Evaluation of left-turn lane offset using the naturalistic driving study data

IntroductionThe Strategic Highway Research Program 2 (SHRP 2) Naturalistic Driving Study (NDS) data were used to evaluate gap acceptance behavior of drivers at left-turn lanes with negative, zero, or positive offsets ranging from − 29 ft to + 6 ft. The objectives of the study were to develop guidance for the design of offset left-turn lanes used as a safety countermeasure, and to provide insight regarding the use of the NDS data to future users.MethodThe study included 3350 gaps in opposing traffic evaluated by 145 NDS volunteer drivers and 275 non-NDS drivers at 14 two-way stop-controlled intersections and 44 signalized opposing left-turn pairs. Logistic regression was used to model the critical gap length for drivers as a function of offset, under conditions when their view was either blocked or not by an opposing left-turning driver.ResultsThe analysis found that the critical gap was longer at left-turn lanes with negative offsets than at those with zero or positive offsets, and was also longer when sight distance was blocked by an opposing left-turning vehicle. Sight distance was much more likely to be restricted by an opposing left-turning vehicle at negative-offset and drivers at those intersections were less likely to accept a gap when an opposing left-turn driver was present.ConclusionsLonger gap lengths could potentially result in decreased operational efficiency of an intersection. In addition, drivers making left-turns at negative-offset left-turn lanes are, on average, more likely to leave the shortest amount of time between their turn and the arrival of the next opposing through-vehicle, which may present a potential safety concern.Practical applicationsThe findings provide guidance for highway designers considering using offset left-turn lanes as a crash countermeasure. This research also highlights the benefits and limitations of using the SHRP 2 NDS data to answer similar research questions.     

Safety analysis of urban signalized intersections under mixed traffic

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

Drivers’ visual scanning behavior at signalized and unsignalized intersections: A naturalistic driving study in China

Introduction: Intersections are the most dangerous locations in urban traffic. The present study aims to investigate drivers’ visual scanning behavior at signalized and unsignalized intersections. Method: Naturalistic driving data at 318 green phase signalized intersections and 300 unsignalized ones were collected. Drivers’ glance allocations were manually categorized into 10 areas of interest (AOIs), based on which three feature subsets were extracted including glance allocation frequencies, durations and AOI transition probabilities. The extracted features at signalized and unsignalized intersections were compared. Features with statistical significances were integrated to characterize drivers’ scanning patterns using the hierarchical clustering method. Andrews Curve was adopted to visually illustrate the clustering results of high-dimensional data. Results: Results showed that drivers going straight across signalized intersections had more often glances at the left view mirror and longer fixation on the near left area. When turning left, drivers near signalized intersections had more frequent glances at the left view mirror, fixated much longer on the forward and rearview mirror area, and had higher transition probabilities from near left to far left. Compared with drivers’ scanning patterns in left turning maneuver at signalized intersections, drivers with higher situation awareness levels would divide more attention to the forward and right areas than at unsignalized intersections. Conclusions: This study revealed that intersection types made differences on drivers’ scanning behavior. Practical applications: These findings suggest that future applications in advanced driver assistance systems and driver training programs should recommend different scanning strategies to drivers at different types of intersections.     

Analyses of rear-end crashes based on classification tree models

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

Whether conversion and weather matter to roundabout safety

IntroductionRoundabouts, as a form of intersection traffic control, are being constructed increasingly because of their promise to improve both efficiency and safety. However, roundabout performance varies from one context to another; and information on their performance during inclement weather is limited.MethodsTo evaluate the safety effects of converting signal-controlled intersections to modern roundabouts in a region that historically was unfamiliar with this type of traffic control, an empirical Bayes approach was used to analyze. Second, to examine the potential effects of rainfall on roundabout safety, a matched-pair approach was used to compare risk estimates of collision occurrence at roundabouts and signalized intersections under inclement weather conditions.ResultsRoundabout installation is shown as an effective safety intervention for serious collisions since conversion from signalized intersections to roundabouts translates into an overall 20% reduction in the occurrence of injury/fatal collisions. However, roundabouts witnessed more property-damage collisions than what would have been expected had the conversion not occurred. With respect to weather, there is no evidence of a statistically significant increase in crashes on days with rainfall relative to good weather conditions for roundabouts, whereas there is evidence of such an increase in crash risk estimated to be 4% to 22% for signalized intersections.ConclusionsWhile injury collisions are consistently found to be lower at intersections that have been converted from signalized intersections to roundabouts, the same is not always that case for property-damage collisions, suggesting that drivers need time to adjust. In terms of weather, the evidence in this paper shows that roundabouts show less sensitivity to rainy conditions than signalized intersections.Practical applicationsThe trade-offs between design, operation, and safety should be considered carefully when planning a new roundabout. More research is required on the specific problems users experience with roundabouts and the effectiveness of public education programs.     

