are a new class of models that can be used for predicting values. To accomplish the objective of this study, Negative Binomial (NB) regression and SVM models were developed and compared using data collected on rural frontage roads in Texas. Several models were estimated using different sample sizes. The study shows that SVM models predict crash data more effectively and accurately than traditional NB models. In addition, SVM models do not over-fit the data and offer similar, if not better, performance than Back-Propagation Neural Network (BPNN) models documented in previous research. Given this characteristic and the fact that SVM models are faster to implement than BPNN models, it is suggested to use these models if the sole purpose of the study consists of predicting motor vehicle crashes.     

Effects of work zone presence on injury and non-injury crashes.

Work zones in the United States have approximately 700 traffic-related fatalities, 24,000 injury crashes, and 52,000 non-injury crashes every year. Due to future highway reconstruction needs, work zones are likely to increase in number, duration, and length. This study focuses on analyzing the effect of work zone duration mainly due to its policy-sensitivity. To do so, we created a unique dataset of California freeway work zones that included crash data (crash frequency and injury severity), road inventory data (average daily traffic (ADT) and urban/rural character), and work zone related data (duration, length, and location). Then, we investigated crash rates and crash frequencies in the pre-work zone and during-work zone periods. For the freeway work zones investigated in this study, the total crash rate in the during-work zone period was 21.5% higher (0.79 crashes per million vehicle kilometer (MVKM)) than the pre-work zone period (0.65 crashes per MVKM). Compared with the pre-work zone period, the increase in non-injury and injury crash rates in the during-work zone period was 23.8% and 17.3%, respectively. Next, crash frequencies were investigated using negative binomial models, which showed that frequencies increased with increasing work zone duration, length, and average daily traffic. The important finding is that after controlling for various factors, longer work zone duration significantly increases both injury and non-injury crash frequencies. The implications of the study findings are discussed in the paper.     

Tobit analysis of vehicle accident rates on interstate highways

There has been an abundance of research that has used Poisson models and its variants (negative binomial and zero-inflated models) to improve our understanding of the factors that affect accident frequencies on roadway segments. This study explores the application of an alternate method, tobit regression, by viewing vehicle accident rates directly (instead of frequencies) as a continuous variable that is left-censored at zero. Using data from vehicle accidents on Indiana interstates, the estimation results show that many factors relating to pavement condition, roadway geometrics and traffic characteristics significantly affect vehicle accident rates.     

Comparison of road crashes incidence and severity between some French counties

Our aim is to compare traffic safety among several counties in France, and explore whether observed differences can be explained by differences in road types distribution and by differences in socio-economic characteristics between counties. Traffic safety is measured by incidence and severity, where incidence is defined by the ratio of counts of injury accidents and exposure, measured by the amount of kilometres driven. Severity is measured by the ratio between fatal and injury accidents. These indexes are analysed in the framework of Generalised Linear Models: counts of injury accidents are analysed with a Negative Binomial regression, which accounts for over-dispersion. Severity being the proportion of fatal accidents among injury accidents corresponds to the probability of a Binomial setting and this is modelled by a logistic regression.This modelling provides an easy way to adjust for covariates such as road type, environment (urban/rural) and evolution over time, and to test their possible interactions. We find that the time trend of each indice (incidence and severity) is the same across counties and across road types. There is a significant interaction between county and road type, meaning that, first, differences in traffic safety between counties are not fully explained by different road type distributions, and second, that the "ranking" of counties in term of incidence or severity varies according to the road type considered, and vice-versa. It was planned to explore global characteristics of the counties (driving and socio-economic data) as possible explanatory factors of differences between counties, but the existence of an interaction of county with road types shows the necessity of collecting and exploring characteristics of the sub-levels of road type within county.     

