Investigation of crashes at pedestrian hybrid beacons: Results of a large-scale study in Arizona

Introduction: The pedestrian hybrid beacon (PHB) is a traffic control device used at pedestrian crossings. A recent Arizona Department of Transportation research effort investigated changes in crashes for different severity levels and crash types (e.g., rear-end crashes) due to the PHB presence, as well as for crashes involving pedestrians and bicycles. Method: Two types of methodologies were used to evaluate the safety of PHBs: (a) an Empirical Bayes (EB) before-after study, and (b) a long-term cross-sectional observational study. For the EB before-after evaluation, the research team considered three reference groups: unsignalized intersections, signalized intersections, and both unsignalized and signalized intersections combined. Results: For the signalized and combined unsignalized and signalized intersection groups, all crash types considered showed statistically significant reductions in crashes (e.g., total crashes, fatal and injury crashes, rear-end crashes, fatal and injury rear-end crashes, angle crashes, fatal and injury angle crashes, pedestrian-related crashes, and fatal and injury pedestrian-related crashes). A cross-sectional study was conducted with a larger number of PHBs (186) to identify relationships between roadway characteristics and crashes at PHBs, especially with respect to the distance to an adjacent traffic control signal. The distance to an adjacent traffic signal was found to be significant only at the α = 0.1 level, and only for rear-end and fatal and injury rear-end crashes. Conclusions: This analysis represents the largest known study to date on the safety impacts of PHBs, along with a focus on how crossing and geometric characteristics affect crash patterns. The study showed the safety benefits of PHBs for both pedestrians and vehicles. Practical Applications: The findings from this study clearly support the installation of PHBs at midblock or intersection crossings, as well as at crossings on higher-speed roads.     

Disaggregated traffic conditions and road crashes in urban signalized intersections

Introduction: Road safety studies in signalized intersections have been performed extensively using annually aggregated traffic variables and crash frequencies. However, this type of aggregation reduces the strength of the results if variables that oscillate over the course of the day are considered (speed, traffic flow, signal cycle length) because average indicators are not able to describe the traffic conditions preceding the crash occurrence. This study aims to explore the relationship between traffic conditions aggregated in 15-min intervals and road crashes in urban signalized intersections. Method: First, an investigation of the reported crash times in the database was conducted to obtain the association between crashes and their precursor conditions. Then, 4.1 M traffic condition intervals were consolidated and grouped using a hierarchical clustering technique. Finally, charts of the frequency of crashes per cluster were explored. Results: The main findings suggest that high vehicular demand conditions are related to an increase in property damage only (PDO) crashes, and an increase in the number of lanes is linked to more PDO and injury crashes. Injury crashes occurred in a wide range of traffic conditions, indicating that a portion of these crashes were due to speeding, while the other fraction was associated with the vulnerability of road users. Traffic conditions with: (a) low vehicular demand and a long cycle length and (b) high vehicular demand and a short cycle length were critical in terms of PDO and injury crashes. Practical Applications: The use of disaggregated data allowed for a stronger evaluation of the relationship between road crashes and variables that oscillate over the course of the day. This approach also permits the development of real-time risk management strategies to mitigate the frequency of critical traffic conditions and reduce the likelihood of crashes.     

Injury-severity analysis of lane change crashes involving commercial motor vehicles on interstate highways

Introduction: One of the challenging tasks for drivers is the ability to change lanes around large commercial motor vehicles. Lane changing is often characterized by speed, and crashes that occur due to unsafe lane changes can have serious consequences. Considering the economic importance of commercial trucks, ensuring the safety, security, and resilience of freight transportation is of paramount concern to the United States Department of Transportation and other stakeholders. Method: In this study, a mixed (random parameters) logit model was developed to better understand the relationship between crash factors and associated injury severities of commercial vehicle crashes involving lane change on interstate highways. The study was based on 2009–2016 crash data from Alabama. Results: Preliminary data analysis showed that about 4% of the observed crashes were major injury crashes and drivers of commercial motor vehicles were at-fault in more than half of the crashes. Acknowledging potential crash data limitations, the model estimation results reveal that there is increased probability of major injury when lane change crashes occurred on dark unlit portions of interstates and involve older drivers, at-fault commercial vehicle drivers, and female drivers. The results further show that lane change crashes that occurred on interstates with higher number of travel lanes were less likely to have major injury outcomes. Practical Applications: These findings can help policy makers and state transportation agencies increase awareness on the hazards of changing lanes in the immediate vicinity and driving in the blind spots of large commercial motor vehicles. Additionally, law enforcement efforts may be intensified during times and locations of increased unsafe lane changing activities. These findings may also be useful in commercial vehicle driver training and driver licensing programs.     