Red light violations at rural and suburban signalized intersections in Jordan

This case study investigated red light violations at rural and suburban signalized intersections in Jordan. Field observations were conducted at 15 signalized intersections located in different Jordanian regions: Amman, Irbid, and Zarqa. The results showed that, out of a total of 1,190 drivers who had a chance for violation, 153 (12.9%) drivers ran red lights. It was found that older drivers have less tendency for running red lights. Based on vehicle type, the analysis showed that truck drivers had the highest violation rate followed by small vehicles and then buses. The Y-shaped intersection had a higher percentage of violations as compared to the T- and cruciform-shaped intersections. The percentage of red light violations was found to be directly proportional to the subjects approach speed and inversely proportional to the conflicting traffic volumes.     

Investigating factors influencing pedestrian injury severity at intersections

Objective: Vehicle crashes that involve pedestrians at intersections have been reported occasionally. Pedestrian injury severity in these crashes is significantly related to driver and pedestrian attributes, vehicle characteristics, and the geometry of intersections. Identifying factors associated with pedestrian injury severity (PIS) is critical for reducing crashes and improving safety. For developing the proposed probit models, drivers involved in crashes are classified into 3 groups: young drivers (16 ≤ age ≤ 24), middle-aged drivers (25 ≤ age ≤ 64), and older drivers (age ≥ 65). This study determines that PIS is significantly but differently affected by these grouped drivers with different sets of explanatory variables.

Methods: A total of 2,614 crash records (2011–2012) at intersections in Cook County, Illinois, were collected. An ordered probit modeling approach was employed to develop the proposed model and examine factors influencing PIS. The likelihood ratio test was used to assess model performance. Elasticity analysis was conducted to interpret the marginal effect of contributing factors on PIS associated with different driver groups by age.

Results: The results show that 4 independent variables, including pedestrian age, vehicle type, point of first contact, and weather condition, significantly affect PIS at intersections for all drivers. Two additional independent variables (i.e., number of vehicles and traffic type) affect PIS for young and middle-aged drivers, and 2 other variables (i.e., divided type and hit-and-run related) are significant to PIS for both young and older drivers.

Conclusions: The independent variables significant to PIS at intersections for young, middle-aged, and older driver groups were identified and the marginal effect of each variable to the likelihood of PIS were assessed.     

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

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

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.     

Effect of pavement condition and geometrics at signalised intersections on casualty crashes

IntroductionThis study investigated the effects of pavement surface condition and other control factors on casualty crashes at signalized intersections. It involved conducting a before and after study for road surface condition and situational factors. It also included assessing the effects of geometric characteristics on safety performance of signalized intersections post resurfacing to control for the effect of pavement surface condition. Pavement surface condition included roughness, rutting, and skid resistance. The control factors included traffic volume, light and surface moisture condition, and speed limit. The geometric characteristics included approach width, number of lanes, intersection depth, presence of median, presence of shared lane, and presence of bus stop.MethodTo account for the repeated observations of the effect of light and surface moisture conditions in four occasions (day-dry, day-wet, night-dry and night-wet) Generalized Estimating Equation (GEE) with Negative Binomial (NB) and log link function was applied. For each signalized intersection in the sample, condition data are collected for the year before and after the year of surface treatment. Crash data, however, are collected for a minimum of three and maximum of five years before and after treatment years.ResultsThe results show that before treatment, light condition, road surface moisture condition, and skid resistance interaction with traffic volume are the significant contributors to crash occurrence. For after treatment; light condition, road surface moisture condition, their interaction product, and roughness interaction with light condition, surface moisture condition, and traffic volume are the significant contributors. The geometric variables that were found to have significant effects on crash frequency post resurfacing were approach width interactions with presence of shared lane, bus stop, or median.ConclusionsThe findings confirm that resurfacing is significant in reducing crash frequency and severity levels.Practical Applications: The study findings would help for better understanding of how geometric characteristics can be improved to reduce crash occurrence.     

