Traffic environment and demographic factors affecting impaired driving and crashes

IntroductionData availability has forced researchers to examine separately the role of alcohol among drivers who crashed and drivers who did not crash. Such a separation fails to account fully for the transition from impaired driving to an alcohol-related crash.MethodIn this study, we analyzed recent data to investigate how traffic-related environments, conditions, and drivers’ demographics shape the likelihood of a driver being either involved in a crash (alcohol impaired or not) or not involved in a crash (alcohol impaired or not). Our data, from a recent case–control study, included a comprehensive sampling of the drivers in nonfatal crashes and a matched set of comparison drivers in two U.S. locations. Multinomial logistic regression was applied to investigate the likelihood that a driver would crash or would not crash, either with a blood alcohol concentration (BAC) = .00 or with a BAC ≥ .05.ConclusionsTo our knowledge, this study is the first to examine how different driver characteristics and environmental factors simultaneously contribute to alcohol use by crash-involved and non-crash-involved drivers. This effort calls attention to the need for research on the simultaneous roles played by all the factors that may contribute to motor vehicle crashes.     

Safety analysis of urban signalized intersections under mixed traffic

IntroductionThis study examined the crash causative factors of signalized intersections under mixed traffic using advanced statistical models.MethodHierarchical Poisson regression and logistic regression models were developed to predict the crash frequency and severity of signalized intersection approaches. The prediction models helped to develop general safety countermeasures for signalized intersections.ResultsThe study shows that exclusive left turn lanes and countdown timers are beneficial for improving the safety of signalized intersections. Safety is also influenced by the presence of a surveillance camera, green time, median width, traffic volume, and proportion of two wheelers in the traffic stream. The factors that influence the severity of crashes were also identified in this study.Practical applicationAs a practical application, the safe values of deviation of green time provided from design green time, with varying traffic volume, is presented in this study. This is a useful tool for setting the appropriate green time for a signalized intersection approach with variations in the traffic volume.     

Safety effects of automated section speed control on the Italian motorway network

Introduction: Automated Section Speed Control (ASSC) has been identified as an effective countermeasure to reduce speeds and improve speed limit compliance. Method: An Empirical Bayes (EB) before-and-after study was performed in this research in order to evaluate the impact of the ASSC system on the expected crash frequency. The study was carried out on a sample of 125 ASSC sites of the Italian motorway network covering 1252 km, where a total of 21,721 crashes were recorded during a 10-year analysis period from 2004 to 2013. Results: Overall, the EB analysis estimated a significant 22% reduction in the expected crash frequency due to the implementation of the ASSC system. The analysis indicated that the effect is slightly larger on property damage only (PDO) crashes (− 23%) than on fatal injury (FI) crashes (− 18%) and that the highest reductions in crash frequency are expected for multi-vehicle FI crashes (− 25%) and multi-vehicle PDO crashes (− 31%). Furthermore, the results indicated that the ASSC system is more effective in reducing crash rates when traffic volume increases and it is therefore strongly recommended as a countermeasure to improve safety on high-traffic-volume motorway sections.     

A combined frequency–severity approach for the analysis of rear-end crashes on urban arterials

Analysis of both the crash count and the severity of injury are required to provide the complete picture of the safety situation of any given roadway. The randomness of crashes, the one-way dependency of injury on crash occurrence and the difference in response types have typically led researchers into developing independent statistical models for crash count and severity classification. The Genetic Programming (GP) methodology adopts the concepts of evolutionary biology such as crossover and mutation in effectively giving a common heuristic approach to model the development for the two different modeling objectives. The chosen GP models have the highest hit rate for rear-end crash classification problem and the least error for function fitting (regression) problems. Higher Average Daily Traffic (ADT) is more likely to result in more crashes. Absence of on-street parking may result in diminished severity of injuries resulting from crashes as they may provide “soft” crash barrier in contrast to fixed road side objects. Graphical presentation of the frequency of crashes with varying input variables shed new light on the results and its interpretation. Higher friction coefficient of roadways result in reduced frequency of crashes during the morning peak hours, with the trend being reversed during the afternoon peak hours. Crash counts have been observed to be at a maximum at a surface width of 30 ft. Sensitivity analysis results reflect that ADT is responsible for the largest variation in crash counts on urban arterials.     

