Analysis of the injury severity of crashes by considering different lighting conditions on two-lane rural roads

IntroductionMany studies have examined different factors contributing to the injury severity of crashes; however, relatively few studies have focused on the crashes by considering the specific effects of lighting conditions. This research investigates lighting condition differences in the injury severity of crashes using 3-year (2009–2011) crash data of two-lane rural roads of the state of Washington.MethodSeparate ordered-probit models were developed to predict the effects of a set of factors expected to influence injury severity in three lighting conditions; daylight, dark, and dark with street lights. A series of likelihood ratio tests were conducted to determine if these lighting condition models were justified.ResultsThe modeling results suggest that injury severity in specific lighting conditions are associated with contributing factors in different ways, and that such differences cannot be uncovered by focusing merely on one aggregate model. Key differences include crash location, speed limit, shoulder width, driver action, and three collision types (head-on, rear-end, and right-side impact collisions).Practical ApplicationsThis paper highlights the importance of deploying street lights at and near intersections (or access points) on two-lane rural roads because injury severity highly increases when crashes occur at these points in dark conditions.     

Analysis of the traffic injury severity on two-lane,two-way rural roads based on classification tree models

Two-lane, two-way roads constitute a major portion of the rural roads in most countries of the world. This study identifies the factors influencing crash injury severity on these roads in Iran. Classification and regression trees (CART), which is one of the most common methods of data mining, was employed to analyze the traffic crash data of the main two-lane, two-way rural roads of Iran over a 3-year period (2006–2008). In the analysis procedure, the problem of three-class prediction was decomposed into a set of binary prediction models, which resulted in a higher overall accuracy of the predictions of the model. In addition, the prediction accuracy of the fatality class, which was nearly 0% in some of the previous studies, increased significantly. The results indicated that improper overtaking and not using a seatbelt are the most important factors affecting the severity of injuries.     

Investigation of speed and trajectory of motorcycle riders at curved road sections of two-lane rural roads under diverse lighting conditions

Introduction: Vehicular accidents at horizontal curves are over-represented compared to accidents that occur at tangent sections. Investigations have been conducted aimed at identifying the major causes that result in higher accident risk, both in terms of severity and rate, at curved road sections. Excessive or abrupt changes in speeding and improper vertical position are cited as major factors of lane departure, whereas other factors (either human or environmental) have also been documented. However, most research involves 4-wheel vehicles rather than other modes of transport that behave differently. More specifically, while motorcyclist fatalities occur more frequently than passenger vehicles, when accounting for vehicle distance traveled only a limited number of research studies address their behavior at curved road sections. Method: This paper presents the findings of field operational tests carried out by motorcyclists along two-lane rural roads with a wide range of horizontal curves using an instrumented motorcycle. Key objectives of the research included the conditions under which the motorcyclists differentiate their trajectory in regards to the direction of the horizontal curves, the correlation between the trajectory and the geometry of the road, and the impact of the lighting conditions on riders’ behavior. Results: The research showed that motorcyclists tend to ride closer to the centerline of the road, neglect the hazards associated with dim lighting conditions, and maintain constant speed in the left hand and the right-hand horizontal curves.     

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.     

Identifying crash type propensity using real-time traffic data on freeways

Introduction: We examine the effects of various traffic parameters on type of road crash. Method: Multivariate probit models are specified on 4-years of data from the A4-A86 highway section in the Ile-de-France region, France. Results: Empirical findings indicate that crash type can almost exclusively be defined by the prevailing traffic conditions shortly before its occurrence. Rear-end crashes involving two vehicles were found to be more probable for relatively low values of both speed and density, rear-end crashes involving more than two vehicles appear to be more probable under congested conditions, while single-vehicle crashes appear to be largely geometry-dependent. Impact on Industry: Results could be integrated in a real-time traffic management application.     

