Safety evaluation of drowsy driving advisory system: Alabama case study

Introduction: The present study discusses roles, characteristics, and safety assessment of a drowsy driving advisory (DDA) system, implemented on rural interstates of Alabama. The DDA system is an engineering countermeasure designed to reduce the likelihood of drowsy driving crashes. It consists of a series of roadside signs with warning and advisory messages for drowsy drivers. The DDA system was implemented upstream of rural rest areas based on a comprehensive crash analysis. Method: A post-implementation study was conducted three years after the DDA system implementation to assess its safety effects. An empirical bayes (EB) method along with predictive methods of the Highway Safety Manual was used in the safety assessment. To overcome the underreported issue of drowsy driving crashes in the crash analysis, the present study used a concept called, expanded definition of drowsy driving (EDD) crashes. Result: The analysis found that the DDA system could reduce total and EDD crashes by 64% and 49%, respectively. It is important to note that such huge crash reduction effects are due to a combined effect of both rest areas and the DDA system, not because of a single treatment. The safety effect of a rest area itself, without considering the effect of the DDA system, was also investigated. Results show that total and EDD crashes would increase about 12–45% and 5–33%, respectively if there is no presence of a rest area. Conclusion: Our findings conclude that the DDA system could significantly reduce both total and drowsy driving crashes when it cooperates with a rest area facility. Practical Application: The findings also provide the guidance of using the DDA system on high-speed roads as a safety countermeasure of drowsy driving crashes. Readers can find details of the DDA system used in this study with its layout, dimension, and roadside safety messages.     

全自动驾驶模式下站台门智能安全监控系统研究

全自动驾驶是一种先进的轨道交通列车运行控制技术,在全自动驾驶模式下,轨道交通列车运维对站台门的安全提出了更高的要求。本文在分析站台门在全自动驾驶模式下的安全监控需求的基础上,从站台门的运行外部环境和站台门自身的运行参数两方面出发,建立站台门智能安全监控系统,实时全面掌握站台门的安全状态,基于BIM技术和自适应学习技术,对站台门的安全状态进行预测,防范于未然并及时做好维修计划调整。该系统能让站台门的安全处于主动控制状态,减少安全隐患。     

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.     

Spatial relationships between alcohol outlet densities and drunk driving crashes: An empirical study of Tianjin in China

Introduction: Numerous studies have demonstrated the close relationship between alcohol availability and alcohol-related crashes. However, there is still a lack of spatial empirical analysis regarding this relationship, particularly in large cities of developing countries. Differences in alcohol outlets and drinking patterns in these cities may lead to quite different patterns of crash outcomes. Method: 3356 alcohol-related crashes were collected from the blood-alcohol test report of a forensic institution in Tianjin, China. Density of alcohol outlets such as retail locations, entertainment venues, restaurants, hotels, and companies were extracted based on 2114 Traffic Analysis Zones (TAZ) together with the residential and demographic characteristics. After applying the exploratory spatial data analysis, this research developed and compared the traditional Ordinary Least Square model (OLS), Spatial Lag Model (SLM), Spatial Error Model (SEM) and Spatial Durbin Model (SDM) to explore spatial effects of all the variables. Results: The results of incremental spatial autocorrelation show that the most significant distance threshold of alcohol-related roadway traffic crashes is 3 km. The SDM is found to be the optimal spatial model to characterize the relationship between alcohol outlets and crashes. The number of alcohol-involved traffic crashes is positively related to population density and retail density, but negatively related to the company density, hotel density, and residential density within the same TAZ. Meanwhile, dense population and hotels have reverse spillover effects in adjacent zones. Conclusions: The significant spatial direct effect and spillover effect of alcohol outlet densities on drunk driving crashes should not be neglected. These findings could help improve transportation planning, traffic law enforcement and traffic management for large cities in developing countries.     

Classification of driving workload affected by highway alignment conditions based on classification and regression tree algorithm

Objective: Guaranteeing a safe and comfortable driving workload can contribute to reducing traffic injuries. In order to provide safe and comfortable threshold values, this study attempted to classify driving workload from the aspects of human factors mainly affected by highway geometric conditions and to determine the thresholds of different workload classifications. This article stated a hypothesis that the values of driver workload change within a certain range.

