How do drivers behave during indecision zone maneuvers?

Yellow signal indications at intersections are well-known to be a contributor to traffic crashes. This study examined drivers’ behavior during yellow signal indication (i.e., indecision zone) maneuvers. Data from a driving simulator study was used, which included drivers’ performance data when they encountered a yellow signal indication at intersections under different secondary-task conditions. This study calculated drivers’ likelihood to go through a yellow signal indication and examined factors that are related to drivers’ decision making on intersection traversing. The results showed that drivers’ decision on stopping or not at a yellow signal indication was associated with different variables including age, distraction, pedal conditions, and time to stop line. Distracted drivers’ insensitive behavior was also captured from the significant interaction effect between time to stop line and distraction conditions, which implied that intersection related crash risk may increase when drivers were distracted.     

Effects of major-road vehicle speed and driver age and gender on left-turn gap acceptance

Because the driver's gap-acceptance maneuver is a complex and risky driving behavior, it is a highly concerned topic for traffic safety and operation. Previous studies have mainly focused on the driver's gap acceptance decision itself but did not pay attention to the maneuver process and driving behaviors. Using a driving simulator experiment for left-turn gap acceptance at a stop-controlled intersection, this study evaluated the effects of major traffic speed and driver age and gender on gap acceptance behaviors. The experiment results illustrate relationships among drivers' left-turn gap decision, driver's acceleration rate, steering action, and the influence of the gap-acceptance maneuver on the vehicles in the major traffic stream. The experiment results identified an association between high crash risk and high traffic speed at stop-controlled intersections. The older drivers, especially older female drivers, displayed a conservative driving attitude as a compensation for reduced driving ability, but also showed to be the most vulnerable group for the relatively complex driving maneuvers.     

How the choice of safety performance function affects the identification of important crash prediction variables

Across the nation, researchers and transportation engineers are developing safety performance functions (SPFs) to predict crash rates and develop crash modification factors to improve traffic safety at roadway segments and intersections. Generalized linear models (GLMs), such as Poisson or negative binomial regression, are most commonly used to develop SPFs with annual average daily traffic as the primary roadway characteristic to predict crashes. However, while more complex to interpret, data mining models such as boosted regression trees have improved upon GLMs crash prediction performance due to their ability to handle more data characteristics, accommodate non-linearities, and include interaction effects between the characteristics.An intersection data inventory of 36 safety relevant parameters for three- and four-legged non-signalized intersections along state routes in Alabama was used to study the importance of intersection characteristics on crash rate and the interaction effects between key characteristics. Four different SPFs were investigated and compared: Poisson regression, negative binomial regression, regularized generalized linear model, and boosted regression trees. The models did not agree on which intersection characteristics were most related to the crash rate. The boosted regression tree model significantly outperformed the other models and identified several intersection characteristics as having strong interaction effects.     

Validating a driving simulator using surrogate safety measures

Traffic crash statistics and previous research have shown an increased risk of traffic crashes at signalized intersections. How to diagnose safety problems and develop effective countermeasures to reduce crash rate at intersections is a key task for traffic engineers and researchers. This study aims at investigating whether the driving simulator can be used as a valid tool to assess traffic safety at signalized intersections. In support of the research objective, this simulator validity study was conducted from two perspectives, a traffic parameter (speed) and a safety parameter (crash history). A signalized intersection with as many important features (including roadway geometries, traffic control devices, intersection surroundings, and buildings) was replicated into a high-fidelity driving simulator. A driving simulator experiment with eight scenarios at the intersection were conducted to determine if the subjects' speed behavior and traffic risk patterns in the driving simulator were similar to what were found at the real intersection. The experiment results showed that speed data observed from the field and in the simulator experiment both follow normal distributions and have equal means for each intersection approach, which validated the driving simulator in absolute terms. Furthermore, this study used an innovative approach of using surrogate safety measures from the simulator to contrast with the crash analysis for the field data. The simulator experiment results indicated that compared to the right-turn lane with the low rear-end crash history record (2 crashes), subjects showed a series of more risky behaviors at the right-turn lane with the high rear-end crash history record (16 crashes), including higher deceleration rate (1.80+/-1.20 m/s(2) versus 0.80+/-0.65 m/s(2)), higher non-stop right-turn rate on red (81.67% versus 57.63%), higher right-turn speed as stop line (18.38+/-8.90 km/h versus 14.68+/-6.04 km/h), shorter following distance (30.19+/-13.43 m versus 35.58+/-13.41 m), and higher rear-end probability (9/59=0.153 versus 2/60=0.033). Therefore, the relative validity of driving simulator was well established for the traffic safety studies at signalized intersections.     