Temporal and spatial analyses of rear-end crashes at signalized intersections

In this study, the generalized estimating equations with the negative binomial link function were used to model rear-end crash frequencies at signalized intersections to account for the temporal or spatial correlation among the data. The longitudinal data for 208 signalized intersections over 3 years and the spatially correlated data for 476 signalized intersections which are located along different corridors were collected in the state of Florida. The modeling results showed that there are high correlations between the longitudinal or spatially correlated rear-end crashes. Some intersection related variables are identified as significantly influencing rear-end crash occurrences at signalized intersections. Intersections with heavy traffic on the major and minor roadways, having more right and left-turn lanes on the major roadway, having a large number of phases per cycle (indicated by the left-turn protection on the minor roadway), with high speed limits on the major roadway, and in high population areas are correlated with high rear-end crash frequencies. On the other hand, intersections with three legs, having channelized or exclusive right-turn lanes on the minor roadway, with protected left-turning on the major roadway, with medians on the minor roadway, and having longer signal spacing have a lower frequency of rear-end crashes.     

Application of the Conway-Maxwell-Poisson generalized linear model for analyzing motor vehicle crashes

This paper documents the application of the Conway–Maxwell–Poisson (COM-Poisson) generalized linear model (GLM) for modeling motor vehicle crashes. The COM-Poisson distribution, originally developed in 1962, has recently been re-introduced by statisticians for analyzing count data subjected to over- and under-dispersion. This innovative distribution is an extension of the Poisson distribution. The objectives of this study were to evaluate the application of the COM-Poisson GLM for analyzing motor vehicle crashes and compare the results with the traditional negative binomial (NB) model. The comparison analysis was carried out using the most common functional forms employed by transportation safety analysts, which link crashes to the entering flows at intersections or on segments. To accomplish the objectives of the study, several NB and COM-Poisson GLMs were developed and compared using two datasets. The first dataset contained crash data collected at signalized four-legged intersections in Toronto, Ont. The second dataset included data collected for rural four-lane divided and undivided highways in Texas. Several methods were used to assess the statistical fit and predictive performance of the models. The results of this study show that COM-Poisson GLMs perform as well as NB models in terms of GOF statistics and predictive performance. Given the fact the COM-Poisson distribution can also handle under-dispersed data (while the NB distribution cannot or has difficulties converging), which have sometimes been observed in crash databases, the COM-Poisson GLM offers a better alternative over the NB model for modeling motor vehicle crashes, especially given the important limitations recently documented in the safety literature about the latter type of model.     

Modeling work zone crash frequency by quantifying measurement errors in work zone length

Work zones are temporary traffic control zones that can potentially cause safety problems. Maintaining safety, while implementing necessary changes on roadways, is an important challenge traffic engineers and researchers have to confront. In this study, the risk factors in work zone safety evaluation were identified through the estimation of a crash frequency (CF) model. Measurement errors in explanatory variables of a CF model can lead to unreliable estimates of certain parameters. Among these, work zone length raises a major concern in this analysis because it may change as the construction schedule progresses generally without being properly documented. This paper proposes an improved modeling and estimation approach that involves the use of a measurement error (ME) model integrated with the traditional negative binomial (NB) model. The proposed approach was compared with the traditional NB approach. Both models were estimated using a large dataset that consists of 60 work zones in New Jersey. Results showed that the proposed improved approach outperformed the traditional approach in terms of goodness-of-fit statistics. Moreover it is shown that the use of the traditional NB approach in this context can lead to the overestimation of the effect of work zone length on the crash occurrence.     