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.     

Assessing the likelihood of secondary crashes on freeways with Adaptive Signal Control System deployed on alternate routes

Introduction: Reducing the likelihood of freeway secondary crashes will provide significant safety, operational and environmental benefits. This paper presents a method for assessing the likelihood of freeway secondary crashes with Adaptive Signal Control Systems (ASCS) deployed on alternate routes that are typically used by diverted freeway traffic to avoid any delay or congestion due to a freeway primary crash. Method: The method includes four steps: (1) identification of secondary crashes, (2) verification of alternate routes, (3) assessment of the likelihood of secondary crashes for freeways with ASCS deployed on alternate routes and non-ASCS (i.e. pre-timed, semi- or fully-actuated) alternate routes, and (4) investigation of unobserved heterogeneity of the likelihood of freeway secondary crashes. Four freeway sections (i.e., two with ASCS deployed on alternate routes and two non-ASCS alternate routes) in South Carolina are considered. Results and Conclusions: Findings from the logistic regression modeling reveal significant reduction in the likelihood of secondary crashes for one freeway section (i.e., Charleston I-26 E) with ASCS deployed on alternate route. Other factors such as rear-end crash, dark or limited light, peak period, and annual average daily traffic contribute to the likelihood of freeway secondary crashes. Furthermore, random-parameter logistic regression model results for Charleston I-26 E reveal that unobserved heterogeneity of ASCS effect exists across the observations and ASCS are associated with the reduction of the likelihood of freeway secondary crashes for 84% of the observations (i.e., primary crashes). Location of the primary crash on the freeway is observed to affect the benefit of ASCS toward freeway secondary crash reduction as the primary crash’s location determines how many upstream freeway vehicles will be able to take the alternate route. Practical Applications: Based on the findings, it is recommended that the South Carolina Department of Transportation (SCDOT) considers deploying ASCS on alternate routes parallel to freeway sections where high percentages of secondary crashes are found.     

Fatal crashes involving large numbers of vehicles and weather

IntroductionAdverse weather has been recognized as a significant threat to traffic safety. However, relationships between fatal crashes involving large numbers of vehicles and weather are rarely studied according to the low occurrence of crashes involving large numbers of vehicles.MethodBy using all 1,513,792 fatal crashes in the Fatality Analysis Reporting System (FARS) data, 1975–2014, we successfully described these relationships.ResultsWe found: (a) fatal crashes involving more than 35 vehicles are most likely to occur in snow or fog; (b) fatal crashes in rain are three times as likely to involve 10 or more vehicles as fatal crashes in good weather; (c) fatal crashes in snow [or fog] are 24 times [35 times] as likely to involve 10 or more vehicles as fatal crashes in good weather. If the example had used 20 vehicles, the risk ratios would be 6 for rain, 158 for snow, and 171 for fog.ConclusionsTo reduce the risk of involvement in fatal crashes with large numbers of vehicles, drivers should slow down more than they currently do under adverse weather conditions. Driver deaths per fatal crash increase slowly with increasing numbers of involved vehicles when it is snowing or raining, but more steeply when clear or foggy.Practical applicationsWe conclude that in order to reduce risk of involvement in crashes involving large numbers of vehicles, drivers must reduce speed in fog, and in snow or rain, reduce speed by even more than they already do.     

Application of machine learning technique for optimizing roadside design to decrease barrier crash costs,a quantile regression model approach

