Investigating fatal and injury crash patterns of teen drivers with unsupervised learning algorithms

Teenagers have been emphasized as a critical driver population class because of their overrepresentation in fatal and injury crashes. The conventional parametric approaches rest on few predefined assumptions, which might not always be valid considering the complicated nature of teen drivers' crash characteristics that are reflected by multidimensional crash datasets. Also, individual attributes may be more speculative when combined with other factors. This research employed joint correspondence analysis (JCA) and association rule mining (ARM) to investigate the fatal and injury crash patterns of at-fault teen drivers (aged 15 to 19 years) in Louisiana. The unsupervised learning algorithms can explore meaningful associations among crash categories without restricting the nature of variables. The analyses discover intriguing associations to understand the potential causes and effects of crashes. For example, alcohol impairment results in fatal crashes with passengers, daytimes severe collisions occur to unrestrained drivers who have exceeded the posted speed limits, and adverse weather conditions are associated with moderate injury crashes. The findings also reveal how the behavior patterns connected with teen driver crashes, such as distracted driving in the morning hours, alcohol intoxication or using cellphone in pickup trucks, and so on. The research results can lead to effectively targeted teen driver education programs to mitigate risky driving maneuvers. Also, prioritizing crash attributes of key interconnections can help to develop practical safety countermeasures. Strategy that covers multiple interventions could be more effective in curtailing teenagers' crash risk.     

Crash risk factors associated with injury severity of teen drivers

This paper focuses on identifying crash risk factors associated with injury severity of teen drivers. Crash data obtained from the Highway Safety and Information System (HSIS) for the entire state of North Carolina, for years 2011 to 2013, was used for analysis and modeling. Among all the crashes during the study period, a total of 62,990 crashes involving teen drivers (15 to 19?years) were analyzed. A partial proportionality odds model was developed to identify factors contributing to injury severity of teen drivers. The results obtained indicate that teen drivers driving sports utility vehicles and pickup trucks are more likely to be severely injured when compared to teen drivers driving passenger cars. Teen drivers are more likely to be severely injured on weekdays, particularly during peak hours. The chances of teen drivers getting involved in severe injury crashes on Tuesdays and Fridays is higher when compared to Sundays. Age, gender, road configuration, terrain, adverse weather condition, and access control are observed to have a significant effect on teen driver's injury severity.     

Assessment of the effectiveness of connected vehicle weather and work zone warnings in improving truck driver safety

Improving work zone safety remains a prime challenge for the transportation sector in the United States. In particular, the frequency and severity of work zone crashes involving large trucks in rural freeways are alarming. Lack of compliance with the instructions provided at work zones results in increased crash risk. In-vehicle advanced warning systems enabled by Connected Vehicle (CV) technology have the potential to prompt appropriate driver response, make navigation more predictable, and improve overall work zone safety. This study falls under the umbrella of the WYDOT Connected Vehicle Pilot Program and seeks to investigate the impacts of the Pilot's real-time weather and work zone notifications on the behavior of truck drivers in rural freeway work zone settings under poor visibility. Twenty professional truck drivers participated in this simulator study. The driving scenarios were designed to mimic the driving conditions experienced on Wyoming Interstate 80. Findings suggest that exposure to the CV notifications has promising safety benefits manifested in improved driver behavior and response. Furthermore, both the weather and work zone notifications acquired high approval from the participants in terms of usefulness and ease of understanding. Nonetheless, the display of multiple work zone warnings on the Human Machine Interface may had introduced little to moderate distraction for some participants. Overall, this study brings forth valuable lessons that are being funneled to support informed decision making to enhance the Pilot's existing Human Machine Interface design.     

