Representativity and univocity of traffic signs and their effect on trajectory movement in a driving simulation task: Warning signs

Objective: The effect of traffic signs on the behavior of drivers is not completely understood. Knowing about how humans process the meaning of signs (not just by learning but instinctively) will improve reaction time and decision making when traveling. The economic, social, and psychological consequences of car accidents are well known.

Methods: This study sounds out which traffic signs are more ergonomic for participants, from a cognitive point of view, and determines, at the same time, their effect in participants' movement trajectories in a driving simulation task.

Results: Results point out that the signs least representative of their meaning produce a greater deviation from the center of the road than the most representative ones.

Conclusions: This study encourages both an in-depth analysis of the effect on movement of roadside signs and the study of how this effect can be modified by the context in which these signs are presented (with the aim to move the research closer to and analyze the data in real contexts). The goal is to achieve clarity of meaning and lack of counterproductive effects on the trajectory of representative signs (those that provoke fewer mistakes in the decision task).     

Improvement of the performance of animal crossing warning signs

Introduction: Animal–vehicle collisions (AVCs) can result in serious injury and death to drivers, animals' death, and significant economic costs. However, the cost effectiveness of the majority of AVC mitigation measures is a significant issue. Method: A mobile-based data collection effort was deployed to measure signs under the Utah Department of Transportation's (UDOT) jurisdiction. The crash data were obtained from the UDOT risk management database. ArcGIS was employed to link these two data sets and extract animal-related crashes and signs. An algorithm was developed to process the data and identify AVCs that occurred within sign recognition distance. Kernel density estimation (KDE) technique was applied to identify potential crash hotspots. Results: Only 2% of AVCs occurred within the recognition distance of animal crossing signs. Almost 58% of animal-related crashes took place on the Interstate and U.S. highways, wherein only 30% of animal crossing signs were installed. State routes with a higher average number of signs experienced a lower number of AVCs per mile. The differences between AVCs that occurred within versus outside of sign recognition distance were not statistically significant regarding crash severity, time of crash, weather condition, driver age, vehicle speed, and type of animal. It is more likely that drivers become accustomed to deer crossing signs than cow signs. Conclusions: Based on the historical crash data and landscape structure, with attention given to the low cost safety improvement methods, a combination of different types of AVC mitigation measures can be developed to reduce the number of animal-related crashes. After an in-depth analysis of AVC data, warning traffic signs, coupled with other low cost mitigation countermeasures can be successfully placed in areas with higher priority or in critical areas. Practical applications: The findings of this study assist transportation agencies in developing more efficient mitigation measures against AVCs.     

Investigating the role of socio-economic factors in comprehension of traffic signs using decision tree algorithm

IntroductionDrivers' ability to comprehend the meaning of traffic signs is essential to safe driving. Drivers' personal characteristics are believed to play a crucial role in determining drivers' comprehension of traffic signs.MethodThis study investigates the role of age, gender, marital status, license category, educational level, driving experience, monthly income, and number of traffic violation during the last five years in drivers' comprehension of 39 posted traffic signs in the city of Irbid, Jordan. These signs include 15 regulatory signs, 17 warning signs, and 7 guidance signs. A total of 400 paper-based surveys were completed by drivers with different socio-economic characteristics. Subsequently, a decision tree was created for each category of traffic signs to identify the most influential factors affecting drivers' comprehension. Each tree was created twice; once using the whole data set for building and validating the tree, and a second time only using 80% of the data for building and 20% for validating.ResultsThe accuracy of the generated trees in predicting drivers' comprehension of regulatory, guidance, and warning traffic signs was 70%, 71%, and 66.5%, respectively, when using the whole data for building and validating the tree, and was 65%, 62.5%, and 61.3%, respectively, when using only 80% of the data for building and the remaining for validating.ConclusionsThe generated decision trees showed that driving experience, marital status, age, and education background are the most influential factors in determining drivers' comprehension of traffic signs as they were primary splitters in such trees.Practical applicationThe rules obtained from the decision tree can be utilized by transportation agencies to determine the drivers who need help with understanding the road traffic signs.     