Introduction: In-transport vehicles often leave the travel lane and encroach onto natural objects on the roadsides. These types of crashes are called run-off the road crashes (ROR). Such crashes accounts for a significant proportion of fatalities and severe crashes. Roadside barrier installation would be warranted if they could reduce the severity of these types of crashes. However, roadside barriers still account for a significant proportion of severe crashes in Wyoming. The impact of the crash severity would be higher if barriers are poorly designed, which could result in override or underride barrier crashes. Several studies have been conducted to identify optimum values of barrier height. However, limited studies have investigated the monetary benefit associated with adjusting the barrier heights to the optimal values. In addition, few studies have been conducted to model barrier crash cost. This is because the crash cost is a heavily skewed distribution, and well-known distributions such as linear or poison models are incapable of capturing the distribution. A semi-parametric distribution such as asymmetric Laplace distribution can be used to account for this type of sparse distribution. Method: Interaction between different predictors were considered in the analysis. Also, to account for exposure effects across various barriers, barrier lengths and traffic volumes were incorporated in the models. This study is conducted by using a novel machine-learning-based cost-benefit optimization to provide an efficient guideline for decision makers. This method was used for predicting barrier crash costs without barrier enhancement. Subsequently the benefit was obtained by optimizing traffic barrier height and recalculating the benefit and cost. The trained model was used for crash cost prediction on barriers with and without crashes. Results: The results of optimization clearly demonstrated the benefit of optimizing the heights of road barriers around the state. Practical Applications: The findings can be utilized by the Wyoming Department of Transportation (WYDOT) to determine the heights of which barriers should be optimized first. Other states can follow the procedure described in this paper to upgrade their roadside barriers.     

Investigating the effects of monthly weather variations on Connecticut freeway crashes from 2011 to 2015

IntroductionThe objective of this research is to investigate the effects of monthly weather conditions on traffic crash experience on freeways, considering the interactions between weather, traffic volumes, and roadway conditions. Methods: Data from the state of Connecticut from 2011to 2015 were used. Random parameters negative binomial models with first-order, autoregressive covariance were estimated for representative types of freeway crashes (front-to-rear, sideswipe-same-direction, and fixed-object), most severe crashes (i.e., fatal and injury crashes), and non-injury crashes (i.e., property-damage-only crashes). Results: Major findings are that variations in monthly traffic volumes, roadway geometry, and weather conditions explain much of the variations in monthly traffic crashes. Time effects exist in the panel monthly data for all types of crashes. Taking into account this effect improves model prediction results. When the raw weather measures are highly correlated, using dimension reduction techniques helps to extract more interpretable weather factors. By considering the interaction effects between roadway condition variables, additional findings were found. In general, lower temperature, more heavy fog days, decreased precipitation, lower wind speed, higher monthly traffic volumes, and narrower inside shoulder were found to be associated with higher monthly crashes. The effects of area type and outside shoulder width change dramatically as the number of through lanes changes. Practical applications: The findings of this research could help researchers and general readers gain a better understanding of the effects of monthly weather conditions and other roadway factors on freeway crashes and give engineers practical guidelines on improving freeway safety.     

Land-traffic crash leading to passenger vehicle submersion,drowning and other fatal injuries: A 44-year study based on records from the Finnish Crash Data Institute

Background: Land motor traffic crash (LMTC) -related drownings are an overlooked and preventable cause of injury death. The aim of this study was to analyze the profile of water-related LMTCs involving passenger cars and leading to drowning and fatal injuries in Finland, 1972 through 2015. Materials and methods: The database of the Finnish Crash Data Institute (FCDI) that gathers detailed information on fatal traffic accidents provided records on all LMTCs leading to drowning during the study period and, from 2002 to 2015, on all water-related LMTCs, regardless of the cause of death. For each crash, we considered variables on circumstances, vehicle, and fatality profiles. Results: During the study period, the FCDI investigated 225 water-related LMTCs resulting in 285 fatalities. The majority of crashes involved passenger cars (124), and the cause of death was mostly drowning (167). Only 61 (36.5%) fatalities suffered some–generally mild–injuries. The crashes frequently occurred during fall or summer (63.7%), in a river or ditch (60.5%), and resulted in complete vehicle’s submersion (53.7 %). Half of the crashes occurred in adverse weather conditions and in over 40% of the cases, the driver had exceeded the speed limit. Among drivers, 77 (68.8%) tested positive for alcohol (mean BAC 1.8%). Conclusion: Multidisciplinary investigations of LMTCs have a much higher potential than do exclusive police and medico-legal investigations. The risk factors of water-related LMTCs are similar to those of other traffic crashes. However, generally the fatal event in water-related LMTC is not the crash itself, but drowning. The paucity of severe physical injuries suggests that victims’ functional capacity is usually preserved during vehicle submersion. Practical Applications: In water-related LMTCs, expansion of safety measures is warranted from general traffic-injury prevention to prevention of drowning, including development of safety features for submerged vehicles and simple self-rescue protocols to escape from a sinking vehicle.     