Using on-board video data for safety analysis – An analysis of right hook crashes involving large buses at signalized intersections

Bus right hook (BRH) crashes at intersections are one of the most common types of crashes for bus carriers, which accounted for as high as 16% of fatal and injury crashes involving large buses at intersections in Taiwan. A BRH crash occurs when a bus and another vehicle traveling in the same direction head into an intersection, but the bus driver makes a right turn across the path of the through-moving vehicle, and both vehicles collide. This study responds to the research needs to identity factors associated with BRH crashes by utilizing in-vehicle data recorder (IVDR) data. A four step analysis procedure was developed, including (1) video data coding, (2) crash sequence analysis to identify crash contributing factors, (3) a case-control study to examine the relationship between the crash contributing factors and crash occurrence, and (4) modeling crash risk in terms of the crash contributing factors to better understand the crash generating process. This study first identified the existence of driver unattended time as the time between when the driver last checked the right back mirror to finally steering for a right turn, indicating the time period wherein the driver did not track the through vehicle on the right side using the right back mirror. It was found that BRH crashes could be attributed to the concurrence of unattended time and the speed difference between the bus and through vehicle. Several recommendations are discussed based on the results to further develop countermeasures to reduce this type of crash.     

Factors influencing crash involvement during early years of owning a new vehicle

Though automobile manufacturers are investing efforts to make newer vehicles safer to drive, an element of uncertainty with the new vehicle seems to persist with the drivers during the early years of ownership. This could be due to a lack of familiarity of the vehicle's power, dimensions or available technologies/features. While the uncertainty in itself is a potential cause of a crash, it is important for the policy-makers, practitioners, and automobile manufacturers to understand the factors that could further aggravate the problem. This research focuses on identifying the factors influencing the likelihood of getting involved in a crash and its severity when driving a new vehicle. Crash data for North Carolina for the years 2013 to 2018 (six years) was used develop partial proportionality odds models, compute the odds ratios, analyze the effects of explanatory variables, and identify factors influencing crashes by the age of the vehicle. The likelihood of getting involved in a severe or moderate injury crash when driving a new vehicle is less for drivers in the age group ≤19 years. Erratic driving behavior (like making wide turns, weaving and swerving in traffic, driving with headlights off, driving on center-line or lane-line, etc.) and speeding increase the risk of getting involved in a moderate injury crash when driving a new vehicle. Likewise, the odds of getting involved in a crash are high on weekends and in adverse weather conditions when driving a new vehicle. They are higher when driving a new motorcycle, heavy vehicle or farm machinery. The findings help policy-makers and practitioners formulate strategies to educate drivers on factors influencing crash risk when driving a new vehicle. Further, automobile manufacturers can establish guidance programs and documentation that explain what to expect when buying and driving a new vehicle.     

山区高速公路隧道路段与开放路段的事故影响因素分析

高速公路隧道构造特殊且通行环境复杂,因而通常事故多发。为探究高速公路隧道路段与开放路段事故影响因素和严重程度致因机理的差异,采集沪昆高速邵怀段2011—2016年期间1 537起事故为研究样本;以事故发生路段为响应变量构建逻辑回归模型,解释各种风险因素对事故发生路段倾向性的影响差异;分别针对隧道路段与开放路段建立模型研究事故伤害严重程度的影响因素。建立二元Logit回归模型分析事故的发生倾向性和2类路段的事故严重程度的影响因素;采用随机参数Logit模型以反映异质性条件对参数的影响。统计表明:与疲劳驾驶、未保持安全距离相关的事故发生在隧道路段的概率更高,其事故发生概率分别是开放路段的2.373和2.482倍;与隧道路段事故严重程度正相关的因素包括下坡（坡度2%以上）、夏季和超速行驶,其中下坡（坡度2%以上）段的严重事故发生的概率为上坡（坡度2%以上）的3.397倍,夏季的严重事故发生概率为秋季的3.951倍,超速行驶相关的严重事故发生概率为其他不当驾驶行为的4.242倍;与开放路段事故严重程度正相关的因素包括超速行驶和疲劳驾驶,其中超速行驶相关的严重事故概率是其他不当驾驶行为的2.713倍,疲劳驾驶相关的严重事故概率是其他不当驾驶行为的4.802倍。研究表明,山区高速公路隧道路段与开放路段的事故发生概率及其严重程度的影响因素存在一定的差异性,研究结论可为山区高速公路差异管理方案制定提供依据。      