Temporal factors in motor-vehicle crash deaths: Ten years later

ObjectiveTo assess trends in traffic fatalities on several temporal scales: year to year, by month, by day of week, and by time of day, to determine why some times correspond with higher rates of crash deaths, and to assess how these trends relate to age, the role of the deceased, and alcohol consumption.MethodTraffic fatalities were identified using the Fatality Analysis Reporting System (FARS) for 1998 through 2014 and assessed for their time of occurrence. Three days that, on average, contained particularly high numbers of crash deaths were then assessed in greater detail, considering the age of the deceased, role of the deceased (vehicle occupant, bicyclist, motorcyclist, or pedestrian), and the blood alcohol content of either the driver (for passenger vehicle occupants) or the deceased.ResultsAnnual crash fatality totals were much lower in 2014 than in 1998, but the decrease was not steady; a marked drop in crash deaths occurred after 2007 and continued until 2014. On average the most fatalities per day occurred in July and August (116 per day), followed closely by June, September, and October. During the week, the greatest number of fatalities on average occur on weekend days, and during the day the most fatalities tend to occur between the hours of 3 p.m. and 7 p.m. Holidays like Independence Day and New Year's Day show elevated crash fatalities, and a greater percentage of these crashes involved alcohol, when compared with adjacent days.ConclusionCertain days and times of year stand out as posing an elevated crash risk, and even with the decrease in average daily fatalities over the past decade, these days and times of year have remained consistent.Practical applicationThese results indicate focused areas for continued efforts to reduce fatal crashes.     

Predicting reduced visibility related crashes on freeways using real-time traffic flow data

ObjectivesThe main objective of this paper is to investigate whether real-time traffic flow data, collected from loop detectors and radar sensors on freeways, can be used to predict crashes occurring at reduced visibility conditions. In addition, it examines the difference between significant factors associated with reduced visibility related crashes to those factors correlated with crashes occurring at clear visibility conditions.MethodRandom Forests and matched case-control logistic regression models were estimated.ResultsThe findings indicated that real-time traffic variables can be used to predict visibility related crashes on freeways. The results showed that about 69% of reduced visibility related crashes were correctly identified. The results also indicated that traffic flow variables leading to visibility related crashes are slightly different from those variables leading to clear visibility crashes.Impact on IndustryUsing time slices 5–15 minutes before crashes might provide an opportunity for the appropriate traffic management centers for a proactive intervention to reduce crash risk in real-time.     

Efficacy of roll stability control and lane departure warning systems using carrier-collected data

IntroductionLarge truck crashes have significantly declined over the last 10 years, likely due, in part, to the increased use of onboard safety systems (OSS). Unfortunately, historically there is a paucity of data on the real-world efficacy of these devices in large trucks. The purpose of this study was to evaluate the two OSSs, lane departure warning (LDW) and roll stability control (RSC), using data collected from motor carriers.MethodA retrospective cohort approach was used to assess the safety benefits of these OSSs installed on Class 7 and 8 trucks as they operated during normal revenue-producing deliveries. Data were collected from 14 carriers representing small, medium, and large carriers hauling a variety of commodities. The data consisted of a total of 88,112 crash records and 151,624 truck-years that traveled 13 billion miles over the observation period.ResultsThe non-LDW cohort had an LDW-related crash rate that was 1.917 times higher than the LDW cohort (p = 0.001), and the non-RSC cohort had an RSC-related crash rate that was 1.555 times higher than the RSC cohort (p < 0.001).ConclusionsThe results across analyses indicated a strong, positive safety benefit for LDW and RSC under real-world conditions.Practical applicationsThe results support the use of LDW and RSC in reducing the crash types associated with each OSS.     

