Culpable versus non-culpable traffic accidents; what is wrong with this picture?

INTRODUCTION: It is often implicitly or explicitly assumed in traffic accident research that drivers with accidents designated as non-culpable are a random sample from the population. However, this assumption is dependent upon differences in the criterion used for culpability. If drivers are erroneously categorized by assuming randomness, results could be grossly misleading. METHOD: The assumption of randomness leads to two predictions: first, no correlation should exist between culpable and non-culpable crashes; and second, the accident groups should differ on the variables known to be associated with accidents, such as amount of driving experience. These predictions were tested in two samples of bus drivers. RESULTS: It was found that in a sample with a harsh criterion (70% culpable accidents) for crash responsibility, the drivers with non-culpable accidents had the features expected, namely, they were more experienced for example, while in a sample with a lenient criterion (50 % culpable), this was not so. DISCUSSION: It was concluded that similar studies to the present one would need to be undertaken to establish exactly what percentage of drivers in a given population should be assigned culpable accidents, and construct a criterion that yields this ratio. Otherwise, the theoretical assumptions of randomness and non-responsibility will probably be violated to some degree. IMPACT ON INDUSTRY: Many estimates of risk of crash involvement may have been wrong. Given the potential for erroneous criteria, a number of studies may make invalid assumptions from their data.     

An investigation on multi-vehicle motorcycle crashes using log-linear models

Motorcyclists are the most crash-prone road-user group in many Asian countries including Singapore; however, factors influencing motorcycle crashes are still not well understood. This study examines the effects of various roadway characteristics, traffic control measures and environmental factors on motorcycle crashes at different location types including expressways and intersections. Using techniques of categorical data analysis, this study has developed a set of log-linear models to investigate multi-vehicle motorcycle crashes in Singapore. Motorcycle crash risks in different circumstances have been calculated after controlling for the exposure estimated by the induced exposure technique. Results show that night-time influence increases crash risks of motorcycles particularly during merging and diverging manoeuvres on expressways, and turning manoeuvres at intersections. Riders appear to exercise more care while riding on wet road surfaces particularly during night. Many hazardous interactions at intersections tend to be related to the failure of drivers to notice a motorcycle as well as to judge correctly the speed/distance of an oncoming motorcycle. Road side conflicts due to stopping/waiting vehicles and interactions with opposing traffic on undivided roads have been found to be as detrimental factors on motorcycle safety along arterial, main and local roads away from intersections. Based on the findings of this study, several targeted countermeasures in the form of legislations, rider training, and safety awareness programmes have been recommended.     

Crash risk factors for novice motorcycle riders

Background: Motorcycle riders have the highest injury and fatality rates among all road users. This research sought in-depth understanding of crash risk factors to help in developing targeted measures to reduce motorcycle crash injuries and fatalities. Methods: We used interview data from a study of 2,399 novice motorcycle riders in Victoria, Australia from 2010 to 2012 linked with their police-recorded crash and offence data. The outcome measure was self and/or police reported crash. The association between potential risk factors and crashes was explored in multivariable logistic regression models. Results: In the multivariable analysis, riders who reported being involved in three or more near crashes had 1.74 times (95% CI 1.11–2.74) higher odds of crashing compared to riders who reported no near-crash events, and riders who participated in a pre-learner course had 1.41 times higher odds of crashing (95% CI 1.07–1.87) compared with riders who did not attend a pre-learner course. Riders who had been involved in a crash before the study had 1.58 times (95% CI 1.14–2.19) higher odds of crashing during the study period compared with riders who were not involved in a crash. Each additional month of having held a license and learner permit decreased the odds of crashing by 2%, and each additional 1,000 km of riding before the study increased the odds of crashing by 2%. Conclusion: Measures of pre-learner training and riding experience were the strongest predictors of crashing in this cohort of novice motorcycle riders. At the time of the study there was no compulsory rider training to obtain a learner permit in Victoria and no on-road courses were available. It may be plausible that riders who voluntarily participated in an unregulated pre-learner course became or remained at high risk of crash after obtaining a rider license. We suggest systematically reviewing the safety benefits of voluntary versus mandatory pre-learner and learner courses and the potential need to include on-road components.     