Motor vehicle-bicycle crashes in Beijing: irregular maneuvers, crash patterns, and injury severity

This research presents a comprehensive analysis of motor vehicle–bicycle crashes using 4 years of reported crash data (2004–2007) in Beijing. The interrelationship of irregular maneuvers, crash patterns and bicyclist injury severity are investigated by controlling for a variety of risk factors related to bicyclist demographics, roadway geometric design, road environment, etc.Results show that different irregular maneuvers are correlated with a number of risk factors at different roadway locations such as the bicyclist age and gender, weather and traffic condition. Furthermore, angle collisions are the leading pattern of motor vehicle–bicycle crashes, and different irregular maneuvers may lead to some specific crash patterns such as head-on or rear-end crashes. Orthokinetic scrape is more likely to result in running over bicyclists, which may lead to more severe injury. Moreover, bicyclist injury severity level could be elevated by specific crash patterns and risk factors including head-on and angle collisions, occurrence of running over bicyclists, night without streetlight, roads without median/division, higher speed limit, heavy vehicle involvement and older bicyclists.This study suggests installation of median, division between roadway and bikeway, and improvement of illumination on road segments. Reduced speed limit is also recommended at roadway locations with high bicycle traffic volume. Furthermore, it may be necessary to develop safety campaigns aimed at male, teenage and older bicyclists.     

Aging and the detection of imminent collisions under simulated fog conditions

The present study examined age-related differences in collision detection performance when contrast of the driving scene was reduced by simulated fog. Older and younger drivers were presented with a collision detection scenario in a simulator in which an object moved at a constant speed on a linear trajectory towards the driver. Drivers were shown part of the motion path of an approaching object that would eventually either collide with or pass by the driver and were required to determine whether or not the object would collide with the driver. Driver motion was either stationary or moving along a linear path down the roadway. A no fog condition and three different levels of fog were examined. Detection performance decreased when dense fog was simulated for older but not for younger observers. An age-related decrement was also found with shorter display durations (longer time to contact). When the vehicle was moving decrements in performance were observed for both younger and older drivers. These results suggest that under inclement weather conditions with reduced visibility, such as fog, older drivers may have an increased crash risk due to a decreased ability to detect impending collision events.     

Driving under the influence of cannabis: Links with dangerous driving, psychological predictors, and accident involvement

Driving under the influence of cannabis (DUIC) has become a growing concern. Studies investigating the impact of DUIC on traffic safety have shown evidence that, during the acute period of cannabis intoxication, cannabis diminishes driving faculties and is associated with an elevated risk of collision. However, DUIC drivers seem to exhibit a general reckless driving style that may contribute to an over-estimation of DUIC-related collisions among this group. In this study, we investigated DUIC drivers with respect to self-reported dangerous driving habits (e.g., risky driving, aggressive driving and negative emotional driving), behaviours observed in a driving simulator, psychological predictors and crash involvement. Results suggest that DUIC is associated with self-reported and observed risky driving and negative emotional driving. We also found that sensation seeking and impulsivity are independent psychological predictors of DUIC. Finally, a trend suggests that self-reported DUIC is associated with an increased risk of being involved in a car accident, after controlling for dangerous driving and demographic variables. Implications for interventions are discussed.     

Drivers’ eye movements as a function of collision avoidance warning conditions in red light running scenarios