Investigating the different characteristics of weekday and weekend crashes

IntroductionThis study provides a systematic approach to investigate the different characteristics of weekday and weekend crashes.MethodWeekend crashes were defined as crashes occurring between Friday 9 p.m. and Sunday 9 p.m., while the other crashes were labeled as weekday crashes. In order to reveal the various features for weekday and weekend crashes, multi-level traffic safety analyses have been conducted. For the aggregate analysis, crash frequency models have been developed through Bayesian inference technique; correlation effects of weekday and weekend crash frequencies have been accounted. A multivariate Poisson model and correlated random effects Poisson model were estimated; model goodness-of-fits have been compared through DIC values. In addition to the safety performance functions, a disaggregate crash time propensity model was calibrated with Bayesian logistic regression model. Moreover, in order to account for the cross-section unobserved heterogeneity, random effects Bayesian logistic regression model was employed.ResultsIt was concluded that weekday crashes are more probable to happen during congested sections, while the weekend crashes mostly occur under free flow conditions. Finally, for the purpose of confirming the aforementioned conclusions, real-time crash prediction models have been developed. Random effects Bayesian logistic regression models incorporating the microscopic traffic data were developed. Results of the real-time crash prediction models are consistent with the crash time propensity analysis. Furthermore, results from these models would shed some lights on future geometric improvements and traffic management strategies to improve traffic safety.Impact on IndustryUtilizing safety performance to identify potential geometric improvements to reduce crash occurrence and monitoring real-time crash risks to pro-actively improve traffic safety.     

Investigation of injury severities in single-vehicle crashes in North Carolina using mixed logit models

IntroductionRoadway departure (RwD) crashes, comprising run-off-road (ROR) and cross-median/centerline head-on collisions, are one of the most lethal crash types. According to the FHWA, between 2015 and 2017, an average of 52 percent of motor vehicle traffic fatalities occurred each year due to roadway departure crashes. An avoidance maneuver, inattention or fatigue, or traveling too fast with respect to weather or geometric road conditions are among the most common reasons a driver leaves the travel lane. Roadway and roadside geometric design features such as clear zones play a significant role in whether human error results in a crash. Method: In this paper, we used mixed-logit models to investigate the contributing factors on injury severity of single-vehicle ROR crashes. To that end, we obtained five years' (2010–2014) of crash data related to roadway departures (i.e., overturn and fixed-object crashes) from the Federal Highway Administration's Highway Safety Information System Database. Results: The results indicate that factors such as driver conditions (e.g., age), environmental conditions (e.g., weather conditions), roadway geometric design features (e.g., shoulder width), and vehicle conditions significantly contributed to the severity of ROR crashes. Conclusions: Our results provide valuable information for traffic design and management agencies to improve roadside design policies and implementing appropriately forgiving roadsides for errant vehicles. Practical applications: Our results show that increasing shoulder width and keeping fences at the road can reduce ROR crash severity significantly. Also, increasing road friction by innovative materials and raising awareness campaigns for careful driving at daylight can decrease the ROR crash severity.     

Ordered logistic models of influencing factors on crash injury severity of single and multiple-vehicle downgrade crashes: A case study in Wyoming

Introduction: The state of Wyoming, like other western United States, is characterized by mountainous terrain. Such terrain is well noted for its severe downgrades and difficult geometry. Given the specific challenges of driving in such difficult terrain, crashes with severe injuries are bound to occur. The literature is replete with research about factors that influence crash injury severity under different conditions. Differences in geometric characteristics of downgrades and mechanics of vehicle operations on such sections mean different factors may be at play in impacting crash severity in contrast to straight, level roadway sections. However, the impact of downgrades on injury severity has not been fully explored in the literature. This study is thus an attempt to fill this research gap. In this paper, an investigation was carried out to determine the influencing factors of crash injury severities of downgrade crashes. Method: Due to the ordered nature of the response variable, the ordered logit model was chosen to investigate the influencing factors of crash injury severities of downgrade crashes. The model was calibrated separately for single and multiple-vehicle crashes to ensure the different factors influencing both types of crashes were captured. Results: The parameter estimates were as expected and mostly had signs consistent with engineering intuition. The results of the ordered model for single-vehicle crashes indicated that alcohol, gender, road condition, vehicle type, point of impact, vehicle maneuver, safety equipment use, driver action, and annual average daily traffic (AADT) per lane all impacted the injury severity of downgrade crashes. Safety equipment use, lighting conditions, posted speed limit, and lane width were also found to be significant factors influencing multiple-vehicle downgrade crashes. Injury severity probability plots were included as part of the study to provide a pictorial representation of how some of the variables change in response to each level of crash injury severity. Conclusion: Overall, this study provides insights into contributory factors of downgrade crashes. The literature review indicated that there are substantial differences between single- and multiple vehicle crashes. This was confirmed by the analysis which showed that mostly, separate factors impacted the crash injury severity of the two crash types. Practical applications: The results of this study could be used by policy makers, in other locations, to reduce downgrade crashes in mountainous areas.     