Analysis of effects of driver’s evasive action time on rear-end collision risk using a driving simulator

Introduction: Driver’s evasive action is closely associated with collision risk in a critical traffic event. To quantify collision risk, surrogate safety measures (SSMs) have been estimated using vehicle trajectories. However, vehicle trajectories cannot clearly capture presence and time of driver’s evasive action. Thus, this study determines the driver’s evasive action based on his/her use of accelerator and brake pedals, and analyzes the effects of the driver’s evasive action time (i.e., duration of evasive action) on rear-end collision risk. Method: Fifty drivers’ car-following behavior on a freeway was observed using a driving simulator. An SSM called “Deceleration Rate to Avoid Crash (DRAC)” and the evasive action time were determined for each driver using the data from the driving simulator. Each driver tested two traffic scenarios – Cars and Trucks scenarios where conflicting vehicles were cars and trucks, respectively. The factors related to DRAC were identified and their effects on DRAC were analyzed using the Generalized Linear Models and random effects models. Results: DRAC decreased with the evasive action time and DRAC was closely related to drivers’ gender and driving experience at the road sections where evasive action to avoid collision was required. DRAC was also significantly different between Cars and Trucks scenarios. The effect of the evasive action time on DRAC varied among different drivers, particularly in the Trucks scenario. Conclusions: Longer evasive action time can significantly reduce crash risk. Driver characteristics are more closely related to effective evasive action in complex driving conditions. Practical Applications: Based on the findings of this study, driver warning information can be developed to alert drivers to take specific evasive action that reduces collision risk in a critical traffic event. The information is likely to reduce the variability of the driver’s evasive action and the speed variations among different drivers.     

Examination of drivers' cell phone use behavior at intersections by using naturalistic driving data

IntroductionMany driving simulator studies have shown that cell phone use while driving greatly degraded driving performance. In terms of safety analysis, many factors including drivers, vehicles, and driving situations need to be considered. Controlled or simulated studies cannot always account for the full effects of these factors, especially situational factors such as road condition, traffic density, and weather and lighting conditions. Naturalistic driving by its nature provides a natural and realistic way to examine drivers' behaviors and associated factors for cell phone use while driving.MethodIn this study, driving speed while using a cell phone (conversation or visual/manual tasks) was compared to two baselines (baseline 1: normal driving condition, which only excludes driving while using a cell phone, baseline 2: driving-only condition, which excludes all types of secondary tasks) when traversing an intersection.ResultsThe outcomes showed that drivers drove slower when using a cell for both conversation and visual/manual (VM) tasks compared to baseline conditions. With regard to cell phone conversations, drivers were more likely to drive faster during the day time compared to night time driving and drive slower under moderate traffic compared to under sparse traffic situations. With regard to VM tasks, there was a significant interaction between traffic and cell phone use conditions. The maximum speed with VM tasks was significantly lower than that with baseline conditions under sparse traffic conditions. In contrast, the maximum speed with VM tasks was slightly higher than that with baseline driving under dense traffic situations.Practical applicationsThis suggests that drivers might self-regulate their behavior based on the driving situations and demand for secondary tasks, which could provide insights on driver distraction guidelines. With the rapid development of in-vehicle technology, the findings in this research could lead the improvement of human-machine interface (HMI) design as well.     

Gender differences of young drivers on injury severity outcome of highway crashes

ProblemGender differences of young drivers involved in crashes and the associated differences in risk factors have not been fully explored in the United States (U.S.). Accordingly, this study investigated the topic, where the odds ratios (ORs) were used to identify differences in crash involvements between male and female young drivers.MethodLogistic regression models for injury severity of young male drivers and young female drivers were developed. Different driver, environmental, vehicle, and road related factors that have affected young female drivers' and young male drivers' crash involvements were identified using the models.ResultsResults indicated that some variables are significantly related to female drivers' injury risk but not male drivers' injury risk and vice versa. Variables such as driving with valid licenses, driving on weekends, avoidance or slow maneuvers at time of crash, non-collision and overturn crashes, and collision with a pedestrian were significant variables in female driver injury severity model but not in young male driver severity model. Travel on graded roadways, concrete surfaces, and wet road surfaces, collision with another vehicle, and rear-end collisions were variables that were significant in male-driver severity model but not in female-driver severity model.SummaryFactors which increase young female drivers' injury severity and young male drivers' injury severity were identified. This study adds detailed information about gender differences and similarities in injury severity risk of young drivers.Practical applicationsIt is important to note that the findings of this study show that gender differences do exists among young drivers. This sends a message to the industry that the transportation professionals and researchers, who are developing countermeasures to increase the traffic safety, may need to pay attention to the differences. This might be particularly true when developing education materials for driver training for young/inexperienced drivers.     