The intersection collision avoidance warning systems (ICAWSs) have substantial potentials in improving driving performance and reducing the number and severity of intersection collisions, through helping drivers timely detect hazardous conflicting vehicles in precrash scenarios. However, the influences of ICAWS on drivers’ visual performance have barely been discussed. This study focuses on exploring the patterns in drivers’ eye movements as a function of ICAWS’s warning conditions in red light running scenarios based on a driving simulation experiment. Two types of speech warning conditions including warning timings (varied form 2.5 s to 5.5 s) and directional information (with or without) are examined, and the no-warning condition is the baseline. The results revealed that more subjects would be likely to benefit from the ICWAS under the earlier warning timings. The warning condition of 4.5 s ahead of a collision had the best effectiveness in terms of visual performances. Under such a warning timing, drivers had shorter fixation duration and higher frequency of searching for the red light running (RLR) vehicles. Compared to the warning condition without directional information, the directional warning information could capture drivers’ attention more efficiently, help driver direct fixations toward the RLR vehicles more quickly and lead to more scanning activities. Compared to female drivers, male drivers had more scanning activities when approaching intersections, detected the RLR vehicles more quickly and were more likely to avoid the RLR collisions. Besides, the experiment results indicated that the female drivers were more inclined to trust the warning information and got more benefits from the RLR-ICAWS in terms of the crash risk reduction rate than male drivers. Finally, the conclusions lead the way toward warning condition design recommendations for improving the effectiveness of the RLR-ICAWSs.     

The effects of driver training on simulated driving performance

Given that the beneficial effects of driver training on accident risk may not be an appropriate criterion measure, this study investigates whether professionally trained and experienced drivers exhibit safer driving behaviour in a simulated driving task compared with drivers without professional driver training. A sample of 54 police trained drivers and a sample of 56 non-police trained drivers were required to complete two tasks. Firstly to overtake a slow-moving bus on a hazardous stretch of single-lane road with bends and hills and secondly to follow a lead vehicle travelling at 55mph in a built-up section with a speed limit of 30mph. Results showed that in comparison with non-police trained drivers, police drivers were significantly less likely to cross the central division of the road at unsafe locations during the overtaking task and reduced their speed on approach to pedestrians at the roadside in the following task to a greater extent. Police drivers also adopted a more central lane position compared with non-police trained drivers on urban roads and at traffic lights during the following task. Driver group differences in simulated driving performance are discussed with reference to the implications for driver training assessment and skill development.     

Driver's visual attention as a function of driving experience and visibility. Using a driving simulator to explore drivers’ eye movements in day, night and rain driving

Road crashes are the main cause of death of young people in the developed world. The reasons that cause traffic crashes are numerous; however, most researchers agree that a lack of driving experience is one of the major contributing factors. In addition it has been demonstrated that environmental factors such as driving during night and rain increases the risk of a crash. Both of these factors may be related to drivers’ visual search strategies that become more efficient with increased experience.In the present study we recorded the eye movements of driving instructors and learner drivers while they drove three virtual routes that included day, night and rain routes in a driving simulator.The results showed that driving instructors had an increased sampling rate, shorter processing time and broader scanning of the road than learner drivers. This broader scanning of the road could be possibly explained by the mirror inspection pattern which revealed that driving instructors fixated more on the side mirrors than learner drivers. Also it was found that poor visibility conditions, especially rain, decrease the effectiveness of drivers’ visual search. The lack of interaction between driving experience and visibility suggests that some aspects of visual search are affected by general rather than situation specific driving experience.The present findings support the effect of driving experience in modifying eye movement strategies. The high accident risk of night and rain driving could be partly explained by the decrement in visual search strategies during these conditions. Finally it is argued that the use of driving simulators can provide valuable insights regarding driving safety.     

Multivariate random-parameters zero-inflated negative binomial regression model: An application to estimate crash frequencies at intersections

Crash data are collected through police reports and integrated with road inventory data for further analysis. Integrated police reports and inventory data yield correlated multivariate data for roadway entities (e.g., segments or intersections). Analysis of such data reveals important relationships that can help focus on high-risk situations and coming up with safety countermeasures. To understand relationships between crash frequencies and associated variables, while taking full advantage of the available data, multivariate random-parameters models are appropriate since they can simultaneously consider the correlation among the specific crash types and account for unobserved heterogeneity. However, a key issue that arises with correlated multivariate data is the number of crash-free samples increases, as crash counts have many categories. In this paper, we describe a multivariate random-parameters zero-inflated negative binomial (MRZINB) regression model for jointly modeling crash counts. The full Bayesian method is employed to estimate the model parameters. Crash frequencies at urban signalized intersections in Tennessee are analyzed. The paper investigates the performance of MZINB and MRZINB regression models in establishing the relationship between crash frequencies, pavement conditions, traffic factors, and geometric design features of roadway intersections. Compared to the MZINB model, the MRZINB model identifies additional statistically significant factors and provides better goodness of fit in developing the relationships. The empirical results show that MRZINB model possesses most of the desirable statistical properties in terms of its ability to accommodate unobserved heterogeneity and excess zero counts in correlated data. Notably, in the random-parameters MZINB model, the estimated parameters vary significantly across intersections for different crash types.     

