Quasi-induced exposure method: evaluation of not-at-fault assumption

Taxi drivers' traffic violations detection using random forest algorithm: A case study in China

OBJECTIVE

To effectively explore the impacts of several key factors on taxi drivers' traffic violations and provide traffic management departments with scientific decisions to reduce traffic fatalities and injuries.

METHODS

43,458 electronic enforcement data about taxi drivers' traffic violations in Nanchang City, Jiangxi Province, China, from July 1, 2020, to June 30, 2021, were utilized to explore the characteristics of traffic violations. A random forest algorithm was used to predict the severity of taxi drivers' traffic violations and 11 factors affecting traffic violations, including time, road conditions, environment, and taxi companies were analyzed using the Shapley Additionality Explanation (SHAP) framework.

RESULTS

Firstly, the ensemble method Balanced Bagging Classifier (BBC) was applied to balance the dataset. The results showed that the imbalance ratio (IR) of the original imbalanced dataset reduced from 6.61% to 2.60%. Moreover, a prediction model for the severity of taxi drivers' traffic violations was established by using the Random Forest, and the results showed that accuracy, m_F1, m_G-mean, m_AUC, and m_AP obtained 0.877, 0.849, 0.599, 0.976, and 0.957, respectively. Compared with the algorithms of Decision Tree, XG Boost, Ada Boost, and Neural Network, the performance measures of the prediction model based on Random Forest were the best. Finally, the SHAP framework was used to improve the interpretability of the model and identify important factors affecting taxi drivers' traffic violations. The results showed that functional districts, location of the violation, and road grade were found to have a high impact on the probability of traffic violations; their mean SHAP values were 0.39, 0.36, and 0.26, respectively.

CONCLUSIONS

Findings of this paper may help to discover the relationship between the influencing factors and the severity of traffic violations, and provide a theoretical basis for reducing the traffic violations of taxi drivers and improving the road safety management.

Exploring European Heavy Goods Vehicle Crashes Using a Three-Level Analysis of Crash Data

Heavy goods vehicles (HGVs) are involved in 4.5% of police-reported road crashes in Europe and 14.2% of fatal road crashes. Active and passive safety systems can help to prevent crashes or mitigate the consequences but need detailed scenarios based on analysis of region-specific data to be designed effectively; however, a sufficiently detailed overview focusing on long-haul trucks is not available for Europe. The aim of this paper is to give a comprehensive and up-to-date analysis of crashes in the European Union that involve HGVs weighing 16 tons or more (16 t+). The identification of the most critical scenarios and their characteristics is based on a three-level analysis, as follows. Crash statistics based on data from the Community Database on Accidents on the Roads in Europe (CARE) provide a general overview of crashes involving HGVs. These results are complemented by a more detailed characterization of crashes involving 16 t+ trucks based on national road crash data from Italy, Spain, and Sweden. This analysis is further refined by a detailed study of crashes involving 16 t+ trucks in the German In-Depth Accident Study (GIDAS), including a crash causation analysis. The results show that most European HGV crashes occur in clear weather, during daylight, on dry roads, outside city limits, and on nonhighway roads. Three main scenarios for 16 t+ trucks are characterized in-depth: rear-end crashes in which the truck is the striking partner, conflicts during right turn maneuvers of the truck with a cyclist riding alongside, and pedestrians crossing the road in front of the truck. Among truck-related crash causes, information admission failures (e.g., distraction) were the main crash causation factor in 72% of cases in the rear-end striking scenario while information access problems (e.g., blind spots) were present for 72% of cases in the cyclist scenario and 75% of cases in the pedestrian scenario. The three levels of data analysis used in this paper give a deeper understanding of European HGV crashes, in terms of the most common crash characteristics on EU level and very detailed descriptions of both kinematic parameters and crash causation factors for the above scenarios. The results thereby provide both a global overview and sufficient depth of analysis of the most relevant cases and aid safety system development.

Prevalence of and Factors Associated with Driving a Vehicle with Timed-Out Inspection Certificate in Spain

This study aimed to estimate the prevalence of vehicles on the road with a timed-out vehicle inspection certificate (TOVIC) and the associations of driver, vehicle, and environmental factors with this infraction. A quasi-induced exposure approach was used in this cross-sectional study to analyze a case series comprising 51,305 drivers passively involved in clean collisions (only one infractor driver involved) between two or more vehicles registered in the Spanish National Register of Road Crashes with Victims from 2014 to 2017. The prevalence of TOVIC was estimated in the whole sample and in subgroups defined by the variables considered. Multivariate logistic regression modeling was used to obtain adjusted odds ratios for the association between TOVIC and each category of the variables. The prevalence of TOVIC was low, although significant differences were found for certain subcategories of drivers, vehicles, and environmental factors. Significant positive adjusted associations were found between TOVIC and license-related infractions, vans (compared to cars), vehicle age, and vehicle defects. Several vehicle-related factors potentially associated with a high risk of involvement in a crash were clearly related with TOVIC, which suggests the need for measures to control this non-negligible number of high-risk vehicles on the road.

Analysis and Prediction of Vehicle Kilometers Traveled: A Case Study in Spain

Knowledge of the kilometers traveled by vehicles is essential in transport and road safety studies as an indicator of exposure and mobility. Its application in the determination of user risk indices in a disaggregated manner is of great interest to the scientific community and the authorities in charge of ensuring road safety on highways. This study used a sample of the data recorded during passenger vehicle inspections at Vehicle Technical Inspection stations and housed in a data warehouse managed by the General Directorate for Traffic of Spain. This study has three notable characteristics: (1) a novel data source is explored, (2) the methodology developed applies to other types of vehicles, with the level of disaggregation the data allows, and (3) pattern extraction and the estimate of mobility contribute to the continuous and necessary improvement of road safety indicators and are aligned with goal 3 (Good Health and Well-Being: Target 3.6) of The United Nations Sustainable Development Goals of the 2030 Agenda. An Operational Data Warehouse was created from the sample received, which helped in obtaining inference values for the kilometers traveled by Spanish fleet vehicles with a level of disaggregation that, to the knowledge of the authors, was unreachable with advanced statistical models. Three machine learning methods, CART, random forest, and gradient boosting, were optimized and compared based on the performance metrics of the models. The three methods identified the age, engine size, and tare weight of passenger vehicles as the factors with greatest influence on their travel patterns.

Road Safety as a Public Health Problem: Case of Ecuador in the Period 2000–2019

Background: Road safety is a significant public health problem because it causes negative consequences on victims and families. The objective was to analyze the most significant changes in traffic crashes in Ecuador during the period from 2000 to 2019. With data obtained from the National Institute of Statistics and Census, we performed the analysis to identify: the number of traffic crashes, the number of victims, and other study variables. Methods: Descriptive and analytical statistics and the contrast of proportions were used to analyze data from 2000 to 2019. Results: According to the ideal joinpoint analysis model, there was a significant decrease in the number of recorded traffic accidents from 2015 to 2019 of −8.54 per year, while the tendency to die increased in females (2.05 per year) and males (3.29 per year). The most common crash was a collision, and the automobile appeared as the most involved vehicle from 2015 to 2019. The hypothesis test contrast is used to determine if statistically significant differences exist between age groups by gender of the driver injured in the period 2017–2018. Conclusions: This study determines the most significant changes in the variables related to traffic crashes, where mortality due to this cause in the last four years has had a growth rate of 1.8% compared to collisions that presented a rate of −31.12%. The contrast of the hypothesis test shows significant differences in the injury level between males and female drivers, depending on the age group.

