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.     

Modeling injury severity of crashes involving trucks: Capturing and exploring risk factors associated with land use and demographic in addition to crash,driver, and on-network characteristics

Nearly 499,000 motor vehicle crashes involving trucks were reported across the United States in 2018, out of which 22% resulted in fatalities and injuries. Given the growing economy and demand for trucking in the future, it is crucial to identify the risk factors to understand where and why the likelihood of getting involved in a severe or moderate injury crash with a truck is higher. The focus of this research, therefore, is on developing a methodology, capturing and integrating data, exploring, and identifying risk factors associated with surrounding land use and demographic characteristics in addition to crash, driver, and on-network characteristics by modeling injury severity of crashes involving trucks. Crash data for Mecklenburg County in North Carolina from 2013 to 2017 was used to develop partial proportional odds model and identify risk factors influencing injury severity of crashes involving trucks. The findings indicate that dark lighting condition, inclement weather condition, the presence of double yellow or no-passing zone, road sections with speed limit >40 mph and curves, and driver fatigue, impairment, and inattention have a significant influence on injury severity of crashes involving trucks. These outcomes indicate the need for effective geometric design and improved visibility to reduce the injury severity of crashes involving trucks. The likelihood of a severe or moderate injury crash involving a truck is also high in areas with high employment, government, light commercial, and light industrial land uses. The findings can be used to identify potential risk areas, proactively plan and prioritize the allocation of resources to improve safety of transportation system users in these areas.     

Driver propensity characterization for different forward collision warning times

The forward collision warning system, which warns danger to the driver after sensing possibility of crash in advance, has been actively studied recently. Such systems developed until now give a warning, regardless of driver’s driving propensity. However, it’s not reasonable to give a warning to every driver at the same time because drivers are different in driving propensity. In this study, to give a warning to each driver differently, three metrics classifying driver’s driving propensity were developed by using the driving data on a testing ground. These three metrics are the predicted time headway, required deceleration divided by the deceleration of the leading vehicle, and the resultant acceleration divided by the deceleration of the leading vehicle. Driving propensity was divided into 3 groups by using these metrics for braking and steering cases. In addition, these metrics were verified by making sure that braking propensity could be classified on public roads as well.     

Examining the influence of network,land use,and demographic characteristics to estimate the number of bicycle-vehicle crashes on urban roads

The focus of this paper is to examine the influence of network, land use, and demographic characteristics on the number of bicycle-vehicle crashes, and to develop area-level bicycle-vehicle crash estimation models (safety performance functions) for urban roads. Mecklenburg County in the State of North Carolina was considered as the study area. The reported bicycle-vehicle crash data, from 2010 to 2015, along with the network, land use, and demographic characteristics data were obtained from the local agencies. Data within a one-mile buffer of 119 selected locations was then captured. Data for 99 selected locations were used for the modeling purpose, while data for the remaining 20 selected locations were used for validating the models. Six alternate models were developed, considering various combinations of explanatory variables that are not correlated with each other. As the bicycle-vehicle crash dataset used in this research was observed to be over-dispersed (variance greater than the mean), Negative Binomial log-link distribution-based models were developed. The validation dataset was used to compare the estimated number of bicycle-vehicle crashes from each model with the actual number of bicycle-vehicle crashes. The results obtained from the analysis and modeling suggest that bicyclists are more often involved in crashes while traveling on segments with no bicycle lane, the traffic light, 45 mph as the speed limit, and in commercial activity, research activity, institutional, multi-family residential (densely populated), and heavy industrial areas. The computed Moran's Index values indicate weak to no spatial correlation between the residuals of each model. However, the residuals seem to depend on the area type and the number of bicycle-vehicle crashes.     

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.     

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.     

Road characteristics and environment factors associated with motorcycle fatal crashes in Malaysia

This study aims to determine risk factors contributing to traffic crashes in 9,176 fatal cases involving motorcycle in Malaysia between 2010 and 2012. For this purpose, both multinomial and mixed models of motorcycle fatal crash outcome based on the number of vehicle involved are estimated. The corresponding model predicts the probability of three fatal crash outcomes: motorcycle single-vehicle fatal crash, motorcycle fatal crash involving another vehicle and motorcycle fatal crash involving two or more vehicles. Several road characteristic and environmental factors are considered including type of road in the hierarchy, location, road geometry, posted speed limit, road marking type, lighting, time of day and weather conditions during the fatal crash. The estimation results suggest that curve road sections, no road marking, smooth, rut and corrugation of road surface and wee hours, i.e. between 00.00 am to 6 am, increase the probability of motorcycle single-vehicle fatal crashes. As for the motorcycle fatal crashes involving multiple vehicles, factors such as expressway, primary and secondary roads, speed limit more than 70 km/h, roads with non-permissible marking, i.e. double lane line and daylight condition are found to cause an increase the probability of their occurrence. The estimation results also suggest that time of day (between 7 pm to 12 pm) has an increasing impact on the probability of motorcycle single-vehicle fatal crashes and motorcycle fatal crashes involving two or more vehicles. Whilst the multinomial logit model was found as more parsimonious, the mixed logit model is likely to capture the unobserved heterogeneity in fatal motorcycle crashes based on the number of vehicles involved due to the underreporting data with two random effect parameters including 70 km/h speed limit and double lane line road marking.     