Discrepancy analysis of driving performance of taxi drivers and non-professional drivers for red-light running violation and crash avoidance at intersections

Due to comfort, convenience, and flexibility, taxis have become increasingly more prevalent in China, especially in large cities. However, many violations and road crashes that occurred frequently were related to taxi drivers. This study aimed to investigate differences in driving performance between taxi drivers and non-professional drivers from the perspectives of red-light running violation and potential crash involvement based on a driving simulation experiment. Two typical scenarios were established in a driving simulator, which includes the red-light running violation scenario and the crash avoidance scenario. There were 49 participants, including 23 taxi drivers (14 males and 9 females) and 26 non-professional drivers (13 males and 13 females) recruited for this experiment. The driving simulation experiment results indicated that non-professional drivers paid more attention to red-light running violations in comparison to taxi drivers who had a higher probability of red-light running violation. Furthermore, it was found that taxi drivers were more inclined to turn the steering wheel in an attempt to avoid a potential collision and non-professional drivers had more abrupt deceleration behaviors when facing a potential crash. Moreover, the experiment results showed that taxi drivers had a smaller crash rate compared to non-professional drivers and had a better performance in terms of crash avoidance at the intersection.     

An on-road assessment of cognitive distraction: impacts on drivers' visual behavior and braking performance

In this on-road experiment, drivers performed demanding cognitive tasks while driving in city traffic. All task interactions were carried out in hands-free mode so that the 21 drivers were not required to take their visual attention away from the road or to manually interact with a device inside the vehicle. Visual behavior and vehicle control were assessed while they drove an 8 km city route under three conditions: no additional task, easy cognitive task and difficult cognitive task. Changes in visual behavior were most apparent when performance between the No Task and Difficult Task conditions were compared. When looking outside of the vehicle, drivers spent more time looking centrally ahead and spent less time looking to the areas in the periphery. Drivers also reduced their visual monitoring of the instruments and mirrors, with some drivers abandoning these tasks entirely. When approaching and driving through intersections, drivers made fewer inspection glances to traffic lights compared to the No Task condition and their scanning of intersection areas to the right was also reduced. Vehicle control was also affected; during the most difficult cognitive tasks there were more occurrences of hard braking. Although hands-free designs for telematics devices are intended to reduce or eliminate the distraction arising from manual operation of these units, the potential for cognitive distraction associated with their use must also be considered and appropriately assessed. These changes are captured in measures of drivers' visual behavior.     

Temporal and spatial analyses of rear-end crashes at signalized intersections

In this study, the generalized estimating equations with the negative binomial link function were used to model rear-end crash frequencies at signalized intersections to account for the temporal or spatial correlation among the data. The longitudinal data for 208 signalized intersections over 3 years and the spatially correlated data for 476 signalized intersections which are located along different corridors were collected in the state of Florida. The modeling results showed that there are high correlations between the longitudinal or spatially correlated rear-end crashes. Some intersection related variables are identified as significantly influencing rear-end crash occurrences at signalized intersections. Intersections with heavy traffic on the major and minor roadways, having more right and left-turn lanes on the major roadway, having a large number of phases per cycle (indicated by the left-turn protection on the minor roadway), with high speed limits on the major roadway, and in high population areas are correlated with high rear-end crash frequencies. On the other hand, intersections with three legs, having channelized or exclusive right-turn lanes on the minor roadway, with protected left-turning on the major roadway, with medians on the minor roadway, and having longer signal spacing have a lower frequency of rear-end crashes.     

Model crash frequency at highway–railroad grade crossings using negative binomial regression

Despite the fact that traffic collisions at highway–railroad grade crossings (HRGXs) are rare events, the impact of HRGX crashes is nevertheless more severe than highway crashes. Empirical studies show that traffic collisions at HRGXs are mainly attributed to railway-related and/or highway-related characteristics, particularly drivers’ abnormal behavior, driving around, or through an HRGX. These factors have different effects on crash likelihood (i.e., the number of traffic collisions or crash frequency) at an HRGX. To explore the causal relationship between crash frequency and the factors related to railroad and highway systems, we used a negative binomial regression model to identify the factors that are statistically significantly associated with traffic collisions at HRGXs, and conducted relevant sensitivity analyses to investigate the marginal effect of daily highway traffic on changes in crash frequency. The empirical study shows that the number of daily trains, the number of tracks, highway separation, annual averaged daily traffic (AADT), and crossing length had statistically significant effects on the mean number of traffic collisions (all p-values?≤?0.0487). Further, the marginal effect of the AADT on the change of crash frequency indicates that crash likelihood monotonically increases with the increase of AADT.     