Validating the representativeness assumption of the quasi-induced exposure method using a national representative field observation survey

OBJECTIVE

The quasi-induced exposure (QIE) method was developed to estimate relative crash risk exposure. A fundamental assumption often made in applying the QIE method is that not-at-fault drivers in clean two-vehicle crashes (i.e., one and only one driver is at-fault) represent the general exposure of the driving population to crash risk in the absence of the intervention being studied. Our study used direct field observation data to test the representativeness of the assumption for not-at-fault drivers obtained from the General Estimating System (GES) crash data, a national crash database in the United States.

METHODS

Distributions of driver gender, age group, vehicle type, and time-of-crash among the not-at-fault drivers in clean two-vehicle crashes (D2) and the ones in two-or-more-vehicle crashes (i.e., all not-at-fault drivers) from the GES data were compared to the driving population estimated from the National Occupant Protection Use Survey (NOPUS), a national representative field observation survey.

RESULTS

The gender and vehicle-type distributions of D2 and all not-at-fault drivers were not statistically significantly different from the ones in the NOPUS data. Age-group distributions for both not-at-fault driving populations were marginally similar to the ones estimated from NOPUS.

CONCLUSION

By system-wide comparisons on gender, age group, vehicle type, and period, our study suggests that the not-at-fault drivers in crash databases with crashes ranging from no injury to fatal injury reflect the general driving population when the collision occurred. Future study should evaluate the representativeness assumption among other important factors, including roadway type, road geometry, and level of urbanization. Our study supports the credibility of applying the QIE method in traffic safety research using crash databases of all crashes with all severities.

Driver Liability Assessment in Vehicle Collisions in Spain

An accurate estimation of exposure is essential for road collision rate estimation, which is key when evaluating the impact of road safety measures. The quasi-induced exposure method was developed to estimate relative exposure for different driver groups based on its main hypothesis: the not-at-fault drivers involved in two-vehicle collisions are taken as a random sample of driver populations. Liability assignment is thus crucial in this method to identify not-at-fault drivers, but often no liability labels are given in collision records, so unsupervised analysis tools are required. To date, most researchers consider only driver and speed offences in liability assignment, but an open question is if more information could be added. To this end, in this paper, the visual clustering technique of self-organizing maps (SOM) has been applied to better understand the multivariate structure in the data, to find out the most important variables for driver liability, analyzing their influence, and to identify relevant liability patterns. The results show that alcohol/drug use could be influential on liability and further analysis is required for disability and sudden illness. More information has been used, given that a larger proportion of the data was considered. SOM thus appears as a promising tool for liability assessment.

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.

Is driving without a valid license associated with an increased risk of causing a road crash?

The aim of this study was to estimate the association between each cause of driving without a valid license (DWVL) and the risk of causing a road crash, considering driver, vehicle and environmental factors. A case-control study based on data from the Spanish Register of Road Accidents with Victims was carried out between 2014 and 2017. Cases included 28,620 drivers of moving private cars, vans and off-road vehicles involved in single crashes plus 50,100 drivers deemed responsible for clean collisions (i.e. those in which only one driver was labeled as responsible). In accordance with the quasi-induce exposure approach, drivers not responsible for clean collisions comprised the control group (N = 51,656). Logistic and multinomial regression models were used to estimate crude and adjusted Odds Ratios or Relative Risk Ratios between each reason for DWVL and the risk of being a case of all, single and multi-vehicle collisions. A significant association was found between all reasons for DWVL and the risk of causing a road crash. This association was particularly high for drivers with a suspended license and drivers who had never obtained a license. In these subgroups of drivers, the proportion of the relationship explained by high-risk driving behaviors is high. Our results support the need for applying continued strategies to identify and control these subgroups of drivers.

Explaining the Association between Driver's Age and the Risk of Causing a Road Crash through Mediation Analysis

It has been widely reported that younger and older drivers have an excess risk of causing a road crash. Two casual hypotheses may coexist: the riskier driving behaviors and age-related mechanisms in extreme age groups (direct path) and the different environmental and vehicle circumstances (indirect path). Our aim was to quantify, through a mediation analysis, the percentage contribution of both paths. A case-control study was designed from the Spanish Register of Road Crashes with victims from 2014 to 2017. Assuming a quasi-induced exposure approach, controls were non-responsible drivers involved in clean collisions between two or more vehicles (n = 52,131). Responsible drivers for these collisions plus drivers involved in single crashes constituted the case group (n = 82,071). A logit model in which the outcome was the log (odds) of causing a road crash and the exposure was age groups was adjusted for driver, vehicle and environmental factors. The highest crash risk was observed in extreme age groups, compared to the 35-44 year old age group: the youngest (18-24 years old, odds ratio = 2.14, 95% confidence interval: 2.06-2.24) and the oldest drivers (>74 years old, odds ratio = 3.30, 95% confidence interval: 3.04-2.58). The mediation analysis identified the direct path as the main explanatory mechanism for these increases: 89% in the youngest and 93% in the oldest drivers. These data support the hypothesis that the excess crash risk observed for younger and older drivers is mainly related to their higher frequency of risky driving behaviors and age-related loss of capabilities. Preventive strategies in extreme-aged drivers should focus on decreasing these behaviors.

Identifying high-risk commercial vehicle drivers using sociodemographic characteristics

Crash data, from the state of Kentucky, for the 2015-2016 period, show that per capita crash rates and increases in crash-related fatalities were higher than the national average. In an effort to explain why the U.S. Southeast experiences higher crash rates than other regions of the country, previous research has argued the regions unique socioeconomic conditions provide a compelling explanation. Taking this observation as a starting point, this study examines the relationship between highway safety and socioeconomic and demographic characteristics, using an extensive crash dataset from Kentucky. Its focus is single- and two-unit crashes that involve commercial motor vehicles (CMVs) and automobiles. Using binary logistic regression and the quasi-induced exposure technique to analyze data on the socioeconomic and demographic attributes of the zip codes in which drivers reside, factors are identified which can serve as indicators of crash occurrence. Variables such as income, education level, poverty level, employment, age, gender, and rurality of the driver's zip code influence the likelihood of a driver being at fault in a crash. Socioeconomic factors exert a similar influence on CMV and automobile crashes, irrespective of the number of vehicles involved. Research findings can be used to identify groups of drivers most likely to be involved in crashes and develop targeted and efficient safety programs.

Validation of not-at-fault driver representativeness assumption for quasi-induced exposure using U.S. national traffic databases

INTRODUCTION

The quasi-induced exposure (QIE) method has been widely implemented into traffic safety research. One of the key assumptions of QIE method is that not-at-fault drivers represent the driving population at the time of a crash. Recent studies have validated the QIE representative assumption using not-at-fault drivers from three-or-more vehicle crashes (excluding the first not-at-fault drivers; D3_other) as the reference group in single state crash databases. However, it is unclear if the QIE representativeness assumption is valid on a national scale and is a representative sample of driving population in the United States. The aims of this study were to assess the QIE representativeness assumption on a national scale and to evaluate if D3_other could serve as a representative sample of the U.S. driving population.