Real-world effects of rear automatic braking and other backing assistance systems

Objective: To evaluate the effects of rearview cameras, rear parking sensors, and rear automatic braking systems on backing crashes. Method: Negative binomial regression was used to compare police-reported backing crash involvements per insured vehicle year in 23 US states during 2012–2015 among General Motors vehicles with Rear Vision Camera alone; Rear Parking Assist alone (rear parking sensors); Rear Vision Camera and Rear Parking Assist; or the Rear Vision Camera, Rear Parking Assist, and Rear Automatic Braking to vehicles with none of these systems. Modeling controlled for other backing assistance systems on vehicles and factors that may affect crash risk. Results: The combination of Rear Vision Camera and Rear Parking Assist reduced backing crash involvement rates by 42%. When Rear Automatic Braking was added to the Rear Vision Camera and Rear Parking Assist, vehicles with all three systems had backing crash involvement rates that were 78% lower than vehicles with none of the systems. On vehicles with Rear Parking Assist alone or Rear Vision Camera alone, backing crash involvement rates were reduced 28% and 5%, respectively, but these reductions were not statistically significant. Conclusions: Rearview cameras and rear parking sensors are preventing some backing crashes, but their effectiveness may be constrained in part by drivers not using or responding to the systems appropriately. Rear automatic braking adds to the effectiveness of these systems because it does not rely entirely on appropriate driver response. Practical applications: Rear parking sensors and rearview cameras are available on most new vehicles, but availability of rear automatic braking is limited. If more vehicles were equipped with rear automatic braking that performed like the system evaluated in the current study, many backing crashes that still occur among vehicles with rearview cameras and rear parking sensors could be prevented.     

Rage against the machine? Google's self-driving cars versus human drivers

IntroductionAutomated driving represents both challenges and opportunities in highway safety. Google has been developing self-driving cars and testing them under employee supervision on public roads since 2009. These vehicles have been involved in several crashes, and it is of interest how this testing program compares to human drivers in terms of safety.MethodsGoogle car crashes were coded by type and severity based on narratives released by Google. Crash rates per million vehicle miles traveled (VMT) were computed for crashes deemed severe enough to be reportable to police. These were compared with police-reported crash rates for human drivers. Crash types also were compared.ResultsGoogle cars had a much lower rate of police-reportable crashes per million VMT than human drivers in Mountain View, Calif., during 2009–2015 (2.19 vs 6.06), but the difference was not statistically significant. The most common type of collision involving Google cars was when they got rear-ended by another (human-driven) vehicle. Google cars shared responsibility for only one crash.ConclusionsThese results suggest Google self-driving cars, while a test program, are safer than conventional human-driven passenger vehicles; however, currently there is insufficient information to fully examine the extent to which disengagements affected these results.Practical applicationResults suggest that highly-automated vehicles can perform more safely than human drivers in certain conditions, but will continue to be involved in crashes with conventionally-driven vehicles.     