Methods: Driving workload scales were stated based on a comprehensive literature review. Through comparative analysis of different psychophysiological measures, heart rate variability (HRV) was chosen as the representative measure for quantifying driving workload by field experiments. Seventy-two participants (36 car drivers and 36 large truck drivers) and 6 highways with different geometric designs were selected to conduct field experiments. A wearable wireless dynamic multiparameter physiological detector (KF-2) was employed to detect physiological data that were simultaneously correlated to the speed changes recorded by a Global Positioning System (GPS) (testing time, driving speeds, running track, and distance). Through performing statistical analyses, including the distribution of HRV during the flat, straight segments and P-P plots of modified HRV, a driving workload calculation model was proposed. Integrating driving workload scales with values, the threshold of each scale of driving workload was determined by classification and regression tree (CART) algorithms.

Results: The driving workload calculation model was suitable for driving speeds in the range of 40 to 120 km/h. The experimental data of 72 participants revealed that driving workload had a significant effect on modified HRV, revealing a change in driving speed. When the driving speed was between 100 and 120 km/h, drivers showed an apparent increase in the corresponding modified HRV. The threshold value of the normal driving workload K was between ?0.0011 and 0.056 for a car driver and between ?0.00086 and 0.067 for a truck driver.

Conclusion: Heart rate variability was a direct and effective index for measuring driving workload despite being affected by multiple highway alignment elements. The driving workload model and the thresholds of driving workload classifications can be used to evaluate the quality of highway geometric design. A higher quality of highway geometric design could keep driving workload within a safer and more comfortable range. This study provided insight into reducing traffic injuries from the perspective of disciplinary integration of highway engineering and human factor engineering.     

Prevalence of alcohol impairment and odds of a driver injury or fatality in on-road farm equipment crashes

Objective: The objective of this article was to estimate the prevalence of alcohol impairment in crashes involving farm equipment on public roadways and the effect of alcohol impairment on the odds of crash injury or fatality.

Methods: On-road farm equipment crashes were collected from 4 Great Plains state departments of transportation during 2005–2010. Alcohol impairment was defined as an involved driver having blood alcohol content of ≥0.08 g/100 ml or a finding of alcohol impairment as a driver contributing circumstance recorded on the police crash report. Injury or fatality was categorized as (a) no injury (no and possible injury combined), (b) injury (nonincapacitating or incapacitating injury), and (c) fatality. Hierarchical multivariable logistic regression modeling, clustered on crash, was used to estimate the odds of an injury/fatality in crashes involving an alcohol-impaired driver.

Results: During the 5 years under study, 3.1% (61 of 1971) of on-road farm equipment crashes involved an alcohol-impaired driver. One in 20 (5.6%) injury crashes and 1 in 6 (17.8%) fatality crashes involved an alcohol-impaired driver. The non-farm equipment driver was significantly more likely to be alcohol impaired than the farm equipment driver (2.4% versus 1.1% respectively, P = .0012). After controlling for covariates, crashes involving an alcohol-impaired driver had 4.10 (95% confidence interval [CI], 2.30–7.28) times the odds of an injury or fatality. In addition, the non-farm vehicle driver was at 2.28 (95% CI, 1.92–2.71) times higher odds of an injury or fatality than the farm vehicle driver. No differences in rurality of the crash site were found in the multivariable model.

Conclusion: On-road farm equipment crashes involving alcohol result in greater odds of an injury or fatality. The risk of injury or fatality is higher among the non-farm equipment vehicle drivers who are also more likely to be alcohol impaired. Further studies are needed to measure the impact of alcohol impairment in on-road farm equipment crashes.     

What is the relationship between self-reported aberrant driving behaviors,mindfulness, and self-reported crashes and infringements?

Objectives: This study investigated the relationship between self-reported aberrant driving behaviors, mindfulness, and self-reported crashes and infringements.