METHOD

Using the Fatality Analysis Reporting System (FARS) and the National Occupant Protection Use Survey (NOPUS), distributions of driver gender, age, vehicle type, time, and roadway type among the not-at-fault drivers in clean two-vehicle crashes, the first not-at-fault drivers in three-or-more-vehicle crashes, and the remaining not-at-fault drivers in three-or-more vehicle crashes were compared to the driver population observed in NOPUS.

RESULTS

The results showed that with respect to driver gender, vehicle type, time, and roadway type, drivers among D3_other did not show statistical significant difference from NOPUS observations. The age distribution of D3_other driver was not practically different to NOPUS observations.

CONCLUSIONS

Overall, we conclude that D3_other drivers in FARS represents the driving population at the time of the crash. Practical applications: Our study provides a solid foundation for future studies to utilize D3_other as the reference group to validate the QIE representativeness assumption and has potential to increase the generalizability of future FARS studies.

Identify Risk Pattern of E-Bike Riders in China Based on Machine Learning Framework

In this paper, the risk pattern of e-bike riders in China was examined, based on tree-structured machine learning techniques. Three-year crash/violation data were acquired from the Kunshan traffic police department, China. Firstly, high-risk (HR) electric bicycle (e-bike) riders were defined as those with at-fault crash involvement, while others (i.e., non-at-fault or without crash involvement) were considered as non-high-risk (NHR) riders, based on quasi-induced exposure theory. Then, for e-bike riders, their demographics and previous violation-related features were developed based on the crash/violation records. After that, a systematic machine learning (ML) framework was proposed so as to capture the complex risk patterns of those e-bike riders. An ensemble sampling method was selected to deal with the imbalanced datasets. Four tree-structured machine learning methods were compared, and a gradient boost decision tree (GBDT) appeared to be the best. The feature importance and partial dependence were further examined. Interesting findings include the following: (1) tree-structured ML models are able to capture complex risk patterns and interpret them properly; (2) spatial-temporal violation features were found as important indicators of high-risk e-bike riders; and (3) violation behavior features appeared to be more effective than violation punishment-related features, in terms of identifying high-risk e-bike riders. In general, the proposed ML framework is able to identify the complex crash risk pattern of e-bike riders. This paper provides useful insights for policy-makers and traffic practitioners regarding e-bike safety improvement in China.

Drivers with child passengers: distracted but cautious?

OBJECTIVE

A remarkable portion of children's traffic-related deaths occurred when travelling in as passengers in vehicles, but so far, few studies have focused on crash characteristics and crash risks of drivers with child passengers. It has been assumed that drivers with child passengers drive responsibly, but on the contrary, children in vehicles can distract drivers, increasing crash risks. In this study, we examined fatal crash characteristics and fatal crash risks of drivers with child passengers.

METHODS

Fatal crash data from the U.S. Fatality Analysis Reporting System (FARS) for 1996-2015 were used. Only passenger-vehicle drivers aged 23-46 years old were included in the analysis because they represent the typical age of drivers with 0-9-year-old child passengers in the database. Prevalence of crash characteristics and the odds of being at fault were examined for drivers with only child passengers and compared to drivers with only adult passengers, with no passengers and with both adult and child passengers. Analyses were done separately for intersection crashes and non-junction crashes.

RESULTS

Female drivers were involved in twice as many fatal crashes alone with child passengers compared to male drivers. Drivers with only child passengers were more often reported as being inattentive, but for them, risk-taking behaviours were less typical than for drivers without child passengers. Our results showed that these differences were more evident in non-junction crashes than in intersection crashes. When risk-taking behaviours were controlled, both male and female drivers with only child passengers had higher odds of being at fault than drivers with adult passengers (with or without children) in non-junction crashes, but these differences were not significant in intersection crashes.

CONCLUSIONS

Drivers with child passengers represent a specific driver population. They have a higher tendency to engage in distractions while driving, but they have fewer risk-taking behaviour-related fatal crashes compared to drivers with no child passengers. Our results indicate that the effects of child-passenger-related distractions on fatal crash risks are more relevant outside intersections, presumably because drivers may try to self-regulate their interactions with child passengers and focus on driving in more demanding traffic situations.

Driver and road characteristics associated with child pedestrian injuries

BACKGROUND

Child pedestrians make up a significant proportion of all road traffic deaths. Our primary objective was to examine the association of driver characteristics with child pedestrian injuries with a secondary objective to broadly describe the road characteristics surrounding these collisions.

METHODS

We included drivers involved in child (<18 years old) pedestrian motor-vehicle collisions (PMVCs) in Calgary and Edmonton, Alberta (2010-2015). These drivers were compared with not at fault (Alberta adaptation of a Canadian culpability scoring tool) drivers involved in vehicle-only collisions. The data were analyzed with unconditional logistic regression.

RESULTS

Seven hundred ninety-three drivers collided with 826 children. One quarter of child PMVC drivers were 40-54 years old (25.2%). Younger drivers, 16-24 (adjusted odds ratio [aOR] = 1.62, 95% CI: 1.27-2.09), and older drivers, ≥55 (aOR = 1.57, 95% CI: 1.24-1.99) were more likely to be involved in a child PMVC. Time of day between 06:01 - 09:00 (aOR = 1.46, 95% CI: 1.16-1.85) and 18:01 - 24:00 (aOR = 1.68, 95% CI: 1.30-2.17), no seatbelt use (aOR = 2.30, 95% CI: 1.09-4.85), having a child passenger in the vehicle (aOR = 2.15, 95% CI: 1.56-2.96), and impairment including 'had been drinking' (aOR = 7.70, 95% CI: 2.85-20.86) and 'fatigued/asleep/medical defect' (aOR = 27.15, 95% CI: 8.30-88.88) were also associated with being a driver involved in a child PMVC.

CONCLUSIONS

Age, time, impairment and distraction were risk factors for being a driver involved in a child PMVC. Because child PMVC driver characteristics differ from the general driver population, driver-based interventions are a rational additional means of preventing child PMVCs.

Acute sleep deprivation and culpable motor vehicle crash involvement

Study Objectives

To quantify the relationship between acute sleep deprivation and culpable involvement in motor vehicle crashes.

Methods

Participants were 6845 drivers involved in a representative sample of crashes investigated by the US Department of Transportation in years 2005-2007. A modified case-control study design was used to compare self-reported hours of sleep in the 24 hr before crashing between drivers deemed culpable versus nonculpable. Analyses controlled for fatigue-related, driver-related, and environmental factors. Specific errors that led to crashes were also examined.

Results

Drivers who reported having slept for 6, 5, 4, and less than 4 hr in the 24 hr before crashing had 1.3 (95% confidence interval [CI] = 1.04 to 1.7), 1.9 (1.1 to 3.2), 2.9 (1.4 to 6.2), and 15.1 (4.2 to 54.4) times the odds, respectively, of having been culpable for their crashes, compared with drivers who reported 7-9 hr of sleep. Drivers who had slept less than 4 hr had 3.4 (95% CI = 2.1 to 5.6) times the increase in odds of culpable involvement in single-vehicle crashes compared with multiple-vehicle crashes. Recent change in sleep schedule, typically feeling drowsy upon waking, and driving for 3+ hr were also associated with culpability (all p ≤ 0.013). Assuming nonculpable drivers comprised a representative sample of all drivers present where crashes occurred, these odds ratios approximate incidence rate ratios for culpable crash involvement per unit of time driving.