考虑多模式失效概率的长下坡路段重型卡车事故预测模型

为挖掘多模式失效概率与长下坡路段重型卡车事故之间的关系，建立了重型卡车在长下坡路段的多模式失效概率与车辆事故之间的关系模型。并针对重型卡车在长下坡路段可能的失效模式，如侧滑、侧翻、视距不足、制动失效，在此基础上建立了多模式失效概率预测模型；通过蒙特卡罗法模拟并求解单模式失效的概率，宽界限法求解失效系统的多模式失效概率；将多模式失效概率作为解释变量与其他道路因素结合，分别建立泊松模型、随机效应泊松模型、随机参数泊松模型，将多模式失效概率与重型卡车事故建立函数关系；对比3种模型的拟合优度指标，优选出最优事故预测模型，用来挖掘重型卡车事故与多模式失效概率之间的关系。以华盛顿州71段长下坡10年的重型卡车事故数据及道路设计数据进行方法验证。结果表明:随机参数泊松模型与随机效应泊松模型的拟合优度相差较小，二者均优于泊松模型；当考虑多模式失效概率时，平曲线半径、纵坡坡度、超高对重型卡车事故的影响均不显著，即三者的影响被削弱，尤其是平曲线半径和超高，多模式失效概率的弹性(0.239)远大于二者的弹性(平曲线半径和超高的弹性分别仅为0.097和0.002)；重型卡车的事故与多模式失效概率近似线性关系，且截距不为0。即多模式失效概率可用于道路安全分析的表征指标，但与事故概率不等价。      

Macroscopic analysis on the frequency and severity of pedestrian crashes on National Roads in Metro Manila,Philippines

Pedestrians are the most vulnerable road users; thus, understanding the primary factors that lead to pedestrian crashes is a chief concern in road safety. However, owing to the limitations of crash data in developing countries, only a few studies have evaluated the comprehensive characteristics of pedestrian crashes, specifically on different road types. This study attempted to develop pedestrian crash frequency and severity models on national roads by using the road characteristics and built environment parameters, based on the road crash data (2016–2018) that involved pedestrians in Metro Manila, Philippines. Remarkable findings included primary roads, presence of footbridges, road sections with bad surface conditions, and increased fractions of commercial, residential, and industrial roads, which exhibited a greater likelihood of pedestrian crashes. Crashes involving elderly pedestrians, heavier vehicles, late-night hours, fair surface conditions, and open spaces were associated with increased likelihoods of fatal outcomes. Essentially, this study provides a macroscopic perspective in understanding the factors associated with the severity and frequency of pedestrian crashes, and it would aid the authorities in identifying proper countermeasures.     

Predicting downgrade crash frequency with the random-parameters negative binomial model: Insights into the impacts of geometric variables on downgrade crashes in Wyoming

Road deaths, injuries and property damage place a huge burden on the economy of most nations. Wyoming has a high crash rate on mountain passes. The crash rates observed in the state is as a result of many factors mainly related to the challenging mountainous terrain in the state, which places extra burden on drivers in terms of requiring higher levels of alertness and driving skill. This study was conducted to investigate factors leading to crashes on Wyoming downgrades, with a focus on geometric variables. Traditionally, crash frequency analysis is conducted using count models such as Poisson or negative binomial models. However, factors that affect crash frequency are known to vary across observations. The use of a methodology that fails to take into account heterogeneity in observed and unobserved effects relating to roadway characteristics can lead to biased and inconsistent estimates. Inferences made from such parameter estimates may be misleading. This study employed the random-parameters negative binomial regression models to evaluate the impact of geometric variables on crash frequency. Five separate models were estimated for total, fatal/injury, property damage only (PDO), truck, and non-truck crash frequencies. Several geometric and traffic variables were found to influence the frequency of crashes on downgrades. These included segment length, vertical grade, shoulder width, lane width, presence of downgrade warning sign, vertical curve length, presence of a passing lane, percentage of trucks, number of lanes and AADT. The results suggest that segment length, lane width, presence of a passing lane, presence of a downgrade warning sign, vertical grade, and percentage of trucks are best modeled as random parameters. The findings of this study will provide transportation agencies with a better understanding of the impact of geometric variables on downgrade crashes.     