The injury epidemiology of adult riders in vehicle-two-wheeler crashes in China,Ningbo, 2011–2015

Introduction: We used road crashes between vehicles and two-wheelers from Yinzhou District Ningbo in 2011–2015 from the China In-depth Accident Study (CIDAS) as sample cases. The risk factors of different injury severity grades were analyzed. Method: The classification tree model was used to screen the possible interaction items, and the corresponding regression model was constructed according to the results of the tree model to explore the influencing factors of cyclist injury. Results: The road types, weather types, gender, age of the riders, and vehicle speed were significantly correlated with the dependent variables. The interaction effect of gender*road type, weather*age, weather*speed and speed*age were obtained through a tree model. Conclusions: The risk of male casualties at the crossroads was 3.31 times higher than that of female casualties at the straight roads. On sunny days, the risk of crash casualties in Ningbo was low, and the fatality risk when the speed reached 38 km/h was 10%. Under the interaction effect of weather and age, the fatality risk in cloudy/foggy and rainy days was almost coincident, and the serious risk in cloudy/foggy conditions was the highest. Practical applications: Through factor analysis, it is confirmed that there is interaction effect among the factors, and it can provide reference for relevant departments to formulate more targeted and effective governance strategies.     

Age and engine displacement are associated with unsafe motorcycle rider actions

Background: Our goal was to examine the relationship between age and engine displacement in cubic centimeters (CCs) and crash responsibility. Methods: Male motorcyclists, aged 16–94, involved in a fatal crash in the United States (1987–2015) who tested negative for both drugs and alcohol were included. Employing a case control design, cases had committed one or more Unsafe Motorcyclist Actions (UMAs), the proxy measure of responsibility; controls had no UMAs recorded. Odds ratios were computed via multinomial regression examining the effect of motorcyclists’ age and motorcycle displacement (up to 1500 CCs, in 250 CC increments) on crash responsibility by any UMA and top three individual UMAs committed. Results: A total of 19,166 motorcyclists met our inclusion criteria. Increased displacement was observed in older motorcyclists and in more recent crashes. Fifty-six percent of motorcyclists committed one or more UMAs (n = 10,743). The top three individual UMAs were: Speeding (35%, n = 6,728), Weaving (24%, n = 3,269), and Erratic Operation (6%, n = 1,162). Odds ratios for committing any UMA were the greatest for riders on 750 CC motorcycles, followed closely by 500 and 1000 CC motorcycles. By 1250 CCs the effect of displacement on rider crash responsibility (any UMA) was no longer statistically significant. Typically, younger ages (e.g., 20–30) on motorcycles with 500–1000 CCs were associated with the highest odds of either speeding, weaving, or erratic riding compared to similar aged riders on 250 CC motorcycles. Exceptions were observed, for example riders at 70 years of age on 1500 CCs having higher odds of speeding than younger riders on equivalent CC motorcycles. Conclusion: Education and legislative measures should be considered. Educationally, the development of training interventions focusing on control, stability, and breaking differences with more powerful motorcycles (750 to 1250 CCs) is needed. Legislatively, licensing tiers could be employed based on displacement and educational requirements. Education and legislative measures could help to curb the trend seen between high-powered motorcycles and crash responsibility.     

Modelling the relationship of driver license and offense history with fatal and serious injury (FSI) crash involvement

Introduction: Previous research has indicated that increases in traffic offenses are linked to increased crash involvement rates, making reductions in offending an appropriate measure for evaluating road safety interventions in the short-term. However, the extent to which traffic offending predicts fatal and serious injury (FSI) crash involvement risk is not well established, prompting this new Victorian (Australia) study. Method: A preliminary cluster analysis was performed to describe the offense data and assess FSI crash involvement risk for each cluster. While controlling demographic and licensing variables, the key traffic offenses that predict future FSI crash involvement were then identified. The large sample size allowed the use of machine learning methods such as random forests, gradient boosting, and Least Absolute Shrinkage and Selection Operator (LASSO) regression. This was done for the ‘all driver’ sample and five sometimes overlapping groups of drivers; the young, the elderly, and those with a motorcycle license, a heavy vehicle license endorsement and/or a history of license bans. Results: With the exception of the group of drivers who had a history of bans, offense history significantly improved the accuracy of models predicting future FSI crash involvement using demographic and licensing data, suggesting that traffic offenses may be an important factor to consider when analyzing FSI crash involvement risk and the effects of road safety countermeasures. Conclusions: The results are helpful for identifying driver groups to target with further road safety countermeasures, and for showing that machine learning methods have an important role to play in research of this nature. Practical Application: This research indicates with whom road safety interventions should particularly be applied. Changes to driver demerit policies to better target offenses related to FSI crash involvement and repeat traffic offenders, who are at greater risk of FSI crash involvement, are recommended.     

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.     

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.     

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.