Conclusions

Driving after having slept less than 7 hr in a 24 hr period is associated with elevated risk of culpable crash involvement. Risk is greatest for drivers who have slept less than 4 hr and is manifested disproportionately in single-vehicle 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.

Cyclists injured in traffic crashes in Hong Kong: A call for action

BACKGROUND

Perceived as a minor transportation mode mainly for recreation, cycling and its related safety issues have not been treated as a citywide concern in Hong Kong and have thus received inadequate research efforts. Our study aimed to illuminate the safety challenges faced by cyclists in Hong Kong.

METHODS

We examined the police crash records from 1998 to 2017 and developed a Bayesian Poisson state space model to evaluate the longitudinal change in traffic injuries to cyclists. We then used quasi-induced exposure to measure the annual relative risk of crash involvement for cycling. Based on an officially published travel characteristics survey, we further measured the risk of injury for cycling per minutes cycled.

RESULTS

Between 1998 and 2017, Hong Kong witnessed a more than twofold increase in the number of cyclist injuries, with an average annual increase rate of 5.18% (95% CI: 0.53%-12.77%). By 2017, cyclists were 2.21 (1.82-2.69) times more likely to be involved in traffic crashes than in 1998. Per 10 million minutes, the injury rates for cycling were 28.64 (27.43-29.70) and 42.54 (41.07-44.02) on weekdays during 2001-2003 and 2010-2012. After adjusting for sex and age groups, cyclists were 1.95 (1.43-2.61) times more likely to be injured in 2010-2012 than in 2001-2003. Per minutes traveled, cyclists also sustained significantly higher risks of fatality and injury than pedestrians, private car drivers and passengers, taxi passengers, public bus passengers, and minibus passengers. A comparison of Hong Kong with other regions suggests that Hong Kong is among the most dangerous areas for cycling in terms of fatality rate per minutes cycled.

CONCLUSIONS

Cyclist injuries have become a substantial public health burden in Hong Kong. A range of countermeasures with proven effectiveness should be promptly implemented to improve the safety of these vulnerable road users.

How instantaneous driving behavior contributes to crashes at intersections: Extracting useful information from connected vehicle message data

Connected and automated vehicles have enabled researchers to use big data for development of new metrics that can enhance transportation safety. Emergence of such a big data coupled with computational power of modern computers have enabled us to obtain deeper understanding of instantaneous driving behavior by applying the concept of "driving volatility" to quantify variations in driving behavior. This paper brings in a methodology to quantify variations in vehicular movements utilizing longitudinal and lateral volatilities and proactively studies the impact of instantaneous driving behavior on type of crashes at intersections. More than 125 million Basic Safety Message data transmitted between more than 2800 connected vehicles were analyzed and integrated with historical crash and road inventory data at 167 intersections in Ann Arbor, Michigan, USA. Given that driving volatility represents the vehicular movement and control, it is expected that erratic longitudinal/lateral movements increase the risk of crash. In order to capture variations in vehicle control and movement, we quantified and used 30 measures of driving volatility by using speed, longitudinal and lateral acceleration, and yaw-rate. Rigorous statistical models including fixed parameter, random parameter, and geographically weighted Poisson regressions were developed. The results revealed that controlling for intersection geometry and traffic exposure, and accounting unobserved factors, variations in longitudinal control of the vehicle (longitudinal volatility) are highly correlated with the frequency of rear-end crashes. Intersections with high variations in longitudinal movement are prone to have higher rear-end crash rate. Referring to sideswipe and angle crashes, along with speed and longitudinal volatility, lateral volatility is substantially correlated with the frequency of crashes. When it comes to head-on crashes, speed, longitudinal and lateral acceleration volatilities are highly associated with the frequency of crashes. Intersections with high lateral volatility have higher risk of head-on collisions due to the risk of deviation from the centerline leading to head-on crash. The developed methodology and volatility measures can be used to proactively identify hotspot intersections where the frequency of crashes is low, but the longitudinal/lateral driving volatility is high. The reason that drivers exhibit higher levels of driving volatility when passing these intersections can be analyzed to come up with potential countermeasures that could reduce volatility and, consequently, crash risk.

Adaptation of a Canadian culpability scoring tool to Alberta police traffic collision report data

Objective: The objective of this study was to adapt a previously validated Canadian Culpability Scoring Tool (CCST) to Alberta police report data. Methods: Police traffic collision reports from motor vehicle (MV) collisions in Calgary and Edmonton (Alberta, Canada) from 2010 to 2014 were used. Adaptation of the CCST was completed with input from personnel within Alberta Transportation, contributing to face and content validity. Two research assistants, given only the information necessary for scoring, evaluated 175 randomly selected MV-MV collisions. Interrater agreement was estimated using kappa (k) and reported with 95% confidence intervals (CIs). Discussion of disagreements between the research assistants and consultation from Alberta Transportation informed the algorithm used in the Alberta Motor Vehicle Collision Culpability Tool (AMVCCT). The AMVCCT was automated and applied to all motorists involved in collisions. Binary logistic regression was used to examine characteristics of the culpable and nonculpable drivers and their effects were reported using odds ratios (ORs) with 95% CIs. Results: Interrater agreement for the random sample was excellent (k = 0.95; 95% CI, 0.92-0.99). Of those drivers hospitalized, 1,130 (37.54%) were rated not culpable and 1,880 (62.46%) were rated culpable. The odds of being culpable were higher for males than for females (OR = 1.43; 95% CI, 1.23-1.66). The odds of being culpable were higher in those impaired by alcohol than those considered "apparently normal" (OR = 61.10; 95% CI, 22.66-164.75). The odds of being deemed culpable, when compared with drivers >54 years old, were higher for those <25 years old (OR = 1.72; 95% CI, 1.35-2.20) and lower for those in the 40- to 54-year-old age group (OR = 0.78; 95% CI, 0.63-0.96). Driving between 12 a.m. and 6 a.m. resulted in higher odds of being culpable compare with all other 6-h time blocks. Direction and statistical significance remained consistent when applying the tool to all MV collisions. Sensitivity analysis including the removal of single vehicle collisions did not affect the direction or statistical significance of the main results. Conclusions: The AMVCCT identified a culpable group that exhibited characteristics expected in drivers who are at fault in collisions. The age groups 25-39 and 40-54 demonstrated different results than the CCST. However, this is the only difference that exists in the findings of the AMVCCT compared to the CCST and could exist due to differences between the driving populations in Alberta and British Columbia. It is possible to adapt the CCST to provinces outside British Columbia and, in doing so, we can identify risk factors for collision contribution and not-at-fault drivers who represent the driving population.

Does crash risk increase when emergency vehicles are driving with lights and sirens?

INTRODUCTION

Emergency vehicles, such as police, ambulances, and fire vehicles, need to arrive at the scene of emergencies as quickly as possible, and thus they often travel in emergency mode - using their lights and sirens and often bypassing traffic signals. We examined whether travelling in emergency mode increased crash risk among police, ambulance and fire vehicles.

METHODS

We conducted a quasi-induced exposure analysis using data from the Iowa Crash Database for the period of 2005 through 2013. The data are maintained by the Iowa Department of Transportation (IADOT), Office of Driver Services (ODS) and includes all investigating police officer's reports of motor vehicle crashes. The quasi-induced exposure method is an approach to calculate crash risk in the absence of exposure data using vehicles without a contributing cause (did not contribute to the crash) as a proxy for the baseline driving population.

