基于随机参数Logit模型的校车事故伤害严重程度分析

为深入分析安全因素对校车事故伤害严重程度的影响，探寻事故数据中未观察到的异质性，基于随机参数Logit模型从驾驶员、车辆、道路特征及环境4个方面构建校车事故伤害严重程度模型。结果表明:①涉事车辆数为2辆且对应参数服从正态分布时，不发生死亡受伤事故的概率为83.84%；②驾驶员年龄35~44岁、涉事车辆数为1辆时，死亡受伤事故概率均降低0.58%；③道路限速值为40~50 km/h时发生死亡受伤事故概率增加0.35%，道路限速值大于60 km/h时发生死亡受伤事故概率增加0.96%；④安全气囊状态打开，死亡受伤事故概率增加2.35%；⑤交通控制方式为车道标线时可能伤害事故概率增加1.85%，控制方式为中央分隔带时未受伤事故概率降低1.44%，死亡受伤事故发生概率却增加0.48%；⑥不安全时倒车转弯时发生可能伤害事故概率降低0.42%，分心驾驶、未按规定车道行驶、跟车太近和其他（饮酒）时未受伤事故概率分别增加1.36%，0.56%，0.39%和0.97%，可能受伤事故和死亡受伤事故发生概率却有所降低。      

Non-fatal spine injuries resulting from motorcycle crashes

This study aims to determine spinal injury patterns and identify crash factors commonly associated with serious spinal injury as a result of motorcycle crashes. Data was retrospectively collected from motorcyclists sustaining spinal injuries from road crashes treated at Kuala Lumpur Hospital, Malaysia, over the 5-year period from 2005 to 2009. Each patient's injuries were analyzed by reviewing his or her medical records for radiographic imaging and computed tomography scans.A total of 151 patients were included in this study, of which, males accounted for over 87%. The first lower lumbar (L1) was the most commonly injured vertebral level, followed by the adjacent thoracic vertebra (T12). Fracture to the vertebral body without dislocation was found to be the most frequently observed spinal injury pattern. Injury severities for a majority of patients (65%) were measured at Maximum Abbreviated Injury Scale (MAIS) of 2. Serious spinal injury was associated with thorax or upper-extremity injury.Prevalence of lumbar spinal injury in the study reflects a predominantly low-speed crash among the motorcyclist in the region. Motorcyclists are at greater odd to sustain severe spinal injury when directly striking an object compare to striking the ground during the crash event.     

Modeling lane-change-related crashes with lane-specific real-time traffic and weather data

This study investigates the relationship between lane-change-related crashes and lane-specific, real-time traffic factors. It is anticipated that the real-time traffic data for the two lanes—the vehicle's lane (subject lane) and the lane to which that a vehicle intends to change (target lane)—are more closely related to lane-change-related crashes, as opposed to congregated traffic data for all lanes. Lane-change-related crash data were obtained from a 62-mile long freeway in Southeast Wisconsin in 2012 and 2013. One-minute traffic data from the 5- to 10-minute interval prior to the crashes were extracted from an immediately upstream detector station and two immediately downstream stations from the crash location. Weather information was collected from a major historical weather database. A matched case-control logistic regression was used for analysis. Results show that the following factors significantly affect the probability of a lane-change-related crash: average flow into the target lane at the first downstream station, the flow ratio at the second downstream station, and snow conditions. Additionally, the average speed in the target lane at the first downstream station contributes to the occurrence of lane-change crashes during snowy conditions. According to the model, the probability of a lane-change-related crash under real-time traffic conditions can aid in flagging potential crash-prone conditions. The identified contributing factors can help traffic operators select traffic control and management countermeasures to proactively mitigate lane-change-related crashes.     