Tracking progress in teenage driver crash risk in the United States since the advent of graduated driver licensing programs

IntroductionThis study examined U.S. teenagers' crash rates since 1996, when the first graduated driver licensing (GDL) program in the United State was implemented.MethodsPassenger vehicle driver crash involvement rates for 16–19 and 30–59 (middle-aged) year-olds were examined, using data from the Fatality Analysis Reporting System, National Automotive Sampling System General Estimates System, Census Bureau, and National Household Travel Surveys.ResultsPer capita fatal and police-reported crash rates in 2012 were lower for 16 year-olds than for middle-aged drivers but older teenagers' rates were higher. Mileage-based fatal and police-reported crash rates in 2008 were higher for teenagers than for middle-aged drivers and higher for 16–17 year-olds than for older teenagers. In 1996–2012, teenagers' per capita fatal and police-reported crash rates declined sharply, especially for 16–17 year-olds, and more so than for middle-aged drivers. Substantial declines also occurred in teenagers' mileage-based fatal and police-reported crash rates from 1995–96 to 2008, generally more so than for middle-aged drivers. Regarding factors in fatal crashes in 1996 and 2012, proportions of young teenagers' crashes occurring at night and with multiple teenage passengers declined, more so than among older teenagers and middle-aged drivers. The proportion of fatally injured drivers who had been drinking declined for teenagers but changed little for middle-aged drivers. Improvements were not apparent in rates of driver errors or speeding among teenage drivers in fatal crashes.ConclusionsTeenage drivers' crash risk dropped during the period of implementation of GDL laws, especially fatal crash types targeted by GDL. However, teenagers' crash risk remains high, and important crash factors remain unaddressed by GDL.Practical applicationsAlthough this study was not designed to examine the role of GDL, the results are consistent with the increased presence of such laws. More gains are achievable if states strengthen their laws.     

A validation of the low mileage bias using naturalistic driving study data

IntroductionThis paper evaluated the low mileage bias (LMB) phenomenon for senior drivers using data mined from the Second Strategic Highway Research Program (SHRP 2) Naturalistic Driving Study. Supporters of the LMB construct postulate that it is only those seniors who drive the lowest annual mileage who are primarily responsible for the increased crash rates traditionally attributed to this population in general.MethodThe current analysis included 802 participants, all aged 65 or older who were involved in 163 property damage and injury crashes, and deemed to be at-fault in 123 (75%) of those instances. Poisson regression models were used to evaluate the association between annualized mileage driven and crash risk.ResultsResults show that the crash rate for drivers with lower annualized mileage (i.e., especially for those driving fewer than approximately 3000 miles per year) was significantly higher than that of drivers with higher annualized mileage, and that 25% of the overall sample were low- mileage drivers according to this criterion. Data were also evaluated by gender and meta-age group (i.e., younger-old: 65–74 and older-old: 75–99), and the results were consistent across these sub-groups.ConclusionsThis study provides strong support for the existence of the LMB.Practical applicationsThese results can help to reshape how transportation safety stakeholders view senior drivers in general and help them to focus their efforts on those seniors most in need of either risk-reducing countermeasures or alternative means of transportation.     

Examining teen driver crashes and the prevalence of distraction: Recent trends, 2007–2015

IntroductionTeen drivers crash at a much higher rate than adult drivers, with distractions found as a factor in nearly 6 out of 10 moderate-to-severe teen crashes. As the driving environment continues to rapidly evolve, it is important to examine the effect these changes may be having on our youngest and most vulnerable drivers.MethodThe purpose of this study was to identify types of vehicle crashes teens are most frequently involved in, as well as the distracting activities being engaged in leading up to these crashes, with a focus on identifying changes or trends over time. We examined 2,229 naturalistic driving videos involving drivers ages 16–19. These videos captured crashes occurring between 2007 and 2015. The data of interest for this study included crash type, behaviors drivers engaged in leading up to the collision, total duration of time the driver's eyes were off the forward roadway, and duration of the longest glance away from forward.ResultsRear-end crashes increased significantly (annual % change = 3.23 [2.40–4.05]), corresponding with national data trends. Among cell phone related crashes, a significant shift occurred, from talking/listening to operating/looking (annual % change = 4.22 [1.15–7.29]). Among rear-end crashes, there was an increase in the time drivers' eyes were off the road (β = 0.1527, P = 0.0004) and durations of longest glances away (β = 0.1020, P = 0.0014).ConclusionsFindings suggest that shifts in the way cell phones are being used, from talking/listening to operating/looking, may be a cause of the increasing number of rear-end crashes for teen drivers.Practical applicationsUnderstanding the role that cell phone use plays in teen driver crashes is extremely important. Knowing how and when teens are engaging in this behavior is the only way effective technologies can be developed for mitigating these crashes.     