RESULTS

From 2005 - 2013, police vehicles were involved in 2406 crashes and ambulances and fire vehicles were involved in 528 crashes. Police vehicles were 1.8 times more likely to crash while driving in emergency mode than usual mode; this was a statistically significant increase. Ambulance and fire vehicles were not more likely to crash in emergency mode compared with usual mode. For police, other factors that contributed to crash risk included gender, age, icy/snowy roads, unpaved roads, and intersections. For ambulances and fire vehicles, other factors that contributed to crash risk included gender, age, weekends, icy/snowy roads and urban locations.

CONCLUSION

Crash risk increased when police vehicles drove with lights and sirens but did not increase for ambulance and fire vehicles. Further research is necessary to develop and evaluate strategies to mitigate crash risk among police vehicles. Cultural approaches which prioritize transportation safety in conjunction with reaching the scene as quickly as possible may be warranted.

Young females at risk while driving with a small child

INTRODUCTION

Previous research suggests that young mothers with little driving experience are at risk when driving with a small child passenger. In this study we examined the prevalence, characteristics and risk of fatal motor vehicle crashes involving an infant passenger under the age of one among female drivers of different ages.

METHODS

We used crash data from the US Fatality Analysis Reporting System for 1994-2013. The prevalence of fatal crashes involving infants was examined by age of female drivers and compared to the number of births among mothers of a similar age. The essential characteristics of the crashes were described, and the odds of being at fault were determined for young (16-24-year-olds) and older female drivers (25-39-year-olds) with an infant passenger or with no passengers.

RESULTS

The prevalence of fatal crashes involving infant passengers was higher among young female drivers in relation to the number of births among mothers of a similar age than among older females. Young female drivers with an infant passenger were more often at fault than older drivers (aOR=1.83, 95%, CI=1.52, 2.20). Their vehicles were older and smaller and they used proper safety seats for infants less often than the older drivers. In addition, young female drivers with an infant passenger but with no adult passenger in the vehicle were more often at fault than young female drivers with no passengers (aOR=1.27, 95% CI=1.06, 1.51). Both young and older female drivers' crashes involving an infant passenger typically occurred in ordinary driving conditions, but these drivers with infant passengers were more often reported as having fallen asleep or inattentive than those with no passengers. The presence of an adult passenger in addition to an infant passenger lowered female drivers' odds of being at fault, regardless of the driver's age.

CONCLUSIONS

Young females driving with an infant passenger, probably most often mothers, are at an elevated risk of a fatal crash, especially when they drive alone with an infant. The protective effect of an adult passenger suggests that another adult in the vehicle can assist the driver by taking care of the infant and enabling the driver to focus on driving.

Estimating young novice drivers' compliance with graduated driver licensing restrictions: A novel approach

OBJECTIVES

Current methods of estimating compliance with graduated driver licensing (GDL) restrictions among young drivers with intermediate driver's licenses-which include surveys, direct observations, and naturalistic studies-cannot sufficiently answer many critical foundational questions: What is the extent of noncompliance among the population of young intermediate drivers? How does compliance change over the course of licensure? How does compliance differ by driver subgroup and in certain driving environments? This article proposes an alternative and complementary approach to estimating population-level compliance with GDL nighttime and passenger restrictions via application of the quasi-induced exposure (QIE) method.

METHODS

The article summarizes the main limitations of previous methods employed to estimate compliance. It then introduces the proposed method of borrowing the fundamental assumption of the QIE method-that young intermediate drivers who are nonresponsible in clean (i.e., one and only one responsible driver) multivehicle crashes are reasonably representative of young intermediate drivers on the road-to estimate population-based compliance. I describe formative work that has been done to ensure this method can be validly applied among young intermediate drivers and provide a practical application of this method: an estimate of compliance with New Jersey's passenger restrictions among 8,006 nonresponsible 17- to 20-year-old intermediate drivers involved in clean 2-vehicle crashes from July 2010 through June 2012.

RESULTS

Over the study period, an estimated 8.4% (95% confidence interval, 7.8%, 9.0%) of intermediate drivers' trips were not in compliance with New Jersey's GDL passenger restriction. These findings were remarkably similar to previous estimates from more resource-intensive naturalistic studies (Goodwin et al. 2006 ; Klauer et al. 2011 ).

CONCLUSION

Studies can practically apply proposed methods to estimate population-level compliance with GDL passenger and night restrictions; examine how compliance varies by relevant driver, vehicle, and environmental factors; and evaluate the implementation of a GDL provision or other intervention aimed at increasing compliance with these restrictions. Important considerations and potential limitations and challenges are discussed.

Compliance With and Enforcement of Graduated Driver Licensing Restrictions

INTRODUCTION

Graduated Driver Licensing (GDL) is the most effective strategy to reduce the burden of young driver crashes, but the extent to which young intermediate (newly licensed) drivers comply with, and police enforce, important GDL passenger and night-time restrictions is largely unknown. Population-level rates of intermediate drivers' compliance were estimated as well as police enforcement among crash-involved drivers who were noncompliant.

METHODS

New Jersey's statewide driver licensing and crash databases were individually linked. The quasi-induced exposure method's fundamental assumption-that nonresponsible young intermediate drivers in clean (i.e., only one responsible driver) multivehicle crashes are reasonably representative of young intermediate drivers on the road-was borrowed. Incidence was then estimated among the 9,250 nonresponsible intermediate drivers who were involved in clean multivehicle crashes from July 2010 through June 2012. The proportion of crash-involved noncompliant intermediate drivers who were issued a GDL citation, by crash responsibility, was calculated. Data were collected in 2013 and analyzed in 2015.

RESULTS

Overall, 8.3% (95% CI=7.8%, 8.9%) of intermediate drivers' trips were noncompliant with New Jersey's passenger restriction and 3.1% (95% CI=2.8%, 3.5%) with its night-time restriction; compliance was significantly lower among those residing in low-income and urban areas, among male drivers, on weekends, and in summer months. The proportion of crash-involved noncompliant intermediate drivers who were issued a GDL citation was low (nonresponsible drivers, 10.3%; responsible drivers, 19.0%).

CONCLUSIONS

The vast majority of intermediate driver trips are in compliance with GDL restrictions. Outreach activities should consider focusing on higher-risk situations and groups with higher noncompliance rates.

Impact of risk attitudes and perception on game theoretic driving interactions and safety

This study employs game theory to investigate behavioural norms of interaction between drivers at a signalised intersection. The choice framework incorporates drivers' risk perception as well as their risk attitudes. A laboratory experiment is conducted to study the impact of risk attitudes and perception in crossing behaviour at a signalised intersection. The laboratory experiment uses methods from experimental economics to induce incentives and study revealed behaviour. Conflicting drivers are considered to have symmetric disincentives for crashing, to represent a no-fault car insurance environment. The study is novel as it uses experimental data collection methods to investigate perceived risk. Further, it directly integrates perceived risk of crashing with other active drivers into the modelling structure. A theoretical model of intersection crossing behaviour is also developed in this paper. This study shows that right-of-way entitlements assigned without authoritative penalties to at-fault drivers may still improve perceptions of safety. Further, risk aversion amongst drivers attributes to manoeuvring strategies at or below Nash mixed strategy equilibrium. These findings offer a theoretical explanation for interactive manoeuvres that lead to crashes, as opposed to purely statistical methods which provide correlation but not necessarily explanation.