A note on modeling road accident frequency: a flexible elasticity model

Count data models and their variants have been widely applied in accident modeling. The traditional log-linear function is used to represent the relationship between explanatory variables and the dependent variable (accident frequency). However, this function assumes constant elasticity for the estimation parameters, which is a limitation in the analysis of the effects of explanatory variables on accident risk. Although interaction effects between explanatory variables have been studied in the road safety context (where they are normally assessed by logistic regression), no one has yet examined the possibility of using a flexible function form allowing non-constant elasticity values. This paper seeks to explore the use of the translog function usually used in the economics context to allow the elasticity to vary with the values of other explanatory variables. Therefore, the objective of this study was to evaluate the application of the translog function to accident modeling and to compare the results with those of the traditional log-linear function negative binomial (NB) model. The results show that, in terms of goodness-of-fit statistics and residual analysis, the NB model with the translog function performs better than the traditional NB model. Additional evaluations in terms of predictive performance, hotspot identification and uncertainty associated with the estimated values were taken into account. Although this study is exploratory in nature, it suggests that the translog function has considerable potential for modeling accident observations. It is hoped that this novel accident modeling methodology will open the door to the reliable interpretation and evaluation of the influence of explanatory variables on accident frequency.     

The effect of infrastructure and demographic change on traffic-related fatalities and crashes: a case study of Illinois county-level data

This paper presents analyses of data from the Highway Safety Information System (HSIS) for the State of Illinois. Our analyses focuses on whether various changes in road network infrastructure and geometric design can be associated with changes in road fatalities and reported accidents. We also evaluate models that control for demographic changes. County-level time-series data is used and fixed effect negative binomial models are estimated. Results cannot confirm the hypothesis that changes in road infrastructure and geometric design have been beneficial for safety. Increases in the number of lanes appears to be associated with both increased traffic-related accidents and fatalities. Increased lane widths appears to be associated with increased fatalities. Increases in outside shoulder width appear to be associated with a decrease in accidents. Inclusion of demographic results does not significantly change these results but does capture much of the residual time trend in the models. Potentially mis-leading results are found when the time trend is not included. In this case a negative association between vertical curvature and both accidents and fatalities. No statistical association with changes in safety is found for median widths, inside shoulder widths, and horizontal and vertical curvature.     

Effect of repeal of the national maximum speed limit law on occurrence of crashes, injury crashes, and fatal crashes on Utah highways

Speed limits were increased in Utah and other States after repeal of the national maximum speed limit law (NMSL) in 1995. This study analyzed effects of the increased speed limit on Utah highways on crash rates, fatality crash rates, and injury crash rates. Annual (1992-1999) rates of crashes, fatality crashes, and injury crashes for the following highway categories were calculated: urban Interstate segments (current speed limit 60-65 miles per hour (mph)); rural Interstate segments (current speed limit 70-75 mph); 55 mph rural non-Interstate highway segments; and high-speed non-Interstate highways (current speed limit 60-65 mph). Data were analyzed using autoregressive integrative moving average intervention time series analysis techniques. There were significant increases in total crash rates on urban (60-65 mph) Interstate segments (confounded by extensive ongoing highway construction on these highways), and in fatal crash rates on high-speed (60-65 mph) rural non-Interstate segments. The following variables were unaffected: total, fatality, and injury crash rates on rural Interstate segments; fatality and injury crash rates on urban Interstate segments; total and injury crash rates on high-speed non-Interstate segments. These results show an adverse effect on crash occurrence for subsets of crash types and highways, but do not show a major overall effect of NMSL repeal and increased speed limit on crash occurrence on Utah highways.     

Poisson, Poisson-gamma and zero-inflated regression models of motor vehicle crashes: balancing statistical fit and theory