Assessment of temporal stability in risk factors of crashes at horizontal curves on rural two-lane undivided highways

Introduction: Safety of horizontal curves on rural two-lane, two-way undivided roadways is not fully explored. This study investigates factors that impact injury severity of such crashes. Method: To achieve the aim of this paper, issues associated with police-reported crash data such as unobserved heterogeneity and temporal stability need to be accounted for. Hence, a mixed logit model was estimated, while heterogeneity in means and variances is investigated by considering four injury severity outcomes for drivers: severe injury, moderate injury, possible injury, and no injury. Crash data for the period between 2011 and 2016 for crashes that occurred in the state of Oregon was analyzed. Temporal stability in factors determining the injury severity was investigated by identifying three time periods through splitting crash data into 2011–2012, 2013–2014, and 2015–2016. Results: Despite some factors affecting injuries in all specified time periods, the values of the marginal effects showed relative differences. The estimation results revealed that some factors increased the risk of being involved in severe injury crashes, including head-on collisions, drunk drivers, failure to negotiate curves, older drivers, and exceeding the speed limits. Conclusions: The hypothesis that attributes of injury severity are temporally stable is rejected. For example, young drivers (30 years old and younger) and middle-aged drivers were found to be temporally instable over time. Practical applications: The findings could help transportation authorities and safety professionals to enhance the safety of horizontal curves through appropriate and effective countermeasures.     

山区双车道公路摩托车事故严重度分析模型比较

山区双车道公路摩托车交通事故频发且死亡人数较多,准确认识不同类别事故严重度影响因素的作用规律是遏制事故发生的重要前提.针对这一问题,基于云南典型双车道公路522起摩托车交通事故数据,通过描述性统计分析时空分布特征、事故涉及交通方式分布特征、事故形态特征,并以3分类的事故严重度为因变量,将其分为仅财产损失、受伤、死亡事故3个等级,从人、车、路和行车环境4个方面选择了 14个潜在影响因素,分别采用有序Logit模型和多项Logit模型建立摩托车事故严重度分析模型,从预测准确率和自变量作用强度两个方面比较分析了两个摩托车事故严重度分析模型的适用范围,筛选重要影响因素.结果表明:仅财产损失事故受涉事者和肇事者年龄的影响较大,受伤事故受事故形态和事故发生季节的影响较大,死亡事故受事故发生季节和肇事者交通方式的影响较大;对3类事故严重度的预测能力,有序Logit模型的预测准确率为84.7％,多项Logit模型的准确率为92.4％,基于多项Logit模型摩托车事故严重度分析模型的预测准确率高且误报率较低,更适用于多分类的交通事故严重度的预测.     

Are sociodemographic characteristics and attitude good predictors of speeding behavior among drivers on Malaysia federal roads?

Objective: The increasing number of road crashes and fatalities on Malaysian federal roads is a cause for concern. The main causes of road crashes and fatalities on federal roads have been attributed to the speeding behavior among drivers. As such, this research investigates the possible predictors from sociodemographic characteristics and attitudes in predicting speeding behavior among drivers on Malaysia federal roads.

Methods: A face-to-face survey was conducted via purposive sampling on 300 drivers at rest areas at 6 crash hotspots on Malaysian federal roads. A set of questions related to speeding behavior was developed. The questionnaire consisted of 10 questions related to sociodemographic characteristics of the participants, 37 questions regarding attitudes toward speeding, and 1 question regarding speeding behavior. Subsequently, the sociodemographics were analyzed using binary logistic regression and the attitude variable was analyzed using factor analysis and binary logistic regression.

Results: The findings from the sociodemographic data revealed that male gender, single/separated status, broad driving experience, crash experience, and leisure/vacation trip purposes emerged as significant predictors for speeding behavior. Additionally, the results of factor analysis for the attitude variable revealed 3 components: Affective, behavioral, and cognitive. These 3 components are originally derived from the ABC model of attitude (affective, behavioral, and cognitive) that was adapted in this study. Furthermore, the findings from binary logistic regression appeared consistent with the model assumption, signifying that behavioral aspects significantly influenced speeding behavior among drivers. Nevertheless, affective and cognitive components were insignificant predictors. Furthermore, strong agreement with speeding countermeasures was observed among the participants.