The epidemiologic principles underlying traffic safety study designs

This article describes the epidemiological principles underlying four observational study designs commonly used to assess traffic safety: the case-control, case-crossover, culpability and quasi-induced exposure designs. We focus in particular on the specific challenges for preventing bias using each design. Whereas recruiting controls representative of the source population poses a special challenge in case-control traffic safety studies, case-crossover designs are prone to recall bias, and culpability and quasi-induced exposure studies can be undermined by difficulties assigning crash responsibility. Using causal diagrams and worked examples, we provide a simple way to teach traffic safety designs to epidemiologists and to encourage proper application of epidemiological principles among researchers designing traffic safety studies.

Validation of quasi-induced exposure representativeness assumption among young drivers

OBJECTIVE

Young driver studies have applied quasi-induced exposure (QIE) methods to assess relationships between demographic and behavioral factors and at-fault crash involvement, but QIE's primary assumption of representativeness has not yet been validated among young drivers. Determining whether nonresponsible young drivers in clean (i.e., only one driver is responsible) 2-vehicle crashes are reasonably representative of the general young driving population is an important step toward ensuring valid QIE use in young driver studies. We applied previously established validation methods to conduct the first study, to our knowledge, focused on validating the QIE representativeness assumption in a young driver population.

METHODS

We utilized New Jersey's state crash and licensing databases (2008-2012) to examine the representativeness assumption among 17- to 20-year-old nonresponsible drivers involved in clean multivehicle crashes. It has been hypothesized that if not-at-fault drivers in clean 2-vehicle crashes are a true representation of the driving population, it would be expected that nonresponsible drivers in clean 3-or-more-vehicle crashes also represent this same driving population (Jiang and Lyles 2010 ). Thus, we compared distributions of age, gender, and vehicle type among (1) nonresponsible young drivers in clean 2-vehicle crashes and (2) the first nonresponsible young driver in clean crashes involving 3 or more vehicles to (3) all other nonresponsible young drivers in clean crashes involving 3 or more vehicles. Distributions were compared using chi-square tests and conditional logistic regression; analyses were conducted for all young drivers and stratified by license status (intermediate vs. fully licensed drivers), crash location, and time of day of the crash.

RESULTS

There were 41,323 nonresponsible drivers in clean 2-vehicle crashes and 6,464 nonresponsible drivers in clean 3-or-more-vehicle crashes. Overall, we found that the distributions of age, gender, and vehicle type were not statistically significantly different between the 3 groups; in each group, approximately one fourth of drivers were represented in each age from age 17 through 20, half were males, and approximately 80% were driving a car/station wagon/minivan. In general, conclusions held when we evaluated the assumption within intermediate and fully licensed young drivers separately and by crash location and time.

CONCLUSIONS

It appears that the representativeness assumption holds among the population of young NJ drivers. We encourage young driver studies utilizing QIE methods to conduct internal validation studies to ensure appropriate application of these methods and we propose utilization of QIE methods to address broader foundational and applied questions in young driver safety.

Temporal patterns of deer-vehicle collisions consistent with deer activity pattern and density increase but not general accident risk

The increasing number of deer-vehicle collisions (DVCs) across Europe during recent decades poses a serious threat to human health and animal welfare and increasing costs for society. DVCs are triggered by both a human-related and a deer-related component. Mitigation requires an understanding of the processes driving temporal and spatial collision patterns. Separating human-related from deer-related processes is important for identifying potentially effective countermeasures, but this has rarely been done. We analysed two time series of 341,655 DVCs involving roe deer and 854,659 non-deer-related accidents (non-DVCs) documented between 2002 and 2011. Nonparametric smoothing and temporal parametric modelling were used to estimate annual, seasonal, weekly and diurnal patterns in DVCs, non-DVCs and adjusted DVCs. As we had access to data on both DVCs and non-DVCs, we were able to disentangle the relative role of human-related and deer-related processes contributing to the overall temporal DVC pattern. We found clear evidence that variation in DVCs was mostly driven by deer-related and not human-related activity on annual, seasonal, weekly and diurnal scales. A very clear crepuscular activity pattern with high activity after sunset and around sunrise throughout the year was identified. Early spring and the mating season between mid-July and mid-August are typically periods of high roe deer activity, and as expected we found a high number of DVC during these periods, although these patterns differed tremendously during different phases of a day. The role of human activity was mainly reflected in fewer DVCs on weekends than on weekdays. Over the ten-year study period, we estimated that DVCs increased by 25%, whereas the number of non-DVCs decreased by 10%. Increasing deer densities are the most likely driver behind this rise in DVCs. Precise estimates of DVC patterns and their relationship to deer and human activity patterns allow implementation of specific mitigation measures, such as tailored driver warning systems or temporary speed limits. To prevent a further increase in DVCs, state-wide measures to decrease roe deer density are required.

Contribution of exposure, risk of crash and fatality to explain age- and sex-related differences in traffic-related cyclist mortality rates

This study was designed to quantify the percent contribution of exposure, risk of collision and fatality rate to the association of age and sex with the mortality rates among cyclists in Spain, and to track the changes in these contributions with time. Data were analyzed for 50,042 cyclists involved in road crashes in Spain from 1993 to 2011, and also for a subset of 13,119 non-infractor cyclists involved in collisions with a vehicle whose driver committed an infraction (used as a proxy sample of all cyclists on the road). We used decomposition and quasi-induced exposure methods to obtain the percent contributions of these three components to the mortality rate ratios for each age and sex group compared to males aged 25-34 years. Death rates increased with age, and the main component of this increase was fatality (around 70%). Among younger cyclists, however, the main component of increased death rates was risk of a collision. Males had higher death rates than females in every age group: this rate increased from 6.4 in the 5-14 year old group to 18.8 in the 65-79 year old group. Exposure, the main component of this increase, ranged between 70% and 90% in all age categories, although the fatality component also contributed to this increase. The contributions of exposure, risk of crash and fatality to cyclist death rates were strongly associated with age and sex. Young male cyclists were a high-risk group because all three components tended to increase their mortality rate.

Modelling crash propensity of carshare members

Carshare systems are considered a promising solution for sustainable development of cities. To promote carsharing it is imperative to make them cost effective, which includes reduction in costs associated to crashes and insurance. To achieve this goal, it is important to characterize carshare users involved in crashes and understand factors that can explain at-fault and not-at fault drivers. This study utilizes data from GoGet carshare users in Sydney, Australia. Based on this study it was found that carshare users who utilize cars less frequently, own one or more cars, have less number of accidents in the past ten years, have chosen a higher insurance excess and have had a license for a longer period of time are less likely to be involved in a crash. However, if a crash occurs, carshare users not needing a car on the weekend, driving less than 1000km in the last year, rarely using a car and having an Australian license increases the likelihood to be at-fault. Since the dataset contained information about all members as well as not-at-fault drivers, it provided a unique opportunity to explore some aspects of quasi-induced exposure. The results indicate systematic differences in the distribution between the not-at-fault drivers and the carshare members based on the kilometres driven last year, main mode of travel, car ownership status and how often the car is needed. Finally, based on this study it is recommended that creating an incentive structure based on training and experience (based on kilometres driven), possibly tagged to the insurance excess could improve safety, and reduce costs associated to crashes for carshare systems.