基于部分优势比的公路隧道交通事故严重程度分析模型

为了明晰公路隧道交通事故严重程度的影响因素,在分析了16条公路隧道3年内发生的296起交通事故的空间特性、事故形态及其发生原因的基础上,以交通事故严重程度为因变量,将其分为仅财产损失、轻伤、重伤或死亡事故3个等级,从人、车和隧道行车环境3个方面选择了14个交通事故严重程度的潜在影响因素,分别采用有序Logit模型和部分优势比模型建立交通事故严重程度分析模型,并采用Brant检验判断比例优势假设。研究结果表明：与公路隧道交通事故严重程度显著相关的有4个自变量,分别为是否涉及大货车、事故涉及车辆数、事故发生时间和天气因素,其中是否涉及大货车、事故发生时间和天气因素3个自变量满足比例优势假设,而事故涉及车辆数不满足比例优势假设;对于部分优势比模型来说,涉及大货车的事故发生轻伤事故、重伤或死亡事故的概率比不涉及大货车的事故分别增加10.2%和3.4%,多车事故发生轻伤事故、重伤或死亡事故的概率比单车事故分别增加1.9%和5.9%,夜间发生轻伤事故、重伤或死亡事故的概率比白天分别增加5.6%和1.7%,非雨天发生轻伤事故、重伤或死亡事故的概率比雨天分别增加4.5%和1.5%。     

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.     

Motorcycle fatalities in Malaysia

Malaysia has the highest road fatality risk (per 100,000 population) among the ASEAN countries and more than 50% of the road accident fatalities involve motorcyclists. This study has collected and analysed data from the police, government authorities, and national and international research institutes. Only fatality data are used due to the severe underreporting of severe injuries (up to 600%) and slight injuries (up to 1400%). The analysis reveals that the highest numbers of motorcycle fatalities occur in rural locations (61%), on primary roads (62%) and on straight road sections (66%). The majority are riders (89%), 16 to 20 years old (22.5%), and 90% of the motorcycles are privately owned. Of those involved in fatal accidents, 75% of the motorcyclists wear helmets, and 35% do not have proper licences. The highest number of fatalities by type of collision is ‘angular or side’ (27.5%). Although fatal motorcyclist crashes mostly involve ‘passenger cars’ (28%), motorcyclists are responsible for 50% of the collisions either by crashing singly (25%) or with other motorcyclists (25%). While male motorcyclists predominate (94% of fatalities), female motorcyclists aged 31 to 70, possessing ‘no licence’, not wearing helmets and travelling during the day, account for a higher percentage than male motorcyclists. Malaysia must acquire more motorcycle exposure data and establish an injury recording system and database based on hospital-records. To reduce motorcycle fatalities, it first has to understand why young male motorcyclists are prone to fatal crashes in the evenings and on weekends on rural primary roads, especially on straight road sections.     

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.     

Factors affecting severity of motorcycle accidents on Thailand's arterial roads: Multiple correspondence analysis and ordered logistics regression approaches

Thailand was classified as a middle-income country and ranked second highest in terms of road traffic fatality rate in the world in 2015. By 2018, this ranking went up to ninth in world which may be because of various earnest safety policies implementation, supporting road safety research and establishing a road safety directing center. However, crash fatality rate has considerably remained high until recent year, indicating a clear need for further related research. Considering severity of the crashes, the majority of fatal crashes involved the motorcycle road user. Therefore, motorcycle crashes are important issues and should be considered to mitigate fatality due to immoderate proportion of motorcycle road user and motorcyclist fatality. This study aims to identify factors that influence the severity of motorcycle accidents on Thailand's arterial roads by employing ordered logistic regression and multiple correspondence analysis. The results demonstrated that although both analyses were relatively different, they provided similar results. Age, road lanes, and helmet wearing were significant factors that influenced the severity of motorcycle accidents. The results could serve as reference for planning strategies or organizing campaigns to reduce and prevent death owing to road traffic accidents, which may enhance the overall image of road traffic safety in Thailand.     