Conclusion: In conclusion, sociodemographic characteristics and attitude have been proven to affect speeding behavior among drivers on Malaysian federal roads. The findings have important implications in designing driver risk profiles on federal roads to develop suitable countermeasures based on the 4E approach (engagement, education, enforcement, and engineering) to enhance road safety.     

Influence of built environment and roadway characteristics on the frequency of vehicle crashes caused by driver inattention: A comparison between rural roads and urban roads

Introduction: With prevalent and increased attention to driver inattention (DI) behavior, this research provides a comprehensive investigation of the influence of built environment and roadway characteristics on the DI-related vehicle crash frequency per year. Specifically, a comparative analysis between DI-related crash frequency in rural road segments and urban road segments is conducted. Method: Utilizing DI-related crash data collected from North Carolina for the period 2013–2017, three types of models: (1) Poisson/negative binomial (NB) model, (2) Poisson hurdle (HP) model/negative binomial hurdle (HNB) model, and (3) random intercepts Poisson hurdle (RIHP) model/random intercepts negative binomial hurdle (RIHNB) model, are applied to handle excessive zeros and unobserved heterogeneity in the dataset. Results: The results show that RIHP and RIHNB models distinctly outperform other models in terms of goodness-of-fit. The presence of commercial areas is found to increase the probability and frequency of DI-related crashes in both rural and urban regions. Roadway characteristics (such as non-freeways, segments with multiple lanes, and traffic signals) are positively associated with increased DI-related crash counts, whereas state-secondary routes and speed limits (higher than 35 mph) are associated with decreased DI-related crash counts in rural and urban regions. Besides, horizontal curved and longitudinal bottomed segments and segments with double yellow lines/no passing zones are likely to have fewer DI-related crashes in urban areas. Medians in rural road segments are found to be effective to reduce DI-related crashes. Practical Applications: These findings provide a valuable understanding of the DI-related crash frequency for transportation agencies to propose effective countermeasures and safety treatments (e.g., dispatching more police enforcement or surveillance cameras in commercial areas, and setting more medians in rural roads) to mitigate the negative consequences of DI behavior.     

采用遗传算法优化气体探测器布局策略研究

石油石化装置具有结构复杂且危险性高的特点,所加工物料多为易燃易爆有毒物质,且工艺单元之间集成度高,一旦发生泄漏若无法及时探测到则易形成气液积聚和火灾爆炸后果强化,装置拥塞度高使人员逃生困难。火气系统FGS作为安全关键系统,其中的气体探测网络如何快速可靠的实现对气体泄漏事件的探测显得尤为重要。已知探测时间,通过引入遗传算法利用其全局搜索的特点克服传统分支定界法的缺点,实现立体空间不同高度下设置探测网络达到场景全覆盖和缩短探测时间,同时求出探测时间附近的多组最优解,为探测器放置提供多种布置方案。通过与传统等间距探测器布置方案比较,从多种布置方案中选择更符合实际的最佳方案。通过海上浮式生产储油船的生产夹板气体探测案例,验证了所提方法的有效性。     

Identifying the impact of the COVID-19 pandemic on driving behavior using naturalistic driving data and time series forecasting

Introduction: COVID-19 has disrupted daily life and societal flow globally since December 2019; it introduced measures such as lockdown and suspension of all non-essential movements. As a result, driving activity was also significantly affected. Still, to-date, a quantitative assessment of the effect of COVID-19 on driving behavior during the lockdown is yet to be provided. This gap forms the motivation for this paper, which aims at comparing observed values concerning three indicators (average speed, speeding, and harsh braking), with forecasts based on their corresponding observations before the lockdown in Greece. Method: Time series of the three indicators were extracted using a specially developed smartphone application and transmitted to a back-end platform between 01/01/2020 and 09/05/2020, a time period containing normal operations, COVID-19 spreading, and the full lockdown period in Greece. Based on the collected data, XGBoost was employed to identify the most influential COVID-19 indicators, and Seasonal AutoRegressive Integrated Moving Average (SARIMA) models were developed for obtaining forecasts on driving behavior. Results: Results revealed the intensity of the impact of COVID-19 on driving, especially on average speed, speeding, and harsh braking per 100 km. More specifically, speeds were found to increase by 2.27 km/h on average compared to the forecasted evolution, while harsh braking/100 km increased to almost 1.51 on average. On the bright side, road crashes in Greece were reduced by 49% during the months of COVID-19 compared to the non-COVID-19 period.     