Factors associated with civilian drivers involved in crashes with emergency vehicles

Motor vehicle crashes involving civilian and emergency vehicles (EVs) have been a known problem that contributes to fatal and nonfatal injuries; however, characteristics associated with civilian drivers have not been examined adequately. This study used data from The National Highway Traffic Safety Administration's Fatality Analysis Reporting System and the National Automotive Sampling System General Estimates System to identify driver, roadway, environmental, and crash factors, and consequences for civilian drivers involved in fatal and nonfatal crashes with in-use and in-transport EVs. In general, drivers involved in emergency–civilian crashes (ECCs) were more often driving: straight through intersections (vs. same direction) of four-points or more (vs. not at intersection); where traffic signals were present (vs. no traffic control device); and at night (vs. midday). For nonfatal ECCs, drivers were more often driving: distracted (vs. not distracted); with vision obstructed by external objects (vs. no obstruction); on dark but lighted roads (vs. daylight); and in opposite directions (vs. same directions) of the EVs. Consequences included increased risk of injury (vs. no injury) and receiving traffic violations (vs. no violation). Fatal ECCs were associated with driving on urban roads (vs. rural), although these types of crashes were less likely to occur on dark roads (vs. daylight). The findings of this study suggest drivers may have difficulties in visually detecting EVs in different environments.     

Effect of different breath alcohol concentrations on driving performance in horizontal curves

Driving under the influence of alcohol on curved roadway segments has a higher risk than driving on straight segments. To explore the effect of different breath alcohol concentration (BrAC) levels on driving performance in roadway curves, a driving simulation experiment was designed to collect 25 participants’ driving performance parameters (i.e., speed and lane position) under the influence of 4 BrAC levels (0.00%, 0.03%, 0.06% and 0.09%) on 6 types of roadway curves (3 radii × 2 turning directions). Driving performance data for 22 participants were collected successfully. Then the average and standard deviation of the two parameters were analyzed, considering the entire curve and different sections of the curve, respectively.     

Self-reported and observed risky driving behaviors among frequent and infrequent cell phone users

The apparently higher crash risk among individuals who use cell phones while driving may be due both to the direct interference of cell phone use with the driving task and tendencies to engage in risky driving behaviors independent of cell phone use. Measurements of actual highway driving performance, self-reported aberrant driving behaviors as measured by the Manchester Driver Behavior Questionnaire (DBQ), and attitudes toward speeding, passing behaviors and relative concern about being involved in a crash were assessed. Individuals who reported frequently using cell phones while driving were found to drive faster, change lanes more frequently, spend more time in the left lane, and engage in more instances of hard braking and high acceleration events. They also scored higher in self-reported driving violations on the DBQ and reported more positive attitudes toward speeding and passing than drivers who did not report using a cell phone regularly while driving. These results indicate that a greater reported frequency of cell phone use while driving is associated with a broader pattern of behaviors that are likely to increase the overall risk of crash involvement.     

To illuminate or not to illuminate: Roadway lighting as it affects traffic safety at intersections

A two-pronged effort to quantify the impact of lighting on traffic safety is presented. In the statistical approach, the effects of lighting on crash frequency for different intersection types in Minnesota were assessed using count regression models. The models included many geometric and traffic control variables to estimate the association between lighting and nighttime and daytime crashes and the resulting night-to-day crash ratios. Overall, the presence of roadway intersection lighting was found to be associated with an approximately 12% lower night-to-day crash ratio than unlighted intersections. In the parallel analytical approach, visual performance analyses based on roadway intersection lighting practices in Minnesota were made for the same intersection types investigated in the statistical approach. The results of both approaches were convergent, suggesting that visual performance improvements from roadway lighting could serve as input for predicting improvements in crash frequency. A provisional transfer function allows transportation engineers to evaluate alternative lighting systems in the design phase so selections based on expected benefits and costs can be made.