Estimating the over-involvement of suspended, revoked, and unlicensed drivers as at-fault drivers in California fatal crashes

INTRODUCTION AND METHOD

Quasi-induced exposure analysis was used to estimate annual fatal crash involvement rates for validly licensed, suspended or revoked (S/R), and unlicensed drivers in California from 1987 through 2009 using fatal crash data obtained from the National Highway Traffic Safety Administration's Fatality Analysis Reporting System and crash culpability determinations from the California Highway Patrol's Statewide Integrated Traffic Records System.

RESULTS

Although there was some year-to-year fluctuation in the annual estimates, S/R and unlicensed drivers were over-involved as at-fault drivers in fatal crashes during every year of the 23-year time period relative to validly licensed drivers. The fatal crash involvement ratios combined across all years were 0.86 for validly licensed drivers, 2.23 for S/R drivers, and 2.34 for unlicensed drivers. Hence, compared to validly licensed drivers, the odds of being at-fault for a fatal crash were 160% higher for S/R drivers (involvement ratio=2.60) and 173% higher for unlicensed drivers (involvement ratio=2.73). The excess risks of S/R and unlicensed drivers are somewhat lower than estimates found in a prior study using the same technique, but the results nonetheless provide evidence that S/R and unlicensed drivers are much more hazardous on the road than are validly licensed drivers and emphasize the importance of using strong countermeasures-including vehicle impoundment-to reduce their high crash risk. These findings support interventions to help reduce driving among S/R and unlicensed drivers.

A comprehensive review on the quasi-induced exposure technique

OBJECTIVE

The goal is to comprehensively examine the state-of-the-art applications and methodological development of quasi-induced exposure and consequently pinpoint the future research directions in terms of implementation guidelines, limitations, and validity tests.

METHODS

The paper conducts a comprehensive review on approximately 45 published papers relevant to quasi-induced exposure regarding four key topics of interest: applications, responsibility assignment, validation of assumptions, and methodological development.

RESULTS

Specific findings include that: (1) there is no systematic data screening procedure in place and how the eliminated crash data will impact the responsibility assignment is generally unknown; (2) there is a lack of necessary efforts to assess the validity of assumptions prior to its application and the validation efforts are mostly restricted to the aggregated levels due to the limited availability of exposure truth; and (3) there is a deficiency of quantitative analyses to evaluate the magnitude and directions of bias as a result of injury risks and crash avoidance ability.

CONCLUSIONS

The paper points out the future research directions and insights in terms of the validity tests and implementation guidelines.

Association of cyclists' age and sex with risk of involvement in a crash before and after adjustment for cycling exposure

This study aimed to estimate the association of cyclists' age and sex with the risk of being involved in a crash with and without adjustment for their amount of exposure. We used the distribution of the entire population and cyclists (total and non-responsible) involved in road crashes in Spain between 1993 and 2009 held by the Spanish National Institute of Statistics and the Spanish General Traffic Directorate to calculate rates of exposure and involvement in a crash. Males aged 45-49 years were used as the reference category to obtain exposure rate ratios (ERR) and unadjusted crash rate ratios (URR). We then used these values in decomposition analysis to calculate crash rate ratios adjusted for exposure (ARR). The pattern of ARR was substantially different from URR. In both sexes the highest values were observed in the youngest age groups, and the values decreased as age increased except for a slight increase in the oldest age groups. In males, a slight increase in the lowest and highest age categories was observed for crashes resulting in severe injury or death, and a decrease was observed for the youngest cyclists who were wearing a helmet. The large differences between age and sex groups in the risk of involvement in a cycling crash are strongly dependent on differences in their exposure rates. Taking exposure rates into account, cyclists younger than 30 years and older than 65 years of age had the highest risk of being involved in a crash.

Effect of passenger presence on older drivers' risk of fatal crash involvement

OBJECTIVE

To determine the association between passenger presence and risk of fatal crash involvement in relation to driver and passenger age and gender, focusing especially on drivers ages 65 and older.

METHODS

Data on US fatal crashes were obtained for 2002-2009. Using the quasi-induced exposure methodology, logistic regression analysis was used to predict the odds of fatal crash involvement as a function of driver age and gender as well as passenger age and gender.

RESULTS

Overall, risk of fatal crash involvement with passengers was 43 percent lower for drivers ages 65-74 and 38 percent lower for drivers 75 and older. Older drivers' risk of fatal crash involvement was lower with almost all combinations of passenger age and gender; there was no reduction in risk with passengers ages 75 and older. Effects were stronger at nonintersection locations than at intersection locations.

CONCLUSION

Older drivers' crash risk is lower with almost every combination of passenger age group and gender. It is unclear whether the presence of passengers lowers older driver crash risk or whether safer drivers tend to ride with passengers.

Risk of road accident associated with the use of drugs: a systematic review and meta-analysis of evidence from epidemiological studies

This paper is a corrigendum to a previously published paper where errors were detected. The errors have been corrected in this paper. The paper is otherwise identical to the previously published paper. A systematic review and meta-analysis of studies that have assessed the risk of accident associated with the use of drugs when driving is presented. The meta-analysis included 66 studies containing a total of 264 estimates of the effects on accident risk of using illicit or prescribed drugs when driving. Summary estimates of the odds ratio of accident involvement are presented for amphetamines, analgesics, anti-asthmatics, anti-depressives, anti-histamines, benzodiazepines, cannabis, cocaine, opiates, penicillin and zopiclone (a sleeping pill). For most of the drugs, small or moderate increases in accident risk associated with the use of the drugs were found. Information about whether the drugs were actually used while driving and about the doses used was often imprecise. Most studies that have evaluated the presence of a dose-response relationship between the dose of drugs taken and the effects on accident risk confirm the existence of a dose-response relationship. Use of drugs while driving tends to have a larger effect on the risk of fatal and serious injury accidents than on the risk of less serious accidents (usually property-damage-only accidents). The quality of the studies that have assessed risk varied greatly. There was a tendency for the estimated effects of drug use on accident risk to be smaller in well-controlled studies than in poorly controlled studies. Evidence of publication bias was found for some drugs. The associations found cannot be interpreted as causal relationships, principally because most studies do not control very well for potentially confounding factors.

Risk factors for causing road crashes involving cyclists: An application of a quasi-induced exposure method

A quasi-induced exposure approach was applied to the Spanish Register of Traffic Crashes to identify driver- and vehicle-related factors associated with the risk of causing a road crash involving a cyclist in Spain from 1993 to 2009. We analyzed 19,007 collisions between a bicycle and another vehicle in which only one of the drivers committed an infraction, and 13,540 records that included the group of non-infractor cyclists in the above collisions plus cyclists involved in single-bicycle crashes. Adjusted odds ratios were calculated for being responsible for each type of crash for each factor considered. Age from 10 to 19 years, male sex, alcohol or drug consumption and non-helmet use were cyclist-related variables associated with a higher risk of crash, whereas cycling more than 1h increased only the risk of single crashes. Bicycles with brake defects and ridden by two occupants were also at higher risk of involvement in a crash, whereas light defects were associated only with collisions with another vehicle. For drivers of the other vehicle, age more than 60 years, alcohol, not using safety devices and nonprofessional drivers were at higher risk. The risk of colliding with a bicycle was higher for mopeds than for passenger cars.

Risk factors for injury accidents among moped and motorcycle riders

OBJECTIVE

To study and quantify the effect of factors related to the riders of powered two-wheelers on the risk of injury accident involvement.