A method for simplifying the analysis of traffic accidents injury severity on two-lane highways using Bayesian networks

Introduction

This study describes a method for reducing the number of variables frequently considered in modeling the severity of traffic accidents. The method's efficiency is assessed by constructing Bayesian networks (BN).

Method

It is based on a two stage selection process. Several variable selection algorithms, commonly used in data mining, are applied in order to select subsets of variables. BNs are built using the selected subsets and their performance is compared with the original BN (with all the variables) using five indicators. The BNs that improve the indicators’ values are further analyzed for identifying the most significant variables (accident type, age, atmospheric factors, gender, lighting, number of injured, and occupant involved). A new BN is built using these variables, where the results of the indicators indicate, in most of the cases, a statistically significant improvement with respect to the original BN.

Conclusions

It is possible to reduce the number of variables used to model traffic accidents injury severity through BNs without reducing the performance of the model.

Impact on Industry

The study provides the safety analysts a methodology that could be used to minimize the number of variables used in order to determine efficiently the injury severity of traffic accidents without reducing the performance of the model.     

Risk and safety perception on urban and rural roads: Effects of environmental features,driver age and risk sensitivity

Objective: The ability to detect changing visual information is a vital component of safe driving. In addition to detecting changing visual information, drivers must also interpret its relevance to safety. Environmental changes considered to have high safety relevance will likely demand greater attention and more timely responses than those considered to have lower safety relevance. The aim of this study was to explore factors that are likely to influence perceptions of risk and safety regarding changing visual information in the driving environment. Factors explored were the environment in which the change occurs (i.e., urban vs. rural), the type of object that changes, and the driver's age, experience, and risk sensitivity.

Methods: Sixty-three licensed drivers aged 18–70 years completed a hazard rating task, which required them to rate the perceived hazardousness of changing specific elements within urban and rural driving environments. Three attributes of potential hazards were systematically manipulated: the environment (urban, rural); the type of object changed (road sign, car, motorcycle, pedestrian, traffic light, animal, tree); and its inherent safety risk (low risk, high risk). Inherent safety risk was manipulated by either varying the object's placement, on/near or away from the road, or altering an infrastructure element that would require a change to driver behavior. Participants also completed two driving-related risk perception tasks, rating their relative crash risk and perceived risk of aberrant driving behaviors.

Results: Driver age was not significantly associated with hazard ratings, but individual differences in perceived risk of aberrant driving behaviors predicted hazard ratings, suggesting that general driving-related risk sensitivity plays a strong role in safety perception. In both urban and rural scenes, there were significant associations between hazard ratings and inherent safety risk, with low-risk changes perceived as consistently less hazardous than high-risk impact changes; however, the effect was larger for urban environments. There were also effects of object type, with certain objects rated as consistently more safety relevant. In urban scenes, changes involving pedestrians were rated significantly more hazardous than all other objects, and in rural scenes, changes involving animals were rated as significantly more hazardous. Notably, hazard ratings were found to be higher in urban compared with rural driving environments, even when changes were matched between environments.

Conclusion: This study demonstrates that drivers perceive rural roads as less risky than urban roads, even when similar scenarios occur in both environments. Age did not affect hazard ratings. Instead, the findings suggest that the assessment of risk posed by hazards is influenced more by individual differences in risk sensitivity. This highlights the need for driver education to account for appraisal of hazards’ risk and relevance, in addition to hazard detection, when considering factors that promote road safety.     

Investigation of hit-and-run crash occurrence and severity using real-time loop detector data and hierarchical Bayesian binary logit model with random effects

Objective: Most of the extensive research dedicated to identifying the influential factors of hit-and-run (HR) crashes has utilized typical maximum likelihood estimation binary logit models, and none have employed real-time traffic data. To fill this gap, this study focused on investigating factors contributing to HR crashes, as well as the severity levels of HR.