Using naturalistic driving study data to investigate the impact of driver distraction on driver's brake reaction time in freeway rear-end events in car-following situation

IntroductionThe rear-end crash is one of the most common freeway crash types, and driver distraction is often cited as a leading cause of rear-end crashes. Previous research indicates that driver distraction could have negative effects on driving performance, but the specific association between driver distraction and crash risk is still not fully revealed. This study sought to understand the mechanism by which driver distraction, defined as secondary task distraction, could influence crash risk, as indicated by a driver's reaction time, in freeway car-following situations.MethodA statistical analysis, exploring the causal model structure regarding drivers’ distraction impacts on reaction times, was conducted. Distraction duration, distraction scenario, and secondary task type were chosen as distraction-related factors. Besides, exogenous factors including weather, visual obstruction, lighting condition, traffic density, and intersection presence and endogenous factors including driver age and gender were considered.ResultsThere was an association between driver distraction and reaction time in the sample freeway rear-end events from SHRP 2 NDS database. Distraction duration, the distracted status when a leader braked, and secondary task type were related to reaction time, while all other factors showed no significant effect on reaction time.ConclusionsThe analysis showed that driver distraction duration is the primary direct cause of the increase in reaction time, with other factors having indirect effects mediated by distraction duration. Longer distraction duration, the distracted status when a leader braked, and engaging in auditory-visual-manual secondary task tended to result in longer reaction times.Practical applicationsGiven drivers will be distracted occasionally, countermeasures which shorten distraction duration or avoid distraction presence while a leader vehicle brakes are worth considering. This study helps better understand the mechanism of freeway rear-end events in car-following situations, and provides a methodology that can be adopted to study the association between driver behavior and driving features.     

Examining traffic crash injury severity at unsignalized intersections

Introduction

This study presents multiple approaches to the analysis of crash injury severity at three- and four-legged unsignalized intersections in the state of Florida from 2003 until 2006. An extensive data collection process was conducted for this study.

Method

The dataset used in the analysis included 2,043 unsignalized intersections in six counties in the state of Florida. For the scope of this study, there were three approaches explored. The first approach dealt with the five injury levels, and an ordered probit model was fitted. The second approach was an aggregated one, and dealt with only the severe versus non-severe crash levels, and a binary probit model was used. The third approach dealt with fitting a nested logit model. Results from the three fitted approaches were shown and discussed, and a comparison between the three approaches was shown.

Results

Several important factors affecting crash severity at unsignalized intersections were identified. These include the traffic volume on the major approach, and the number of through lanes on the minor approach (surrogate measure for traffic volume), and among the geometric factors, the upstream and downstream distance to the nearest signalized intersection, left and right shoulder width, number of left turn movements on the minor approach, and number of right and left turn lanes on the major approach. As for driver factors, young and very young at-fault drivers were associated with the least fatal probability compared to other age groups.

Impact on industry

The analysis identified some countermeasures to reduce injury severity at unsignalized intersections. The spatial covariates showed the importance of including safety awareness campaigns for speeding enforcement. Also, having a 90-degree intersection design is the most appropriate safety design for reducing severity. Moreover, the assurance of marking stop lines at unsignalized intersections is very essential.     

Investigating the self-reported behavior of drivers and their attitudes to traffic violations

IntroductionDriving behavior theoretical models consider attitudes as an important determinant of driver behavior. Moreover, the association between the self-reported tendency to commit violations and accident involvement is widely recognized. This research investigates drivers’ self-reported behavior and attitudes to risky behaviors related to the traffic violations of speeding, drink-driving, and cell phone use using cluster analysis.MethodA sample of 601 Greek drivers participating at the SARTRE 4 pan-European survey is utilized. The analysis identified three clusters of drivers. Drivers in Cluster 1 commit traffic violations more often; drivers in Cluster 2 favor traffic violation countermeasures while having moderate views toward compliance with traffic rules; and drivers in Cluster 3 strongly support traffic violation countermeasures and also have strong views toward compliance with traffic rules. Risky behaviors and related attitudes that differentiate the three distinct groups of drivers (clusters) were determined.ResultsThe findings indicate that differences in attitudes and behaviors may be attributed to factors such as age, gender, and area of residence. The research findings also provided some insight about the current level of drivers’ attitudes to traffic violations, especially those that negatively affect traffic safety. The pattern of their views on violations may form the basis of risk behavior-related interventions tailored to the identified groups, aiming at informing, educating, and raising the awareness of the public.Impact on IndustryAgencies focused on safety interventions could exploit this information in designing and implementing education campaigns, enforcement programs and in defining relevant priorities.     