Effects of head restraint and vehicle damage severity on neck injury risk

Domestic automobile insurance claims were investigated to correlate the driver neck injury risk with the safety rating of the head restraint, the severity of vehicle damage, and other human factors. The results of our statistical analysis reveal that the risk of neck injury for the driver is significantly different for vehicle size, use, driver gender, driver age, impact direction, accident location, and safety rating of head restraint, depending on vehicle the damage level which is assumed to imply impact severity during a rear-end crash accident. One of the unique findings from domestic insurance claims from low-speed rear-end crash accidents is the frequent reports of lower back injury together with whiplash. Thus, the risk of lower back discomfort is also included in this statistical analysis.     

Using machine leaning techniques for evaluation of motorcycle injury severity

There is a growing interest in the application of the machine learning techniques in predicting the motorcycle crash severity. This is partly due to a progress in autonomous vehicles technology, and machine learning technique, which as a main component of autonomous vehicle could be implemented for traffic safety enhancement. Wyoming's motorcycle crash fatalities constitute a concern since the count of riders being killed in motorcycle crashes in 2014 was 11% of the total road fatalities in the state. The first step of crash reduction could be achieved through identification of contributory factors to crashes. This could be accomplished by using a right model with high accuracy in predicting crashes. Thus, this study adopted random forest, support vector machine, multivariate adaptive regression splines and binary logistic regression techniques to predict the injury severity outcomes of motorcycle crashes. Even though researchers applied all the aforementioned techniques to model motorcycle injury severities, a comparative analysis to assess the predictive power of such modeling frameworks is limited. Hence, this study contributes to the road safety literature by comparing the performance of the discussed techniques. In this study, Wyoming's motorcycle crash injury severities are modeled as functions of the characteristics that give rise to crashes. Before conducting any analyses, feature reduction was used to identify a best number of predictors to be included in the model. Also to have an unbiased estimation of the performance of different machine learning techniques, 5-fold cross-validation was used for model performance evaluation. Two measure, Area under the curve (AUC), and confusion matrix were used to compare different models' performance. The machine learning results indicate that random forest model outperformed the other models with the least misclassification and higher AUC. It was also revealed that a dichotomous response variable, with fatality and incapacitation injury in one category, along with all other categories in another group would result in a lower misclassification rate than a polychotomous response variable. This might result from the nature of motorcycle crashes, lacking a protection compared with passenger cars, preventing machine learning technique to get trained properly. Moreover, the most important variables identified by the random forest model are those related to the operating speed, resentful other party, traffic volume, truck traffic volume, riding under the influence, horizontal curvature, wide roadway with more than two lanes and rider's age.     

基于负二项分布的高速公路交通事故影响因素分析

为分析高速公路交通事故的影响因素,构建基于负二项分布的事故分析模型,探究事故数与交通特性、公路线形及路面性能间关系.鉴于传统固定参数模型难以刻画各因素对事故风险影响的异质性,引入了随机参数建模方法.结果表明:相比于固定参数负二项模型,构建的随机参数负二项模型有更好的拟合优度,且能更合理地反映各因素对事故的作用效果;将随...     