Comparative analysis of zonal systems for macro-level crash modeling

IntroductionMacro-level traffic safety analysis has been undertaken at different spatial configurations. However, clear guidelines for the appropriate zonal system selection for safety analysis are unavailable. In this study, a comparative analysis was conducted to determine the optimal zonal system for macroscopic crash modeling considering census tracts (CTs), state-wide traffic analysis zones (STAZs), and a newly developed traffic-related zone system labeled traffic analysis districts (TADs).MethodPoisson lognormal models for three crash types (i.e., total, severe, and non-motorized mode crashes) are developed based on the three zonal systems without and with consideration of spatial autocorrelation. The study proposes a method to compare the modeling performance of the three types of geographic units at different spatial configurations through a grid based framework. Specifically, the study region is partitioned to grids of various sizes and the model prediction accuracy of the various macro models is considered within these grids of various sizes.ResultsThese model comparison results for all crash types indicated that the models based on TADs consistently offer a better performance compared to the others. Besides, the models considering spatial autocorrelation outperform the ones that do not consider it.ConclusionsBased on the modeling results and motivation for developing the different zonal systems, it is recommended using CTs for socio-demographic data collection, employing TAZs for transportation demand forecasting, and adopting TADs for transportation safety planning.Practical ApplicationsThe findings from this study can help practitioners select appropriate zonal systems for traffic crash modeling, which leads to develop more efficient policies to enhance transportation safety.     

Older drivers’ involvement in fatal RTCs. Do crashes fatal to them differ from crashes involving them but fatal to others?

In several countries, older drivers are disproportionately involved in fatal road traffic crashes (RTCs) for various reasons. This study maps the circumstances of occurrence of crashes involving older drivers that are fatal to either them or other road users and highlights differences between them. Sweden’s national in-depth studies of fatal RTCs archive was used and focus was placed on crashes in which a driver aged 65 years or older was involved between 2002 and 2004 (n = 197). Thirteen driver and crash characteristics were analyzed simultaneously and typical crash patterns (classes) were highlighted. For each pattern, the proportions of crashes fatal to the older driver vs. to someone else were compared. Four patterns were identified: (1) crashes on low-speed stretches, involving left turn and intersections; (2) crashes involving very old drivers and older vehicles, (3) rear-end collisions on high-speed stretches; and (4) head-on and single-vehicle crashes in rural areas. Older drivers dying in the crash were over-represented in classes 2 and 4. The study shows that when older drivers are involved in fatal RTCs, they are often the ones who die (60%). Typical circumstances surrounding their involvement include manoeuvring difficulties, fast-moving traffic, and colliding in an old vehicle. Preventing fatal RTCs involving older drivers requires not only age-specific but also general measures.     

Can post encroachment time substitute intersection characteristics in crash prediction models?

IntroductionTransportation safety analyses have traditionally relied on crash data. The limitations of these crash data in terms of timeliness and efficiency are well understood and many studies have explored the feasibility of using alternative surrogate measures for evaluation of road safety. Surrogate safety measures have the potential to estimate crash frequency, while requiring reduced data collection efforts relative to crash data based measures. Traditional crash prediction models use factors such as traffic volume, sight distance, and grade to make risk and exposure estimates that are combined with observed crashes, generally using an Empirical Bayes method, to obtain a final crash estimate. Many surrogate measures have the notable advantage of not directly requiring historical crash data from a site to estimate safety. Post Encroachment Time (PET) is one such measure and represents the time difference between a vehicle leaving the area of encroachment and a conflicting vehicle entering the same area. The exact relationship between surrogate measures, such as PET, and crashes in an ongoing research area.MethodThis paper studies the use of PET to estimate crashes between left-turning vehicles and opposing through vehicles for its ability to predict opposing left-turn crashes. By definition, a PET value of 0 implies the occurrence of a crash and the closer the value of PET is to 0, the higher the conflict risk.ResultsThis study shows that a model combining PET and traffic volume characteristic (AADT or conflicting volume) has better predictive power than PET alone. Further, it was found that PET may be capturing the impact of certain other intersection characteristics on safety as inclusion of other intersection characteristics such as sight distance, grade, and other parameters result in only marginal impacts on predictive capacity that do not justify the increased model complexity.     