The impact of traffic barrier geometric features on crash frequency and injury severity of non-interstate highways

Introduction: The main objective of this research is to investigate the effect of traffic barrier geometric characteristics on crashes that occurred on non-interstate roads. Method: For this purpose, height, side-slope rate, post-spacing, and lateral offset of about 137 miles of traffic barriers were collected on non-interstate (state, federal aid primary, federal aid secondary, and federal aid urban) highways in Wyoming. In addition, crash reports recorded between 2008 and 2017 were added to the traffic barrier dataset. The safety performance of traffic barriers with regards to their geometric features was analyzed in terms of crash frequency and crash severity using random-parameters negative binomial, and random-parameters ordered logit models, respectively. Results: From the results, box beam barriers with a height of 27–29 inches were less likely to be associated with injury and fatal injury crashes compared to other barrier types. On the other hand, the likelihood of a severe injury crash was found to be higher for box beam barriers with a height taller than 31 inches. Both W-beam and box beam barriers with a post-spacing between 6.1 and 6.3 inches reduced the probability of severe injury crashes. In terms of the crash frequency, flare traffic barriers had a lower crash frequency compared to parallel traffic barriers. Non-interstate roads without longitudinal rumble strips were associated with a higher rate of traffic barrier crashes.     

Prediction of responsibility for drivers and riders involved in injury road crashes

Introduction: Responsibility analysis allows the evaluation of crash risk factors from crash data only, but requires a reliable responsibility assessment. The aim of the present study is to predict expert responsibility attribution (considered as a gold-standard) from explanatory variables available in crash data routinely recorded by the police, according to a data-driven process with explicit rules. Method: Driver responsibility was assessed by experts using all information contained in police reports for a sample of about 5000 injury crashes that occurred in France in 2011. Three statistical methods were used to predict expert responsibility attribution: logistic regression with L1 penalty, random forests, and boosting. Potential predictors of expert attribution referred to inappropriate driver actions and to external conditions at the time of the crash. Logistic regression was chosen to construct a score to assess responsibility for drivers and riders in crashes involving one or more motor vehicles, or involving a cyclist or pedestrian. Results: Cross-validation showed that our tool can predict expert responsibility assessments on new data sets. In addition, responsibility analyses performed using either the expert responsibility or our predicted responsibility return similar odds ratios. Our scoring process can then be used to reliably assess responsibility based on national police report databases, provided that they include the information needed to construct the score.     

Do NHTSA vehicle safety ratings affect side impact crash outcomes?

Introduction: Side impact crash injuries tend to be severe, mainly due to the effects of the mechanism of such crashes. This study addresses the relationship between side impact crash injury severities and side impact safety ratings of the passenger cars involved in such crashes. It is motivated by the lack of research on side impact safety ratings in relation to the real-world crash outcomes. Method: Analysis of Crashworthiness Data System’s (CDS) data show the head and thorax are the most common regions of impact of severe injuries, while the neck is the least. Irrespective of body regions, higher-rated vehicles were found to provide better occupant protection to both younger and older driver age groups. Assessment based on injury severity score (ISS) indicates that higher-rated vehicles have an overall lower average ISS compared to lower-rated vehicles. Results: Ultimately, this study shows that vehicles rated with National Highway Traffic Safety Administration’s (NHTSA) new criteria had lower average ISS compared to vehicles rated under the old criteria. The 2011 NHTSA side impact rating criteria being relatively new, it has very few crashes to draw meaningful statistically significant conclusions. However, this paper establishes the fact that vehicles with higher star ratings (under experimental conditions) indeed offer increased occupant protection in the field conditions. Practical applications: Previous studies have found that safety was given priority while buying new vehicles. However, people associated vehicle safety with technologies and specific safety features rather than the vehicle’s crash test results or ratings (Koppel, Charlton, Fildes, & Fitzharris, 2008). The results from this study provide a point of reference for safety advocates to educate the drivers about the importance of considering vehicle safety ratings during a vehicle purchase.     

Pedestrians under influence (PUI) crashes: Patterns from correspondence regression analysis

Introduction: Alcohol-related impairment is a key contributing factor in traffic crashes. However, only a few studies have focused on pedestrian impairment as a crash characteristic. In Louisiana, pedestrian fatalities have been increasing. From 2010 to 2016, the number of pedestrian fatalities increased by 62%. A total of 128 pedestrians were killed in traffic crashes in 2016, and 34.4% of those fatalities involved pedestrians under the influence (PUI) of drugs or alcohol. Furthermore, alcohol-PUI fatalities have increased by 120% from 2010 to 2016. There is a vital need to examine the key contributing attributes that are associated with a high number of PUI crashes. Method: In this study, the research team analyzed Louisiana’s traffic crash data from 2010 to 2016 by applying correspondence regression analysis to identify the key contributing attributes and association patterns based on PUI involved injury levels. Results: The findings identified five risk clusters: intersection crashes at business/industrial locations, mid-block crashes on undivided roadways at residential and business/residential locations, segment related crashes associated with a pedestrian standing in the road, open country crashes with no lighting at night, and pedestrian violation related crashes on divided roadways. The association maps identified several critical attributes that are more associated with fatal and severe PUI crashes. These attributes are dark to no lighting, open country roadways, and non-intersection locations. Practical Applications: The findings of this study may be used to help design effective mitigation strategies to reduce PUI crashes.     