Evaluation of risk factors for road accidents under mixed traffic: Case study on Indian highways

This paper presents an evaluation of risk factors for highway crashes under mixed traffic conditions. The basis of selecting study sites was abutting land use, roadway, and traffic characteristics. Accordingly, the study selected thirteen segments on the existing highway network in the state of West Bengal of India, covering a wide spectrum of such road attributes. A systematic investigation based on site-specific accident data to capture the highway sections' safety features revealed that the crash rate has steadily increased for years with traffic regardless of roadway category and conditions. A number of risk factors that affect road accidents were identified; they are mid-block access, pavement and shoulder conditions, vehicle involvement, time of day, and road configuration, i.e., two and multi-lane. The empirical observation indicates that the crash rate is relatively lower on multi-lane highways; however, the severity of any crash on such a road is relatively high. Notably, the crash frequencies on such roads are less during daylight hours due to the lane-based unidirectional traffic movement. This is quite the opposite during nighttime when drivers exhibit an inability to meet traffic contingencies, thereby increasing crash risk. The majority of crashes on two-lane highways are, on the other hand, due to unsafe driving manoeuvers. The study also observed that frequent mid-block accesses and poor shoulder conditions reduce scopes to rectify driving errors and increase crash risk as a consequence. The paper subsequently suggests proactive approaches to identify safety deficits at the time of planning and designing.     

山区公路小半径弯道路段事故严重度影响因素及其异质性比较分析

为分析影响山区公路小半径路段典型事故的严重程度的相关因素及其异质性效应，基于某山区双车道公路1 067起交通事故数据，从驾驶员、车辆、道路和环境4个方面选取15个潜在特征变量，采用二项Logit模型和随机参数二项Logit模型，分别构建小半径弯道路段上追尾碰撞、正面碰撞和侧面碰撞3类典型事故的严重度分析模型，分析3类典型事故严重度的显著影响因素，并采用边际弹性系数量化分析影响因素的作用强度。结果表明，小半径弯道路段上不同形态事故的严重度影响因素存在明显差异:①追尾碰撞严重度的显著影响因素依次为摩托车、夜间、弯道转角、驾驶员年龄、季节，摩托车和冬季分别是服从(2.716.1.5642)和(-1.495，2.1162)正态分布的异质性影响因素，导致发生伤亡事故的概率为95.72%和23.58%；②正面碰撞严重度的显著影响因素依次为货车、摩托车、驾驶员超车、弯道转角和弯道长度，货车导致其伤亡事故概率增加108.8%，摩托车和弯道长度分别是服从(6.941，9.9012)和(-0.004，0.0032)正态分布的异质性影响因素，导致发生伤亡事故的概率为76.11%和9.18%；③侧面碰撞严重度的显著影响因素依次为摩托车、驾驶员年龄及弯道有接入口，摩托车和接入口分别是服从(5.211，5.1112)和(-1.408，2.1462)正态分布的异质性影响因素，导致发生伤亡事故的概率为88.87%和25.47%。④与传统二项Logit模型相比，追尾碰撞、正面碰撞和侧面碰撞的随机参数二项Logit模型的拟合优度分别提高了2.85%，4.15%，6.76%，且定量捕捉了异质性影响因素，更适用于事故严重度的精细化分析。      

Pedestrian injury outcomes in the developing urban metropolis: Econometric models for assessing risk factors

Pedestrian fatality and injury is one of the most concerning issues around the globe. The predictors for such mishaps have been investigated in the developed countries through econometric models and are proven useful techniques. Such studies in the context of developing countries, especially for urban cities, are however still very scarce. Using five years reported pedestrian crash data, this study looks into the performance of three statistical models - Multinomial Logit (MNL), Ordered Logit (OL) and Partial Proportional Odds (PPO) model while examining the impact of various attributes related to pedestrian crashes severity outcomes for Dhaka metropolitan city in Bangladesh. The comparative analysis reveals that the performance of the PPO model is relatively better for the available dataset in terms of identifying critical risk factors. Undivided roadway, heavy vehicles, unfit vehicles, adult drivers with no seat belt use, young and older pedestrians, pedestrian road crossing action are found to be associated with higher probability of fatal injuries. In contrast, one-way traffic movement, daytime, motorcycles and mid-aged pedestrians decrease the likelihood of fatal injury. Based on these identified risk factors, a combined 3-E approach has been suggested to reduce the severity levels of pedestrian in the event of crash occurrence.     