Assessing the risk of secondary crashes on highways

IntroductionThe occurrence of “secondary crashes” is one of the critical yet understudied highway safety issues. Induced by the primary crashes, the occurrence of secondary crashes does not only increase traffic delays but also the risk of inducing additional incidents. Many highway agencies are highly interested in the implementation of safety countermeasures to reduce this type of crashes. However, due to the limited understanding of the key contributing factors, they face a great challenge for determining the most appropriate countermeasures.MethodTo bridge this gap, this study makes important contributions to the existing literature of secondary incidents by developing a novel methodology to assess the risk of having secondary crashes on highways. The proposed methodology consists of two major components, namely: (a) accurate identification of secondary crashes and (b) statistically robust assessment of causal effects of contributing factors. The first component is concerned with the development of an improved identification approach for secondary accidents that relies on the rich traffic information obtained from traffic sensors. The second component of the proposed methodology is aimed at understanding the key mechanisms that are hypothesized to cause secondary crashes through the use of a modified logistic regression model that can efficiently deal with relatively rare events such as secondary incidents. The feasibility and improved performance of using the proposed methodology are tested using real-world crash and traffic flow data.ResultsThe risk of inducing secondary crashes after the occurrence of individual primary crashes under different circumstances is studied by employing the estimated regression model. Marginal effect of each factor on the risk of secondary crashes is also quantified and important contributing factors are highlighted and discussed.Practical applicationsMassive sensor data can be used to support the identification of secondary crashes. The occurrence mechanism of these secondary crashes can be investigate by the proposed model. Understanding the mechanism helps deploy appropriate countermeasures to mitigate or prevent the secondary crashes.     

Identifying crash propensity using specific traffic speed conditions

INTRODUCTION: In spite of recent advances in traffic surveillance technology and ever-growing concern over traffic safety, there have been very few research efforts establishing links between real-time traffic flow parameters and crash occurrence. This study aims at identifying patterns in the freeway loop detector data that potentially precede traffic crashes. METHOD: The proposed solution essentially involves classification of traffic speed patterns emerging from the loop detector data. Historical crash and loop detector data from the Interstate-4 corridor in the Orlando metropolitan area were used for this study. Traffic speed data from sensors embedded in the pavement (i.e., loop detector stations) to measure characteristics of the traffic flow were collected for both crash and non-crash conditions. Bayesian classifier based methodology, probabilistic neural network (PNN), was then used to classify these data as belonging to either crashes or non-crashes. PNN is a neural network implementation of well-known Bayesian-Parzen classifier. With its superb mathematical credentials, the PNN trains much faster than multilayer feed forward networks. The inputs to final classification model, selected from various candidate models, were logarithms of the coefficient of variation in speed obtained from three stations, namely, station of the crash (i.e., station nearest to the crash location) and two stations immediately preceding it in the upstream direction (measured in 5 minute time slices of 10-15 minutes prior to the crash time). RESULTS: The results showed that at least 70% of the crashes on the evaluation dataset could be identified using the classifiers developed in this paper.     