Traffic rules and traffic safety

In the present paper the effects of traffic rules on driver behaviour and on traffic safety are discussed. The discussion is mainly based on research conerning driver’s use of safety equipment, their speed adjustment and drunken driving with respect to safety potential, effects of the rules, and effects of enforcement on driver behaviour. Factors influencing drivers’ decisions to comply with rules are also considered. It is concluded that only rules that are possible to enforce should be implemented and that police surveillance should be visible to the drivers. Also, the traffic system should be seen as a social system where drivers are interacting with other drivers and road users. Rules and regulations are important to help the actors of the system to function in a safe and effective way.     

Assessing driver behavior using shrp2 adverse weather data

IntroductionThis study explored how drivers adapt to inclement weather in terms of driving speed, situational awareness, and visibility as road surface conditions change from dry to slippery and visibility decreases. The proposed work mined existing data from the SHRP 2 NDS for drivers who were involved in weather-related crash and near-crash events. Baseline events were also mined to create related metadata necessary for behavioral comparisons. Methods: Researchers attempted, to the greatest extent possible, to match non-adverse-weather driving scenarios that are similar to the crash and near-crash event for each driver. The ideal match scenario would be at a day prior to the crash during non-adverse weather conditions having the same driver, at the same time of day, with the same traffic level on the same road on which the crash or near-crash occurred. Once the matched scenarios have been identified, a detailed analysis will be performed to determine how a driver’s behavior changed from normal driving to inclement-weather driving. Results: Data collected indicated that, irrespective of site location (i.e., state), most crashes and near-crashes occurred in rain, with only about 12% occurring in snowy conditions. Also, the number of near-crashes was almost double the number of crashes showing that many drivers were able to avoid a crash by executing an evasive maneuver such as braking or steering. Conclusions: Most types of near crashes were rear-end and sideswipe avoidance epochs, as the drivers may have had a difficult time merging or trying to change lanes due to low visibility or traffic. Hard braking combined with swerving were the most commonly used evasive maneuvers, occurring when drivers did not adjust their speeds accordingly for specific situations. Practical applications: Results from this study are expected to be utilized to educate and guide drivers toward more confident and strategic driving behavior in adverse weather.     

Driving safety improves after individualized training: An RCT involving older drivers in an urban area

Objective: This study aimed to reproduce the results of a previous investigation on the safety benefits of individualized training for older drivers. We modified our method to address validity and generalizability issues.

Methods: Older drivers were randomly assigned to one of the 3 arms: (1) education alone, (2) education?+?on road training, and (3) education?+?on road?+?simulator training. Older drivers were recruited from a larger urban community. At the pre- and posttests (separated by 4 to 8 weeks) participants followed driving directions using a Global Positioning System (GPS) navigation system.

Results: Our findings support the positive influence of individualized on-road training for urban-dwelling older drivers. Overall, driving safety improved among drivers who received on-road training over those who were only exposed to an education session, F(1, 40) = 11.66, P = .001 (26% reduction in total unsafe driving actions [UDAs]). Statistically significant improvements were observed on observation UDAs (e.g., scanning at intersections, etc.), compliance UDAs (e.g., incomplete stop), and procedural UDAs (e.g., position in lane).

Conclusion: This study adds to the growing evidence base in support of individualized older driver training to optimize older drivers’ safety and promote continued safe driving.     

Assessing characteristics related to the use of seatbelts and cell phones by drivers: Application of a bivariate probit model

IntroductionThe effects of cell phone use and safety belt use have been an important focus of research related to driver safety. Cell phone use has been shown to be a significant source of driver distraction contributing to substantial degradations in driver performance, while safety belts have been demonstrated to play a vital role in mitigating injuries to crash-involved occupants.MethodThis study examines the prevalence of cell phone use and safety belt non-use among the driving population through direct observation surveys. A bivariate probit model is developed to simultaneously examine the factors that affect cell phone and safety belt use among motor vehicle drivers.ResultsThe results show that several factors may influence drivers' decision to use cell phones and safety belts, and that these decisions are correlated.Practical applicationsUnderstanding the factors that affect both cell phone use and safety belt non-use is essential to targeting policy and programs that reduce such behavior.