METHODOLOGY

Based on national data held by the police from 1996 to 2005, we conducted a case-control study with responsibility for the accident as the event of interest. We estimated the odds ratios for accident responsibility. Making the hypothesis that the non-responsible riders in the study are representative of all the riders on the road, we thus identified risk factors for being responsible for injury accidents. The studied factors are age, gender, helmet wearing, alcohol consumption, validity of the subject's driving licence and for how long it has been held, the trip purpose and the presence of a passenger on the vehicle. Moped and motorcycle riders are analyzed separately, adjusting for the main characteristics of the accident.

RESULTS

For both moped and motorcycle riders, being male, not wearing a helmet, exceeding the legal limit for alcohol and travelling for leisure purposes increased the risk of accident involvement. The youngest and oldest users had a greater risk of accident involvement. The largest risk factor was alcohol, and we identified a dose-effect relationship between alcohol consumption and accident risk, with an estimated odds ratio of over 10 for motorcycle and moped riders with a BAC of 2 g/l or over. Among motorcycle users, riders without a licence had twice the risk of being involved in an accident than those holding a valid licence. However, the number of years the rider had held a licence reduced the risk of accident involvement. One difference between moped and motorcycle riders involved the presence of a passenger on the vehicle: while carrying a passenger increased the risk of being responsible for the accident among moped riders, it protected against this risk among motorcycle riders.

CONCLUSION

This analysis of responsibility has identified the major factors contributing to excess risk of injury accidents, some of which could be targeted by prevention programmes.

Comparison of two methods to assess the effect of age and sex on the risk of car crashes

BACKGROUND

This study was designed to compare two methods (direct measurement of exposure and quasi-induced exposure) for assessing the effect of age and sex on the risk of being involved in a car crash in Spain.

METHODS

Spanish crash rates (per 10,000,000 driver-km) for age and sex groups of drivers aged 18-64 years old were obtained for 2004-2007, using information from the Spanish General Traffic Office (census of reported car crashes) and the Spanish Household Survey on Alcohol and Drugs (estimate of the mean km driven for each car driver). The rate ratios estimated by direct exposure estimates were compared to those obtained with the quasi-induced exposure method, which compares the age and sex of responsible and non-responsible drivers involved in the same clean collision (in which only one of the drivers committed a driving infraction).

RESULTS

Both methods detected an increased risk of involvement in a crash for the youngest (18-20 years) and the oldest drivers (60-64 years), compared to middle-aged drivers (45-49 years). However, the rate ratios obtained with the quasi-induced method for the youngest group (2.0 for men, 1.6 for women) were much lower than those obtained with crash rates (13.4 for men, 5.7 for women). Both methods detected a similar increase in the risk of involvement of male drivers compared to women in the youngest age group. This excess risk for men was maintained with increasing age up to 45-49 years when the quasi-induced method was used. However, direct comparisons of crash rates revealed an increased risk of involvement in women compared to men of the same age from 25-29 years onward.

CONCLUSIONS

Both direct measurement of driving exposure and the quasi-induced exposure method detected some well-known patterns of risk associated with driver's age and sex. However, factors that could explain important differences between the two methods deserve attention, especially those related with the excess risk for the youngest drivers as well as sex-related risk.

A review of the validity of the underlying assumptions of quasi-induced exposure

In recent years, the quasi-induced exposure technique has been widely implemented in a variety of traffic safety-related settings. One of the primary concerns associated with the applications of quasi-induced exposure is that the underlying assumptions are not explicitly verified or validated before the exposure measurement is adopted. Of principal interest is the assumption that the non-responsible driver/vehicles in two-vehicle crashes are representative of the general driving population on the road at the time of crash occurrence. The objective here is to provide an alternative to test the not-at-fault assumptions with the use of three-or-more-vehicle crashes, which are readily available in many crash databases. With the use of Michigan and Maine crash data as examples, the examination of the validity is developed at two levels: (1) at all locations where crashes took place; (2) at locations where three-or-more-vehicle crashes are prone to occur. Non-responsible drivers are disaggregated by three basic driver-vehicle characteristics (age, gender, and vehicle type) and compared at these two levels for statistical and operational (practical) differences. Examination of the results demonstrates that all of the examined non-responsible driver distributions are consistently similar from both operational and statistical points of view. Compared to other approaches to validation, such as using "exposure truth" (e.g., actual vehicle miles traveled or a trip diary), the proposed validation is much more simplistic, straightforward, and cost-effective.

Older drivers' road traffic crashes in the UK

A sample of over 2000 crashes involving drivers aged 60 years or over was considered, from three UK midland police forces, from the years 1994-2007 inclusive. Each case was summarized on a database including the main objective features (such as time and place), a summary narrative, a sketch plan and a list of explanatory factors. The main findings were that older drivers have significant problems with intersection collisions and failing to give right of way; these formed the largest single class of crashes in the sample. Possible behavioural explanations for this will be discussed, along with other findings regarding older driver blameworthiness, fatigue and illness, time of day factors, and 'unintended accelerations'.

The efficiency of using non-culpable crash-claim involvements from insurance data as a means of estimating travel exposure for road user sub-groups

INTRODUCTION

Induced exposure has a long history of development and usage in traffic safety research but a major question has always concerned the extent to which the accumulation of culpable and non-culpable involvements can be considered independent.

METHOD

Culpability assessments of 32,630 vehicles' crash-claim involvements adjudicated by insurance adjusters were matched with vehicle odometer readings taken at emission testing using consistent identification of vehicles and principal operators over a 5-year period.

RESULT

It was found that the accumulation of culpable crash involvements was not entirely independent of that for non-culpable involvements. However, the rate of non-culpable involvements was determined to be an acceptable surrogate for travel exposure rate where sample sizes were large.

DISCUSSION

The relationship between the rate of non-culpable involvements and the rate of travel exposure for data subsets when both were normalized by the overall sample rates was reminiscent of an accident-volume curve for roadway locations in traffic engineering theory. This suggested that only a portion of non-culpable involvements actually related directly to travel and this lead to a correction factor that could be applied.

IMPACT ON INDUSTRY

While lack of independence of involvement rates may be problematic for a direct risk ratio application, it does not invalidate the use of non-culpable involvements to predict travel. For insurers that have a need to estimate travel amounts for different driver/vehicle groups as part of the insurance rating purposes, this can be a useful application.

Killer crashes: fatal road traffic accidents in the UK

Road traffic accidents are responsible for over 3000 deaths per year in the UK, according to Department for Transport (2004a) figures. Although progress is being made in a number of areas, vehicle occupant fatalities have not been falling in line with casualty reduction targets for the year 2010. A sample of 1185 fatal vehicle occupant cases was considered, from ten UK police forces, from the years 1994-2005 inclusive. The main findings were: (1) over 65% of the accidents examined involved driving at excessive speed, a driver in excess of the legal alcohol limit, or the failure to wear a seat belt by a fatality, or some combination of these. (2) Young drivers have the great majority of their accidents by losing control on bends or curves, typically at night in rural areas and/or while driving for 'leisure' purposes. These accidents show high levels of speeding, alcohol involvement and recklessness. (3) Older drivers had fewer accidents, but those fatalities they were involved in tended to involve misjudgement and perceptual errors in 'right of way' collisions, typically in the daytime on rural rather than urban roads. Blameworthy right of way errors were notably high for drivers aged over 65 years, as a proportion of total fatal accidents in that age group.