Motorcycle crashes potentially preventable by three crash avoidance technologies on passenger vehicles

Objective: The objective of this study was to identify and quantify the motorcycle crash population that would be potential beneficiaries of 3 crash avoidance technologies recently available on passenger vehicles.

Methods: Two-vehicle crashes between a motorcycle and a passenger vehicle that occurred in the United States during 2011–2015 were classified by type, with consideration of the functionality of 3 classes of passenger vehicle crash avoidance technologies: frontal crash prevention, lane maintenance, and blind spot detection. Results were expressed as the percentage of crashes potentially preventable by each type of technology, based on all known types of 2-vehicle crashes and based on all crashes involving motorcycles.

Results: Frontal crash prevention had the largest potential to prevent 2-vehicle motorcycle crashes with passenger vehicles. The 3 technologies in sum had the potential to prevent 10% of fatal 2-vehicle crashes and 23% of police-reported crashes. However, because 2-vehicle crashes with a passenger vehicle represent fewer than half of all motorcycle crashes, these technologies represent a potential to avoid 4% of all fatal motorcycle crashes and 10% of all police-reported motorcycle crashes.

Discussion: Refining the ability of passenger vehicle crash avoidance systems to detect motorcycles represents an opportunity to improve motorcycle safety. Expanding the capabilities of these technologies represents an even greater opportunity. However, even fully realizing these opportunities can affect only a minority of motorcycle crashes and does not change the need for other motorcycle safety countermeasures such as helmets, universal helmet laws, and antilock braking systems.     

Analyses of rear-end crashes based on classification tree models

OBJECTIVE: Signalized intersections are accident-prone areas especially for rear-end crashes due to the fact that the diversity of the braking behaviors of drivers increases during the signal change. The objective of this article is to improve knowledge of the relationship between rear-end crashes occurring at signalized intersections and a series of potential traffic risk factors classified by driver characteristics, environments, and vehicle types. METHODS: Based on the 2001 Florida crash database, the classification tree method and Quasi-induced exposure concept were used to perform the statistical analysis. Two binary classification tree models were developed in this study. One was used for the crash comparison between rear-end and non-rear-end to identify those specific trends of the rear-end crashes. The other was constructed for the comparison between striking vehicles/drivers (at-fault) and struck vehicles/drivers (not-at-fault) to find more complex crash pattern associated with the traffic attributes of driver, vehicle, and environment. RESULTS: The modeling results showed that the rear-end crashes are over-presented in the higher speed limits (45-55 mph); the rear-end crash propensity for daytime is apparently larger than nighttime; and the reduction of braking capacity due to wet and slippery road surface conditions would definitely contribute to rear-end crashes, especially at intersections with higher speed limits. The tree model segmented drivers into four homogeneous age groups: < 21 years, 21-31 years, 32-75 years, and > 75 years. The youngest driver group shows the largest crash propensity; in the 21-31 age group, the male drivers are over-involved in rear-end crashes under adverse weather conditions and the 32-75 years drivers driving large size vehicles have a larger crash propensity compared to those driving passenger vehicles. CONCLUSIONS: Combined with the quasi-induced exposure concept, the classification tree method is a proper statistical tool for traffic-safety analysis to investigate crash propensity. Compared to the logistic regression models, tree models have advantages for handling continuous independent variables and easily explaining the complex interaction effect with more than two independent variables. This research recommended that at signalized intersections with higher speed limits, reducing the speed limit to 40 mph efficiently contribute to a lower accident rate. Drivers involved in alcohol use may increase not only rear-end crash risk but also the driver injury severity. Education and enforcement countermeasures should focus on the driver group younger than 21 years. Further studies are suggested to compare crash risk distributions of the driver age for other main crash types to seek corresponding traffic countermeasures.