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Logit模型的城市道路交通事件检测仿真

以Logit模型为基础,利用效用函数与概率的概念,建立分时段的城市道路交通事件检测算法。由PARAMICS软件产生模拟交通流数据,将数据输入LIMDEP软件并标定效用函数的系数,同时还输出最大概率预测表。仿真试验结果表明:(1)基于Logit模型的检测算法不仅能够用于城市道路的事件检测,还可判断事件发生所在的车道。(2)在路段长度、车道数、流量相等的模拟条件下,交叉口信号超过仿真所设定的1 min时段长度时,检测效果降低。若将模型时段长度由1 min提高至超过最大信号周期,即可解决检测效果降低的问题。     

Do driver training programs reduce crashes and traffic violations? — A critical examination of the literature

This paper reviews the evaluation literature on the effectiveness of classroom and behind-the-wheel driver training. The primary focus is on North America programs as originally taught in high schools but now also by private instructors. Studies from the United Kingdom, Australia, New Zealand and Scandinavia are also included.By far the most rigorous study to date was the experimental study in DeKalb, Georgia, U.S.A. This study used a randomized design including a control group and a very large sample size to provide reasonable statistical precision. I reexamine the DeKalb data in detail and conclude that the study did show evidence of small short-term crash and violation reductions per licensed driver. However, when the accelerated licensure caused by the training is allowed to influence the crash and violation counts, there is evidence of a net increase in crashes.The other studies reviewed present a mixed picture but the better designed quasi-experimental evaluations usually showed no effects on crash rates but almost all suffer from inadequate sample size. I show that as many as 35,000 drivers would be required in a two group design to reliably detect a 10% reduction in crash rates.The advent of GDL laws in North America and other countries has largely remedied the concern over accelerated licensure of high risk teenage drivers by delaying the progress to full licensure. Conventional driver training programs in the U.S. (30 h classroom and 6 h on-the-road) probably reduce per licensed driver crash rates by as little as 5% over the first 6–12 months of driving. The possibility of an effect closer to 0 cannot be dismissed.Some GDLs contain an incentive for applicants to complete an advanced driver training program in return for shortening the provisional period of the GDL. The results of Canadian studies indicate that any effects of the driver training component are not sufficient to offset the increase in accidents due to increased exposure.There is no evidence or reason to believe that merely lengthening the number of hours on the road will increase effectiveness. Programs directed toward attitude change and risk taking better address the underlying cause of the elevated crash risk of young drivers but these behaviors are notoriously resistant to modification in young people.     

Sampling bias and weight factors for in-depth motorcycle crash data in Thailand

Motorcycle crashes are documented in Thailand's national records but are underreported and lacking detail. In-depth motorcycle crash data, collected by Thailand Accident Research Center (TARC), contains a smaller number of motorcycle crashes but more detail. However, to draw conclusions at a national level, representativeness of the TARC in-depth data is currently unknown, and the correction of sampling biases may be required. In this study, the Capture-recapture method was used to examine the underreporting in the national crash data (from the government insurance company). It was found that 69% of fatal and 70% of non-fatal injuries were underreported, respectively. The in-depth crash data was found to be biased. The weighting methods post-stratification and iterative proportional fitting were applied to compensate for the bias and are shown to improve the representativeness of the in-depth motorcycle crash data. Weighted in-depth crash data appears to be suitable to draw conclusions on motorcyclist safety in Thailand.     

山区高速公路组合线形路段车道偏移行为

为在道路设计阶段确定平纵组合与相邻路段线形对车道偏离的影响,并为减少因道路线形因素引发的侧碰、追尾甚至车辆驶出路外事故提供改善依据,基于真实的山区高速公路道路设计参数及周边地形,搭建驾驶模拟场景,利用驾驶模拟试验获取小客车车道偏离数据,并对应获取车辆当前所在路段及上、下游路段的线形参数。以车辆车道内行驶为参照,沿道路行进方向,将车道偏离行为分为左偏驶离车道与右偏驶离车道。因车道偏离受驾驶人影响,采用双层Logit模型,分别判定道路线形及驾驶人层的影响。研究结果表明：相比直线路段,曲线更易引发车道偏离行为,驾驶人易偏向于曲线内侧行驶;上游300 m路段曲率差越大、平均车速越大,则车道偏离的概率增大;相对于缓坡（-2%≤坡度S≤2%）,行驶于上坡（S>2%）或下坡（S<2%）路段时,车辆车道偏离概率减小;车辆行驶于外侧车道的左偏驶离车道概率大于行驶于内侧车道;驾驶人因素对左偏驶离车道的影响比例为8.8%,对右偏驶离车道的影响比例为25.6%。研究结论可从组合线形角度帮助工程师设计更安全的山区高速公路。     