Odds of work zone crash occurrence and getting involved in advance warning,transition, and activity areas by injury severity

Work zone area types include advance warning area, transition area, and activity area. The geometric conditions, traffic control aspects, traffic operations, and driver's maneuverability differ within each work zone area type. Therefore, the odds of getting involved in a crash and factors associated with injury severity vary by work zone area type. The focus of this research is to examine the odds of getting involved in a crash in work zone advance warning, transition, and activity areas by injury severity. Five years (2010–2014) of crash data for the state of North Carolina was obtained from the Highway Safety Information Systems (HSIS) and used in this research. Three partial proportional odds models and one proportional odds model were developed using Statistical Analysis Software (SAS) in this research. The results indicate that the odds of getting involved in a work zone crash in the transition area when compared to the advance warning area is higher during cloudy weather condition, on wet roads and interstates, and on roads equipped with double yellow / no passing zone, with rigid post barrier, grass, and flexible post barrier median. Further, the odds of getting involved in a work zone crash in the activity area when compared to the advance warning area is higher during cloudy weather condition, on interstate and US routes, and on roads with stop and go signal, double yellow / no passing zone and flexible post barrier median. Overall, the findings indicate that the odds and factors associated with crash occurrence depend on the work zone area type. The odds of getting involved in a severe or moderate injury crash is higher on curved roads in all the three work zone area types compared to straight roads. It is higher 1) in the advance warning area on roads with semi-flexible post barrier medians, 2) in the transition area on US routes, and 3) in the activity area on dark lighted roads, US routes, and State routes. Overall, the odds of getting involved in a severe or moderate injury crash and associated factors vary by work zone area type. The findings from this research assist the practitioners to take precautionary measures and reduce the odds of getting involved in a crash by implementing work zone area-specific safety countermeasures.     

Applying interpretable machine learning to classify tree and utility pole related crash injury types

In spite of enormous improvements in vehicle safety, roadway design, and operations, there is still an excessive amount of traffic crashes resulting in injuries and major productivity losses. Despite the many studies on factors of crash frequency and injury severity, there is still further research to be conducted. Tree and utility pole/other pole related (TUOP) crashes present approximately 12 to 15% of all roadway departure (RwD) fatal crashes in the U.S. The count of TUOP crashes comprise nearly 22% of all fatal crashes in Louisiana. From 2010 to 2016, there were 55,857 TUOP crashes reported in Louisiana. Individually examining each of these crash reports is not a realistic option to investigate crash factors. Therefore, this study employed text mining and interpretable machine learning (IML) techniques to analyze all TUOP crashes (with available crash narratives) that occurred in Louisiana from 2010 to 2016. This study has two major goals: 1) to develop a framework for applying machine learning models to classify injury levels from unstructured textual content, and 2) to apply an IML framework that provides probability measures of keywords and their association with the injury classification. The present study employed three machine learning algorithms in the classification of injury levels based on the crash narrative data. Of the used modeling techniques, the eXtreme gradient boosting (XGBoost) model shows better performance, with accuracy ranging from 0.70 to 24% for the training data and from 0.30% to 16% for the test data.     