Methods: Three hundred and eighteen participants (M = 46.0 years, SD = 13.7 years; female: 81.8%) completed an online survey that assessed aberrant driving behaviors, mindfulness (including regular mindfulness meditation [MM]), and self-reported crashes and infringements during the past 2 years. Structural equation modeling (SEM) was used to examine the relationship between self-reported aberrant driving behaviors and mindfulness simultaneously, as well as with participants' age and estimated kilometers driven over the past year.

Results: The results of the SEM showed that mindfulness was negatively related to each self-reported aberrant driving behavior, with the strongest relationships being between mindfulness and driving-related lapses (?0.58) and errors (?0.46). Participants who practice MM had significantly fewer crashes in the past 2 years and reported significantly fewer driving-related violations and lapses compared to participants who did not practice MM (crashes: 9.3% vs. 18.8%, P < .05; violations: M = 6.66 [SD = 3.44] vs. M = 7.68 [SD = 4.53], P < .05; errors: M = 5.17 [SD = 3.44] vs. M = 6.19 [SD = 4.12], P < .05).

Conclusions: More research is needed to understand whether MM results in more mindful and attentive drivers or whether individuals who practice MM may have other traits or behaviors that are linked to improved safety.     

Investigating factors influencing drivers' speed selection behavior under reduced visibility conditions

Objective: Driving speed is a major concern for driving safety under reduced visibility conditions. Many factors affect speed selection in low visibility, but few studies have been conducted examining drivers' characteristics, particularly in China. The present study aimed to investigate the correlation between drivers' demographic information, driving ability, and speed choice in low-visibility conditions using a sample of Chinese drivers.

Methods: A self-designed driving ability scale was used to assess driving ability in reduced visibility conditions. The reliability and validity of 306 gathered questionnaires were examined in this article, and a structural equation model (SEM) was built to explore the predictors of drivers' speed selection behavior under reduced visibility conditions and to measure the relationships between various factors.

Results: Age and driving experience have no direct relationship to speed selection behavior in reduced visibility, but the frequency of using expressways and annual mileage are significantly related to the speed on roads that have a speed restriction of 80 or 120 km/h. Under reduced visibility conditions, driving ability has a significant effect on speed selection behavior, and driving skill (DS) is the most influential on speed selection behavior on roads with a speed limit of 120 km/h; otherwise, the effect of risk perception (RP) does not differ by speed choice on 3 roads with different speed limits. Driving speed in good weather also has a positive influence on speed selection behavior in low visibility.

Conclusion: Driving ability is directly associated with speed selection in reduced visibility conditions, and some demographic data indirectly influence speed selection. This study provides useful recommendations for drivers' training programs to reduce casualties from accidents in low-visibility conditions.     

Developing a theoretical foundation to change road user behavior and improve traffic safety: Driving under the influence of cannabis (DUIC)

Objectives: This study explored a theoretical model to assess the influence of culture on willingness and intention to drive under the influence of cannabis (DUIC). This model is expected to guide the design of strategies to change future DUIC behavior in road users.

Methods: This study used a survey methodology to obtain a nationally representative sample (n = 941) from the AmeriSpeak Panel. Survey items were designed to measure aspects of a proposed definition of traffic safety culture and a predictive model of its relationship to DUIC.

Results: Although the percentage of reported past DUIC behaviors was relatively low (8.5%), this behavior is still a significant public health issue—especially for younger drivers (18–29 years), who reported more DUIC than expected. Findings suggest that specific cultural components (attitudes, norms) reliably predict past DUIC behavior, general DUIC willingness, and future DUIC intention. Most DUIC behavior appears to be deliberate, related significantly to willingness and intention. Intention and willingness both appear to fully moderate the relationship between traffic safety culture and DUIC behavior.

Conclusions: This study explored a theoretical model to understand road user behavior involving drug (cannabis)-impaired driving as a significant risk factor for traffic safety. By understanding the cultural factors that increase DUIC behavior, we can create strategies to transform this culture and sustain safer road user behavior.