Factors affecting lane change crashes

This study aims to investigate the contributing factors affecting the occurrence of crashes while lane-changing maneuvers of drivers. Two different data sets were used from the same drivers' population. The first data set was collected from the traffic police crash reports and the second data set was collected through a questionnaire survey that was conducted among 429 drivers. Two different logistic regression models were developed by employing the two sets of the collected data. The results of the crash occurrence model showed that the drivers' factors (gender, nationality and years of experience in driving), location and surrounding condition factors (non-junction locations, light and road surface conditions) and roads feature (road type, number of lanes and speed limit value) are the significant variables that affected the occurrence of lane-change crashes. About 57.2% of the survey responders committed that different sources of distractions were the main reason for their sudden or unsafe lane change including 21.2% was due to mobile usage. The drivers' behavior model results showed that drivers who did sudden lane change are more likely to be involved in traffic crashes with 2.53 times than others. The drivers who look towards the side mirrors and who look out the windows before lane-change intention have less probability to be involved in crashes by 4.61 and 3.85 times than others, respectively. Another interesting finding is that drivers who reported that they received enough training about safe lane change maneuvering during issuing the driving licenses are less likely to be involved in crashes by 2.06 times than other drivers.     

Modeling two-way stop-controlled intersection crashes with zero-inflated models on Louisiana rural two-lane highways

Intersection safety continues to be a crucial issue throughout the United States. In 2016, 27% of the 37,461 traffic fatalities on U.S. roadways occurred at or near intersections. Nearly 70% of intersection-related fatalities occurred at unsignalized intersections. At such intersections, vehicles stopping or slowing to turn create speed differentials between vehicles traveling in the same direction. This is particularly problematic on two-lane highways. Research was performed to analyze safety performance for intersections on rural, two-lane roadways, with stop control on the minor roadway. Roadway, traffic, and crash data were collected from 4148 stop-controlled intersections of all 64 Parishes (counties) statewide in Louisiana, for the period of 2013 to 2017. Four count approaches, Poisson, Negative Binomial (NB), Zero-inflated Poisson (ZIP) and Zero-inflated Negative Binomial (ZINB) were used to model the number of intersection crashes for different severity levels. The results indicate that ZIP models provide a better fit than all other models. In addition to traffic volume, larger curve radii of major and minor roads and wider lane widths of major roads led to significantly smaller crash occurrences. However, higher speed limits of major roads led to significantly greater crash occurrences. Four-leg stop-controlled intersections have 35% greater total crashes, 49% greater fatal and injury crashes, and 25% greater property damage only (PDO) crashes, relative to three-leg intersections.     

Potential safety benefits of lane departure prevention technology

Motor vehicles with advanced safety technologies are rapidly entering the marketplace and the impact of new features are transforming safety on roadways. Among the several safety related technologies currently available in the market, this paper aims to forecast the reduction in crashes with gradual adoption of vehicles with lane departure prevention (LDP) technology. Crash data for the state of Alabama from 2014 to 2016 were used to evaluate the safety benefits of LDP technology. In Alabama, 75% of single-vehicle crashes are the result of lane departure. A 20% effective LDP system implies, whereby an LDP system would prevent a vehicle from exiting a roadway on 20% of applicable instances, would reduce 2.7% and 16.4% of the relevant single-vehicle lane departure (SVLD) crashes by 2020 and 2045 respectively. With increase in the effectiveness of the technology, a greater reduction in crashes was observed. With 100% effectiveness, this technology can prevent 66.5% of SVLD crashes by year 2045. This study presents the first estimations of the number of crashes that could be reduced using LDP and therefore could have significant impacts on public and industry adoption rates of the technology. The results of this study influence policy making and regulatory approaches to improving motor vehicle safety and further recommend education and outreach activities to increase awareness on the benefits of LDP technology.     

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.     

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。