The interactive effect on injury severity of driver-vehicle units in two-vehicle crashes

IntroductionThis study sets out to investigate the interactive effect on injury severity of driver-vehicle units in two-vehicle crashes.MethodA Bayesian hierarchical ordered logit model is proposed to relate the variation and correlation of injury severity of drivers involved in two-vehicle crashes to the factors of both driver-vehicle units and the crash configurations. A total of 6417 crash records with 12,834 vehicles involved in Florida are used for model calibration.ResultsThe results show that older, female and not-at-fault drivers and those without use of safety equipment are more likely to be injured but less likely to injure the drivers in the other vehicles. New vehicles and lower speed ratios are associated with lower injury degree of both drivers involved. Compared with automobiles, vans, pick-ups, light trucks, median trucks, and heavy trucks possess better self-protection and stronger aggressivity. The points of impact closer to the driver's seat in general indicate a higher risk to the own drivers while engine cover and vehicle rear are the least hazardous to other drivers. Head-on crashes are significantly more severe than angle and rear-end crashes. We found that more severe crashes occurred on roadways than on shoulders or safety zones.ConclusionsBased on these results, some suggestions for traffic safety education, enforcement and engineering are made. Moreover, significant within-crash correlation is found in the crash data, which demonstrates the applicability of the proposed model.     

Driver speed selection and crash risk: Insights from the naturalistic driving study

IntroductionThis study investigates how speed limits affect driver speed selection, as well as the related crash risk, while controlling for various confounding factors such as traffic volumes and roadway geometry. Data from a naturalistic driving study are used to examine how driver speed selection varies among freeways with different posted speed limits, as well as how the likelihood of crash/near-crash events change with respect to mean speed and standard deviation.MethodRegression models are estimated to assess three measures of interest: the average speed of vehicles during the time preceding crash/near-crash and baseline (i.e., normal) driving events; the variation in travel speeds leading up to each event as quantified by the standard deviation in speeds over this period; and the probability of a specific event resulting in a crash/near-crash based on speed selection and other factors.ResultsSpeeds were relatively stable across levels-of-service A and B, within a range of 1.5 mph on average. Speeds were marginally lower (3.3 mph) on freeways posted at 65 mph versus 70 mph. In comparison, speeds were approximately 10.2 to 13.4 mph lower on facilities posted at 55 mph or 60 mph. Speeds were shown to be 2.5 mph lower in rainy weather and 11 mph lower under snow or sleet.ConclusionsSignificant correlation was observed with respect to speed selection behavior among the same individuals. Mean speeds are shown to increase with speed limits. However, these increases are less pronounced at higher speed limits. Drivers tend to reduce their travel speeds in presence of junctions and work zones, under adverse weather conditions, and particularly under heavy congestion. Crash risk increased with the standard deviation in speed, as well as on vertical curves and ramp junctions, and among the youngest and oldest age groups of drivers.     

Crashes and near-crashes on horizontal curves along rural two-lane highways: Analysis of naturalistic driving data

IntroductionPrior research has shown the probability of a crash occurring on horizontal curves to be significantly higher than on similar tangent segments, and a disproportionally higher number of curve-related crashes occurred in rural areas. Challenges arise when analyzing the safety of horizontal curves due to imprecision in integrating information as to the temporal and spatial characteristics of each crash with specific curves.MethodsThe second Strategic Highway Research Program(SHRP 2) conducted a large-scale naturalistic driving study (NDS),which provides a unique opportunity to better understand the contributing factors leading to crash or near-crash events. This study utilizes high-resolution behavioral data from the NDS to identify factors associated with 108 safety critical events (i.e., crashes or near-crashes) on rural two-lane curves. A case-control approach is utilized wherein these events are compared to 216 normal, baseline-driving events. The variables examined in this study include driver demographic characteristics, details of the traffic environment and roadway geometry, as well as driver behaviors such as in-vehicle distractions.ResultsLogistic regression models are estimated to discern those factors affecting the likelihood of a driver being crash-involved. These factors include high-risk behaviors, such as speeding and visual distractions, as well as curve design elements and other roadway characteristics such as pavement surface conditions.ConclusionsThis paper successfully integrated driver behavior, vehicle characteristics, and roadway environments into the same model. Logistic regression model was found to be an effective way to investigate crash risks using naturalistic driving data.Practical ApplicationsThis paper revealed a number of contributing factors to crashes on rural two-lane curves, which has important implications in traffic safety policy and curve geometry design. This paper also discussed limitations and lessons learned from working with the SHRP 2 NDS data. It will benefit future researchers who work with similar type of data.