There has been considerable research conducted over the last 20 years focused on predicting motor vehicle crashes on transportation facilities. The range of statistical models commonly applied includes binomial, Poisson, Poisson-gamma (or negative binomial), zero-inflated Poisson and negative binomial models (ZIP and ZINB), and multinomial probability models. Given the range of possible modeling approaches and the host of assumptions with each modeling approach, making an intelligent choice for modeling motor vehicle crash data is difficult. There is little discussion in the literature comparing different statistical modeling approaches, identifying which statistical models are most appropriate for modeling crash data, and providing a strong justification from basic crash principles. In the recent literature, it has been suggested that the motor vehicle crash process can successfully be modeled by assuming a dual-state data-generating process, which implies that entities (e.g., intersections, road segments, pedestrian crossings, etc.) exist in one of two states-perfectly safe and unsafe. As a result, the ZIP and ZINB are two models that have been applied to account for the preponderance of "excess" zeros frequently observed in crash count data. The objective of this study is to provide defensible guidance on how to appropriate model crash data. We first examine the motor vehicle crash process using theoretical principles and a basic understanding of the crash process. It is shown that the fundamental crash process follows a Bernoulli trial with unequal probability of independent events, also known as Poisson trials. We examine the evolution of statistical models as they apply to the motor vehicle crash process, and indicate how well they statistically approximate the crash process. We also present the theory behind dual-state process count models, and note why they have become popular for modeling crash data. A simulation experiment is then conducted to demonstrate how crash data give rise to "excess" zeros frequently observed in crash data. It is shown that the Poisson and other mixed probabilistic structures are approximations assumed for modeling the motor vehicle crash process. Furthermore, it is demonstrated that under certain (fairly common) circumstances excess zeros are observed-and that these circumstances arise from low exposure and/or inappropriate selection of time/space scales and not an underlying dual state process. In conclusion, carefully selecting the time/space scales for analysis, including an improved set of explanatory variables and/or unobserved heterogeneity effects in count regression models, or applying small-area statistical methods (observations with low exposure) represent the most defensible modeling approaches for datasets with a preponderance of zeros.     

Segment characteristics and severity of head-on crashes on two-lane rural highways in Maine

More than two out of three of all fatal crashes in Maine occur on rural, two-lane collector or arterial roads. Head-on crashes on these roads account for less than 5% of the crashes, but they are responsible for almost half of all fatalities. Data analyzed in this study was provided by Maine Department of Transportation and covers all head-on crashes for 2000-2002 during which period there were 3,136 head-on crashes reported. Out of these, 127 were fatal crashes and 235 produced incapacitating but not fatal injuries. These two categories made up over 75% of the crash cost. A clear majority of head-on crashes on two-lane, rural roads in Maine were caused by drivers making errors or misjudging situations. Illegal/unsafe speed was a factor in 32% of the crashes while driver inattention/distraction was a primary factor in 28%. Fatigue was responsible for around one in 40 crashes and one in 12 fatal crashes. Alcohol or drugs was a factor in one in 12 crashes and one in nine fatal head-on crashes. Less than 8% of fatalities involved someone overtaking another vehicle, and only around 14% involved a driver intentionally crossing the centerline. Two in three fatal head-on crashes occurred on straight segments and 67% of these happened on dry pavement. There is a clear trend towards higher speed limits leading to a higher percentage of crashes becoming fatal or having incapacitating injuries. There is also a clear trend - if one keeps speeds constant and AADT within a certain range - that wider shoulders give higher crash severities. Also, for higher-speed roads, more travel lanes (than two) increase crash severity. In summary, there seems to be two major reasons why people get across the centerline and have head-on collisions: (a) people are going too fast for the roadway conditions; or (b) people are inattentive and get across the centerline more or less without noticing it. The latter category of crashes could probably be reduced if centerline rumble-strips were installed. More or less all head-on collisions could be eliminated if median barriers were installed. In-vehicle technology could also be used to significantly reduce the incidence of lane departures. Furthermore, today's speed limits should be better enforced since a high percentage of serious crashes involve illegal speeding. This should be combined with lowered speed limits for targeted high-crash segments.     

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.     