The effects of driver factors and sign design features on the comprehensibility of traffic signs

PROBLEM: This paper addresses the effects of driver factors and sign design features on the comprehensibility of traffic signs. METHODS: A survey was designed to capture subjects' personal particulars, ratings on sign features, and comprehension scores, and then administered to 109 Hong Kong full driving license holders. RESULTS: Years with driving license and education level were significant predictors of sign comprehensibility. Contrary to expectation, the driver factors of age group, years of active driving, hours of driving, last time driving, driving frequency, and non-local driving experience had no effect on comprehension performance. Sign familiarity was correlated with comprehension score for licensed drivers, whereas sign concreteness, simplicity, and meaningfulness were not. IMPACT ON INDUSTRY: The results of this study provide useful guidelines for designing more user-friendly traffic signs in the future. It identified particular driver groups who lacked good understanding of traffic signs, and this information may assist the relevant organizations to better allocate traffic training resources, and better target future studies of traffic sign comprehension.     

Racial disparities in parent knowledge of concussion and recognition of signs and symptoms

Introduction: Concussion is a type of traumatic brain injury that can be sustained through participation in different sports. It is important that a parent be able to identify common and uncommon symptoms of a concussion to ensure the safety and good health of their child. The purpose of this study was to compare knowledge of concussion scores among White and African American parents and guardians. Methodology: This cross-sectional study consisted of a single survey of 53 questions that was given to parents/guardians of high school athletes at a preseason parent meeting. Parent and guardian knowledge of concussion was assessed through a series of 45 questions. Participants were asked to correctly identify signs and symptoms of concussion, answer questions regarding the anatomy of a concussion (i.e. a concussion is an injury to the brain), answer true/false questions about general concussion knowledge, select from a list the consequences of multiple concussions and select from a list the consequences of returning to play too soon from a concussion. Knowledge of concussion was calculated by summing correct responses for the 45 knowledge questions. Racial differences were calculated using an ANCOVA, controlling for socioeconomic school type. The statistical significance level was set a priori p ≤ 0.05 for all analyses. Results: Participants of this study consisted of 176 [115 (65.3%) White, 61 (34.7%) African American] parents/guardians of high school athletes. Significant differences in knowledge of concussion scores between White parents/guardians [38.50 ± 4.55 (85.6% correct)], and African American parents/guardians [35.15 ± 4.97, 78.1% correct)] were identified (F_(1,172) = 4.82, p = 0.03). Conclusion: Knowledge of concussion disparities exist between African American and White parents/guardians. This disparity could cause complications from concussion to surface among children and adolescents participating in sport as their parents/guardians may not be able to correctly identify the signs and symptoms in order to seek proper medical care. Practical Application: Findings from this study highlight quantitative differences in concussion knowledge of parents from different demographics. These findings underline disparities and inequities in access to concussion-health resources that need to be addressed.     

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.     

Does time of day matter at highway work zone crashes?

Introduction: It is widely agreed that highway work zones pose significant threats to road users because driving conditions in work zones are quite different from the normal ones, particularly when traffic volumes approach a highway capacity. Therefore, work zone safety is a critical aspect for state agencies and traffic engineers. Method: In the current study, a total of 10,218 crashes that occurred in highway work zones in the state of Washington for the period between 2007 and 2013 were used. Time of day is disaggregated into four subgroups: (1) Morning from 6:00 to 11:00 a.m. (2) Midday from 12:00 to 5:00 p.m. (3) Night from 6:00 to 11:00 p.m., and (4) Late night from 12:00 to 5:00 a.m. Then, four mixed logit models were estimated to account and correct for heterogeneity in the crash data by considering three injury severity levels: severe injury, minor injury, and no injury. Results: The estimation results reveal that most contributing factors are uniquely significant in a specific time of day period, whereas three factors affect injury severity regardless of time of day such as the indicators of not deployed airbag, one passenger vehicle involved in the crash, and rear-end collision. Further, some factors were found to affect injury severity into two or three time periods, such as female drivers that found to decrease the probability of no injury in morning and night time periods, while increasing severe injury outcome in midday time. Conclusions: The effect of time of day on injury severity of work-zone related crashes should be modeled separately rather than using a holistic model. Practical applications: As a starting point, findings of the current study can be used by transportation officials to reduce fatalities and injuries of work zone crashes by identifying factors that uniquely contribute to each time of day period.     