Methods: This study analyzed 4-year crash and real-time loop detector data by employing hierarchical Bayesian models with random effects within a sequential logit structure. In addition to evaluation of the impact of random effects on model fitness and complexity, the prediction capability of the models was examined. Stepwise incremental sensitivity and specificity were calculated and receiver operating characteristic (ROC) curves were utilized to graphically illustrate the predictive performance of the model.

Results: Among the real-time flow variables, the average occupancy and speed from the upstream detector were observed to be positively correlated with HR crash possibility. The average upstream speed and speed difference between upstream and downstream speeds were correlated with the occurrence of severe HR crashes. In addition to real-time factors, other variables found influential for HR and severe HR crashes were length of segment, adverse weather conditions, dark lighting conditions with malfunctioning street lights, driving under the influence of alcohol, width of inner shoulder, and nighttime.

Conclusions: This study suggests the potential traffic conditions of HR and severe HR occurrence, which refer to relatively congested upstream traffic conditions with high upstream speed and significant speed deviations on long segments. The above findings suggest that traffic enforcement should be directed toward mitigating risky driving under the aforementioned traffic conditions. Moreover, enforcement agencies may employ alcohol checkpoints to counter driving under the influence (DUI) at night. With regard to engineering improvements, wider inner shoulders may be constructed to potentially reduce HR cases and street lights should be installed and maintained in working condition to make roads less prone to such crashes.     

Investigating the effect of geometric dimensions of median traffic barriers on crashes: Crash analysis of interstate roads in Wyoming using actual crash datasets

IntroductionDespite the numerous safety studies done on traffic barriers’ performance assessment, the effect of variables such as traffic barrier’s height has not been identified considering a comprehensive actual crash data analysis. This study seeks to identify the impact of geometric variables (i.e., height, post-spacing, sideslope ratio, and lateral offset) on median traffic barriers’ performance in crashes on interstate roads.MethodGeometric dimensions of over 110 miles median traffic barriers on interstate Wyoming roads were inventoried in a field survey between 2016 and 2018. Then, the traffic barrier data collected was combined with historical crash records, traffic volume data, road geometric characteristics, and weather condition data to provide a comprehensive dataset for the analysis. Finally, an ordered logit model with random-parameters was developed for the severity of traffic barrier crashes. Based on the results, traffic barrier’s height was found to impact crash severity.ResultsCrashes involving cable barriers with a height between 30″ and 42″ were less severe than other traffic barrier types, while concrete barriers with a height shorter than 32″ were more likely involved with severe injury crashes. As another important finding, the post-spacing of 6.1–6.3 ft. was identified as the least severe range in W-beam barriers.Practical applicationsThe results show that using flare barriers should reduce the number of crashes compared to parallel barriers.     

On the development of a new hybrid risk assessment process using occupational accidents’ data: Application on the Greek Public Electric Power Provider

The occupational accidents have a major impact upon human integrity and also bring about high costs for the social health and insurance system of a country. In addition, risk analysis is an essential process for the safety policy of a company, having as main aim the effacement of any potential of damage in a productive procedure, while the quantified risk evaluation is the most crucial part of the whole procedure of assessing hazards in the work. The main goal of this study is double: a) the development and presentation of a new hybrid risk assessment process (HRAP) and b) the application of HRAP in the Greek Public Power Corporation (PPC) (the unique electric power provider and the largest industry in Greece), by using occupational accidents that have been recorded, during the 12-year period of 1993-2004. The new process consists of four distinct phases a) the hazard sources’ identification phase, b) the risk consideration phase, c) the risk-evaluation phase, and d) the phase of the risk assessment and safety-related decision making. The results show that in some cases the risk value has been calculated in PPC to be higher than 500 (in the risk rating of 0-1000), which imposes the taking of suppressive measures for abolishing the danger source, while the fatal accident frequency rate (per 10⁸ man-hrs) is FAFR ≅ 2.4.