Analyzing the influence of median cross-section design on highway safety using vehicle dynamics simulations

Although vehicle dynamics simulations have long been used in vehicle design and crash reconstruction, their use for highway design is rare. This paper investigates the safety of highway medians through iterative simulations of off-road median encroachments. The commercially available software CarSim was used to simulate over one hundred thousand encroachments, representing the entire passenger vehicle fleet and a wide range of encroachment angles, departure speeds, steering inputs, and braking inputs. Each individual simulation output was then weighted using data from previous studies to reflect the probability of each specific accident scenario occurring in a real-life median encroachment. Results of this analysis illustrate the relative influence of median cross-section geometry on the resulting accident outcomes. The simulations indicate that the overall safety of a highway median depends on the occurrence of both vehicle rollover and median crossover events, and the cross-section shape, slope, and width are all shown to greatly affect each of these incidents. An evaluation of the simulation results was conducted with vehicle trajectories from previous experimental crash tests. Further assessment of the aggregate simulation results to actual crash data was achieved through comparison with several databases of crash statistics. Both efforts showed a strong agreement between the simulations and the real-life crash data.     

Analysis of injury severity of drivers involved in single- and two-vehicle crashes on highways in Ontario

This study analyzes driver's injury severity in single- and two-vehicle crashes and compares the effects of explanatory variables among various types of crashes. The study identified factors affecting injury severity and their effects on severity levels using 5-year crash records for provincial highways in Ontario, Canada. Considering heteroscedasticity in the effects of explanatory variables on injury severity, the heteroscedastic ordered logit (HOL) models were developed for single- and two-vehicle crashes separately. The results of the models show that there exists heteroscedasticity for young drivers (≤30), safety equipment and ejection in the single-vehicle crash model, and female drivers, safety equipment and head-on collision in the two-vehicle crash models. The results also show that young car drivers have opposite effects between single-car and car–car crashes, and sideswipe crashes have opposite effects between car–car and truck–truck crashes. The study demonstrates that separate HOL models for single-vehicle and different types of two-vehicle crashes can identify differential effects of factors on driver's injury severity.     

Effects on accidents of periodic motor vehicle inspection in Norway

An extensive programme of periodic motor vehicle inspection was introduced in Norway after 1995, when the treaty between Norway and the European Union (EU) granting Norway (not a member of the EU) access to the EU inner market took effect (The EEA treaty). This paper evaluates the effects on accidents of periodic inspections of cars. Trucks and buses were not included in the study. Negative binomial regression models were fitted to data on accidents and inspections created by merging data files provided by a major insurance company and by the Public Roads Administration. Technical defects prior to inspection were associated with an increased accident rate. Inspections were found to strongly reduce the number of technical defects in cars. Despite this, no effect of inspections on accident rate were found. This finding is inconsistent with the fact that technical defects appear to increase the accident rate; one would expect the repair of such defects to reduce the accident rate. Potential explanations of the findings in terms of behavioural adaptation among car owners are discussed. It is suggested that car owners adapt driving behaviour to the technical condition of the car and that the effect attributed to technical defects before inspection may in part be the result of a tendency for owners who are less concerned about safety to neglect the technical condition of their cars. These car owners might have had a higher accident rate than other car owners irrespective of the technical condition of the car.     

Factors influencing safety in a sample of marked pedestrian crossings selected for safety inspections in the city of Oslo

This paper reports an analysis of factors influencing safety in a sample of marked pedestrian crossings in the city of Oslo, Norway. The sample consists of 159 marked pedestrian crossings where a total of 316 accidents were recorded during a period of five years. The crossings were selected for inspection because of they were, for various reasons, regarded as sub-standard. The sample of crossings is therefore not representative of all pedestrian crossings in Oslo. Factors influencing the number of accidents were studied by means of negative binomial regression. Factors that were studied included the volume of pedestrians and vehicles, the number of traffic lanes at the crossing, the location of the crossing (midblock or junction), the type of traffic control, the share of pedestrians using the crossing and the speed of approaching vehicles. The analysis confirmed the presence of a “safety-in-numbers” effect, meaning that an increase in the number of pedestrians is associated with a lower risk of accident for each pedestrian. Crossings located in four-leg junctions or roundabouts had more accidents than crossings located in three-leg junctions or on sections between junctions. A high share of pedestrians crossing the road outside the marked crossing was associated with a high number of accidents. Increased speed was associated with an increased number of accidents.     

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.