Evaluating safety-influencing factors at stop-controlled intersections using automated video analysis

Introduction: Although stop signs are popular in North America, they have become controversial in cities like Montreal, Canada where they are often installed to reduce vehicular speeds and improve pedestrian safety despite limited evidence demonstrating their effectiveness. The purpose of this study is to evaluate the impact of stop-control configuration (and other features) on safety using statistical models and surrogate measures of safety (SMoS), namely vehicle speed, time-to-collision (TTC), and post-encroachment time (PET), while controlling for features of traffic, geometry, and built environment. Methods: This project leverages high-resolution user trajectories extracted from video data collected for 100 intersections, 336 approaches, and 130,000 road users in Montreal to develop linear mixed-effects regression models to account for within-site and within-approach correlations. This research proposes the Intersection Exposure Group (IEG) indicator, an original method for classifying microscopic exposure of pedestrians and vehicles. Results: Stop signs were associated with an average decrease in approach speed of 17.2 km/h and 20.1 km/h, at partially and fully stop-controlled respectively. Cyclist or pedestrian presence also significantly lower vehicle speeds. The proposed IEG measure was shown to successfully distinguish various types of pedestrian-vehicle interactions, allowing for the effect of each interaction type to vary in the model. Conclusions: The presence of stop signs significantly reduced approach speeds compared to uncontrolled approaches. Though several covariates were significantly related to TTC and PET for vehicle pairs, the models were unable to demonstrate a significant relationship between stop signs and vehicle–pedestrian interactions. Therefore, drawing conclusions regarding pedestrian safety is difficult. Practical Applications: As pedestrian safety is frequently used to justify new stop sign installations, this result has important policy implications. Policies implementing stop signs to reduce pedestrian crashes may be less effective than other interventions. Enforcement and education efforts, along with geometric design considerations, should accompany any changes in traffic control.     

交通标志语义认知与情绪加工的脑电研究

为了让驾驶员更正确快速地理解交通标志,避免不当驾驶行为,探究驾驶员对交通标志的语义认知和情绪加工过程。采用刺激1-刺激2(S1-S2)试验范式,基于事件相关电位(ERPs)技术和脑电信号(EEG)时频(TF)分析方法,研究交通标志语义一致性和情绪唤醒的客观评价指标。结果显示,交通标志和文字语义不一致条件下,同一时间窗表现出更大的N400和θ波活动,证实了交通标志和文字之间存在较大的语义距离;交通标志和文字语义一致条件下,诱发更大的晚期正成分(LPP);N400、θ波和LPP可以用于判断交通标志的语义距离和情绪唤醒。ERPs结合EEG的TF分析结果能从神经科学的视角解释行为数据,可用于测量交通标志理解度。     

Adaptive response criteria in road hazard detection among older drivers

Objectives: The majority of existing investigations on attention, aging, and driving have focused on the negative impacts of age-related declines in attention on hazard detection and driver performance. However, driving skills and behavioral compensation may accommodate for the negative effects that age-related attentional decline places on driving performance. In this study, we examined an important question that had been largely neglected in the literature linking attention, aging, and driving: can top-down factors such as behavioral compensation, specifically adaptive response criteria, accommodate the negative impacts from age-related attention declines on hazard detection during driving?