An analysis of motorcyclists' injury severities in work-zone crashes with unobserved heterogeneity

Work-zone related crashes in Florida are increasing steadily in recent years. With such growing concern, work-zone is recognized in Florida Strategic Highway Safety Plan. The severity and complexity of motorcycle crashes in work-zones is critically important and worth investigating. However, the resulting effect of work-zone on motorcyclists' injuries in work-zone crashes is not fully understood. The purpose of study is to identify the contributing factors of motorcyclists' injury severity sustained in the work-zone crashes in Florida. Recognizing the relatively higher risk of motorcyclists in work-zones with respective to non-work-zones, this study further uncovers the contributing factors for single- and multi-vehicle motorcycle crashes in Florida work-zones. This study investigated motorcyclists' injury severity applying random parameter multinomial logit with possible heterogeneity in means and variances of the random parameters for single-motorcycle and multi-vehicle motorcycle crashes. This study utilizes the Crash Analysis Reporting (CAR) system in Florida over a period of five years from 2012 to 2016 (inclusive). The model result indicates a complex relationship between dark condition, old-aged motorcyclist (50–65), requirement and absence of endorsement, partial ejection, straight roadway segment, shoulder width (up to 1.22 m (4 ft), and 2.74–3.66 m (9–12 ft), urban interstate, activity area, and lane closure and work on shoulder-median work-zone types. The effect of work-zone on single-motorcycle crashes tends to have much more in resulting injury severities relative to multi-vehicle motorcycle crashes. It is more important to investigate the injury severity by single- and multi-vehicle crashes involving motorcycles in work-zones. These risk factors identified in the study are expected to provide more insights for the countermeasures specific to engineering (roadway design) and policy (motorcycle training), which can be considered to improve motorcycle safety in Florida.     

基于GIS分析的深圳市道路交通事故空间分布特征研究

掌握城市道路交通事故空间分布特征是城市道路交通安全管理的重要基础。基于深圳市2014~2016年的道路交通事故数据,首先应用地理编码方法对原始事故记录进行空间定位,形成事故的空间分布。其次针对考虑/不考虑路网密度的2种情况,应用密度分析方法对道路交通事故多发的区域和事故严重程度较高的区域进行鉴别,比较2种情况下区域分布的差异并分析造成这种差异的可能原因。最后利用异常点分析和热点分析2种空间聚类分析模型对事故严重程度较高的区域进行进一步鉴别,并对密度分析和聚类分析2种方法得到的结果进行了比较。密度分析结果表明：就事故频度而言,深圳市中心城区单位面积上的交通事故频度较高,而郊区单位长度道路上的交通事故分布更为密集;就事故严重程度而言,郊区的交通事故平均严重程度高于市中心区域。造成上述差异的原因可能与郊区道路限速较高等因素有关。聚类分析结果与密度分析结果相近,在郊区形成了高严重程度的事故聚类,而在中心城区形成了低严重程度的事故聚类,说明郊区的交通事故严重程度总体高于市中心区域。从2种方法的比较来看,密度分析简单易行,有助于交通管理部门对城市交通事故空间分布特征直观快速的了解;聚类分析可精确到事故点,为精细化的交通安全管理工作提供支撑。研究结果表明基于密度分析和聚类分析的研究方法对于确定道路交通事故空间分布特征有良好的作用。     

Factors associated with crashes due to overcorrection or oversteering of vehicles

The objective of this research is to identify factors associated with crashes due to overcorrection or oversteering of vehicles. Crash data was collected from 2011 to 2013 for the State of North Carolina in the United States. Logistic regression modeling was used to analyze crash data because of the dichotomous nature of the dependent variable (overcorrection or oversteering). The crash involvement due to overcorrection or oversteering of a vehicle decreased as the age of the driver increased. Drivers are 2.22 times more likely to overcorrect or oversteer when ill, 3.44 times more likely to overcorrect or oversteer when under fatigue, and 1.61 times more likely to overcorrect or oversteer when fallen asleep compared to normal physical conditions. Overall, driver characteristics and speed limit tend to play a major role in overcorrection or oversteering of vehicles. Programs to reduce impaired driving might help in the reduction of overcorrection or oversteering related crash fatalities or injuries. Additionally, training and driver education programs focusing on identified factors associated with crashes due to overcorrection or oversteering of vehicles will benefit drivers on how to respond during emergency or panic situations.