Methods: In the experiment, we used the Drive Aware Task, a task combining the driving context with well-controlled laboratory procedures measuring attention. We compared younger (n = 16, age 21–30) and older (n = 21, age 65–79) drivers on their attentional processing of hazards in driving scenes, indexed by percentage of correct responses and reaction time of hazard detection, as well as sensitivity and response criteria using signal detection analysis.

Results: Older drivers, in general, were less accurate and slower on the task than younger drivers. However, results from this experiment revealed that older, but not younger, drivers adapted their response criteria when the traffic condition changed in the driving scenes. When there was more traffic in the driving scene, older drivers became more liberal in their responses, meaning that they were more likely to report that a driving hazard was detected.

Conclusions: Older drivers adopt compensatory strategies for hazard detection during driving. Our findings showed that, in the driving context, even at an older age our attentional functions are still adaptive according to environmental conditions. This leads to considerations on potential training methods to promote adaptive strategies that may help older drivers maintain performance in road hazard detection.     

Are older drivers’ driving patterns during an on-road driving task representative of their real-world driving patterns?

Abstract

Objective: The current study investigated whether older drivers’ driving patterns during a customized on-road driving task were representative of their real-world driving patterns.

Methods: Two hundred and eight participants (male: 68.80%; mean age?=?81.52 years, SD?=?3.37 years, range?=?76.00–96.00 years) completed a customized on-road driving task that commenced from their home and was conducted in their own vehicle. Participants’ real-world driving patterns for the preceding 4-month period were also collected via an in-car recording device (ICRD) that was installed in each participant’s vehicle.

Results: During the 4-month period prior to completing the on-road driving task, participants’ median real-world driving trip distance was 2.66?km (interquartile range [IQR]?=?1.14–5.79?km) and their median on-road driving task trip distance was 4.41?km (IQR?=?2.83–6.35?km). Most participants’ on-road driving task trip distances were classified as representative of their real-world driving trip distances (95.2%, n?=?198).

Conclusions: These findings suggest that most older drivers were able to devise a driving route that was representative of their real-world driving trip distance. Future research will examine whether additional aspects of the on-road driving task (e.g., average speed, proportion of trips in different speed zones) are representative of participants’ real-world driving patterns.     

Distracted walking: cell phones increase injury risk for college pedestrians

Introduction

Distraction on cell phones jeopardizes motor-vehicle driver safety, but few studies examine distracted walking. At particular risk are college students, who walk frequently in and near traffic, have increased pedestrian injury rates compared to other age groups, and frequently use cell phones. Method: Using an interactive and immersive virtual environment, two experiments studied the effect of cell phone conversation on distraction of college student pedestrians. In the first, we examined whether pedestrians would display riskier behavior when distracted by a naturalistic cell phone conversation than when undistracted. We also considered whether individual difference factors would moderate the effect of the distraction. In a second experiment, we examined the impact of three forms of distraction on pedestrian safety: (a) engaging in a cell phone conversation, (b) engaging in a cognitively challenging spatial task by phone, and (c) engaging in a cognitively challenging mental arithmetic task by phone. Results: Results revealed that cell phone conversations distracted college pedestrians considerably across all pedestrian safety variables measured, with just one exception. Attention to traffic was not affected by the naturalistic phone conversation in Experiment 1, but was altered by the cognitively-demanding content of some types of conversation in Experiment 2. The content of the conversation did not play a major role in distraction across other variables; both mundane and cognitively complex conversations distracted participants. Moreover, no significant associations between individual difference factors and susceptibility to distraction emerged. Impact on Industry: Results may inform researchers, policy makers, and pedestrians themselves. Educational campaigns might discourage telephone conversations in pedestrian environments.     

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

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

Two-way left turn lane or raised median? A truck safety based study

Introduction: Due to their size and weight, trucks require more space and time to make left turns when exiting or entering a roadway. Therefore, appropriate median treatments are critical for roadways with substantial truck traffic. The two-way left-turn lane (TWLTL) and raised median (RM) are the two types of median most commonly used to improve roadway mobility and manage roadway accessibility. However, previous studies on these median treatments have focused primarily on the general traffic conditions and geometric roadway features without considering the truck traffic impact. Method: To fill this gap, this study investigates the truck impacts on TWLTL and RM by considering two major influencing factors – truck percentage and roadway access point density. First, a negative binomial regression is developed to analyze the relationship between crash frequency and various influencing factors. Next, the crash rate difference analysis between the TWLTL and RM is conducted to identify critical points for these two factors. Results: The findings indicate that, compared with RM, TWLTL has significantly higher crash frequency, especially for roadways with a higher percentage of trucks. This suggests that the percentage of trucks should be taken into consideration when selecting an appropriate type of roadway median.     

Using European naturalistic driving data to assess secondary task engagement when stopped at a red light

Problem: Some evidence exists that drivers choose to engage in secondary tasks when the driving demand is low (e.g., when the car is stopped). While such a behavior might generally be considered as rather safe, it could be argued that the associated diversion of attention away from the road still leads to a reduction of situational awareness, which might increase collision risk once the car regains motion. This is especially relevant for texting, which is associated with considerable eyes-off-the-road-time. Nonetheless, it seems that previous research has barely addressed the actual engagement in secondary tasks while waiting at a red light (as compared to just addressing the tasks’ mere prevalence). Objective: The present study investigated secondary task engagement while stopped at a red light using European naturalistic driving data collected through the UDRIVE project. Attention was given to the whole engagement process, including simple prevalence and the tasks’ relation (in terms of start/end) to the red light period. Moreover, given that texting is one of the most problematic forms of distraction, it was characterized in more detail regarding glance behavior. Method: Videos of 804 red light episodes from 159 drivers were annotated. Glance behavior was also coded for a sub-set of 75 texting events and their matched baselines. Results, conclusions and practical applications: Drivers engaged in at least one secondary task across almost half of the annotated red light episodes. Drivers who texted while stopped spent most of the time looking at their cell phone. Consequently, drivers might not have been prepared for potentially unexpected events once the light turned green. Further, drivers concluded texting a considerable number of times well after the red light period, which has potential implications for traffic safety.     

Behavior and lifestyle characteristics of male Kuwaiti drivers

Introduction: The high traffic accident risk among young drivers is a well-known and well-documented fact in most countries. Lifestyle has proven to affect driving behavior as well as accident risk. This study covers the lifestyle component of the problems related to young male Kuwaiti drivers’ accident risk. Methods: The purpose of the study is to measure the relationship between lifestyle and accident risk. Lifestyle is measured through a questionnaire, where 302 male Kuwaiti drivers (mean age = 28 years; range 25-35 years) answer 39 questions related to behavioral and social factors, road conditions, police enforcement, and life satisfaction. They also report their involvement in accidents and traffic violations. Results: The questionnaire's validity and reliability (Cronbach's alpha = 0.7) were achieved. Principal component analysis reduced the 39 items on the questionnaire to 5 factors. Inadequate police enforcement is strongly correlated (r = 0.862) to accident risk and traffic violations and is thus considered the best predictor of traffic accidents in Kuwait. Impact on Industry: As driving-related incidents (on-the-job and off-the-job) are a significant source of fatalities and lost-work-days, the study points to the importance of considering cultural factors in the design of comprehensive safety programs for industry.     

Risk perception and driving behaviour among adolescents in two Norwegian counties before and after a traffic safety campaign

A campaign to promote safe driving behaviour has been carried out among adolescents in two Norwegian counties since 1998. It focused on traffic accident risk perception. The aims of the present paper are to present the results of the evaluation of the effect of the campaign and to examine the association between risk perception and traffic behaviour. A representative sample of adolescents aged 18–24 years old in the two Norwegian counties participating in the campaign (experimental group), as well as a control group of adolescents in two other counties not participating in the campaign were used. Data were collected before it started in October 1998 and also after the campaign (n=4376). The pre-and post-samples consisted of independent groups of respondents. The response rate was 93%. The respondents perceived the risk to be higher after the campaign than before. The respondents of the post-sample of the experimental group also reported less risk behaviour in traffic. The number of speeding accidents was also reduced. Perceived risk was not changed among adolescents in the control group. Neither were there any changes in self-report risk behaviour. The questionnaire measured cognition-based as well as emotion-based risk perception. Model tests showed that assessments of the probability of traffic accidents and concern were non-significant predictors for self-report risk behaviour. Worry and other emotional reactions related to traffic hazards significantly predicted behaviour. Sensation seeking, normlessness (i.e. a state where social norms no longer regulate behaviour), and indifference with regard to traffic safety affected emotion-based risk perception.     

Stability and safety evaluation of mixed traffic flow with connected automated vehicles on expressways

Introduction: Connected automated vehicles (CAVs) technology has deeply integrated advanced technologies in various fields, providing an effective way to improve traffic safety. However, it would take time for vehicles on the road to vehicles from human-driven vehicles (HDVs) progress to CAVs. Moreover, the Cooperative Adaptive Cruise Control (CACC) vehicle would degrade into the Adaptive Cruise Control (ACC) vehicle due to communication failure. Method: First, the different car-following models are used to capture characteristics of different types of vehicles (e.g., HDVs, CACC, and ACC). Second, the stability of mixed traffic flow is analyzed under different penetration rates of CAVs. Then, multiple safety measures, such as standard deviation of vehicle speed (SD), time exposed rear-end crash risk (TER), time exposed time-to-collision (TET), and time-integrated time-to-collision (TIT) are used to evaluate the safety of mixed traffic flow on expressways. Finally, the sensitivity of traffic demand, the threshold of time-to-collision (TTC), and the parameters of car-following models are analyzed based on a numerical simulation. Results: The results show that the ACC vehicle has no significant impact on the SD of mixed traffic flows, but it leads to the deterioration of TET and TIT, making the reduction proportion of TER slower. When the penetration rate exceeds 50%, the increase of CACC vehicles reduces traffic safety risks significantly. Furthermore, the increase in traffic demand and car-following parameters worsens traffic safety on expressways. Conclusions: This paper suggests that the CACC vehicles degenerate into ACC vehicles due to communication failure, and the safety risk of mixed traffic flow increases significantly. Practical Applications: The application of CAVs can improve the stability and safety of traffic flow.     

Analysis of motor-vehicle crashes at stop signs in four US cities

Problem: Nearly 700,000 police-reported motor vehicle crashes occur annually at stop signs, and approximately one-third of these crashes involve injuries. The purpose of this study was to develop a better understanding of the crashes that occur at stop signs and to identify potential countermeasures. Method: Police reports of crashes at stop sign-controlled intersections during 1996–2000 in four U.S. cities were examined in detail. At total of 1,788 crash reports for intersections with two-way stop signs were included in the study. Results: Stop sign violations accounted for about 70% of all crashes. Typically these crashes were angular collisions. Among crashes not involving stop violations, rear-end crashes were most common, accounting for about 12% of all crashes. Stop sign violation crashes were classified into several subtypes — driver stopped, driver did not stop, snow/wet/ice, and other/unknown. In about two-thirds of stop sign violation crashes, drivers said they had first come to a stop. In these cases, inability or failure to see approaching traffic often was cited as the cause of the crash. Drivers younger than 18 as well as drivers 65 and older were disproportionately found to be at fault in crashes at stop signs. Impact on industry: Potential countermeasures include changing traffic control and intersection design, improving intersection sight distance, and increasing conspicuity of stop signs through supplemental pavement markings and other devices.     

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.