从道路交通事故研究看我国汽车正面碰撞法规试验形式

基于我国道路交通深度事故调查中所得数据,从碰撞位置与重叠率、碰撞速度和人员伤亡等方面,分析100%重叠率刚性固定壁障碰撞试验和40%重叠率偏置可变形壁障碰撞试验与我国交通事故形态和人员伤害与特征的关联度.讨论现有正面碰撞法规的试验形式的局限性,并论证了将偏置正面碰撞推荐性标准纳入我国正面碰撞强制性标准体系的必要性和紧迫性.     

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.     

Safety performance functions for low-volume rural minor collector two-lane roadways

The Fixing America's Surface Transportation Act (FAST Act) highlights a data-driven method to improve traffic safety on all public paved roads in the U.S. The first edition of the Highway Safety Manual (HSM) is a widely used tool that provides crash predictive models in the form of safety performance functions (SPFs). There are no specific SPFs for low-volume roadways in the HSM. It is important to know that low-volume roadways are the major roadway types in terms of total mileage. This study used 2015–2019 crash data from Texas, incorporating with other relevant geometric and traffic variables, to develop SPFs for a specific low-volume roadway type (rural minor collector two-lane roadways). This study proposed a rules-based SPF developed approach that makes the prediction accuracies higher compared to the full model. The R² values range from 0.18 to 0.22 for all data (without splitting) for different injury level models. The prediction accuracies are improved in the decision tree-based models. For different class specific models (based on injury levels), the R² values range from 0.25 to 0.41. Three SPF groups are developed based on crash injury types. The SPFs can provide guidance in refining the prediction accuracies of rural minor collectors.     

基于碰撞相容性因素的车辆追尾事故深入数据分析

为详细了解北京市追尾事故车辆的分类对比情况,基于车辆碰撞相容性因素构建了追尾事故的深入数据分析模型,包括追尾事故车辆的基本类型、结构形式及几何特征对比、车辆碰撞损坏特点、车内乘员伤害情况及其相关性等方面内容。利用真实交通事故案例对车辆碰撞相容性相关因素进行统计分析,为车辆追尾事故的预防与伤害减轻等项研究提供参考,指出今后研究的侧重点。     

北京市公路特殊路段防撞护栏碰撞试验条件研究

现行公路规范规定护栏最高防撞能量为520 kJ,不能满足水资源保护区、水库、跨越干线铁路等特殊危险路段的安全防护需要,通过对北京市各高速公路交通流状况调查分析,对特殊路段护栏碰撞能量和碰撞试验条件进行研究,提出适合北京地区特殊防护路段护栏的碰撞试验条件为33 t重大货车以65 km/h速度、20°角碰撞护栏,碰撞能量为630 kJ,为新型高防护等级护栏的研究开发奠定基础。     

车辆与弯道混凝土护栏碰撞的动态数值模拟及试验

对车辆与弯道混凝土护栏碰撞动态数值模拟结果和实车足尺碰撞试验结果进行对比分析，从车辆行驶轨迹、乘员冲击加速度以及车辆损伤形态3个方面，验证了动态数值模拟的准确性，并分析了弯道混凝土护栏曲线半径对乘员碰撞过程中所承受冲击加速度的影响；得到乘员风险的最不利护栏半径。结果表明：有限元仿真是进行汽车护栏碰撞研究的有效方法；弯道处护栏的形式对碰撞时乘员的安全有很大影响。     

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.     

在用桥梁护栏安全性能改进方法研究

为探讨在用桥梁护栏安全性能改进方法,通过实际交通流的观测分析,确定了在用护栏安全性能的检验条件。利用力学分析和三维有限元模拟,确定了兼顾桥梁主体结构安全性和护栏防护性能的在用桥梁护栏安全性能评价方法。设计了具有双层护栏结构的在用桥梁护栏改造方案。结果表明:在用桥梁护栏的典型碰撞检验车辆应选择实际交通流中占有较大比重的车型或需要重点防护的车型,碰撞检验速度取实际交通流的85%位运行速度;在用桥梁护栏安全性能评价应包括防护性能评价和桥梁主体结构安全性评价两部分;内侧梁柱式护栏与外侧组合式护栏结合的改造方案可满足桥梁主体结构安全性要求,防护能力为SS级;在保证防护能力的前提下,可通过景观优化设计使改造后的护栏与环境景观相协调。     

高速公路跨线桥SS级防撞护栏优化设计

基于中国交通规范、高速公路跨线桥交通事故的特征及高速公路跨线桥现有的安全状况,设计了一种全新的高速公路跨线桥防撞混凝土护栏,使之能够满足防御大型货车撞击的要求.为了获得重新设计的护栏的最佳结构参数,应用正交试验法以混凝土护栏截面的6个参数为设计变量,每个变量考虑5个水平,建立汽车-护栏碰撞有限元系统模型进行25组不同截...     

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.     

Crash prediction model for two-lane rural highways in the Ashanti region of Ghana

Crash Prediction Models (CPMs) have been used elsewhere as a useful tool by road Engineers and Planners. There is however no study on the prediction of road traffic crashes on rural highways in Ghana. The main objective of the study was to develop a prediction model for road traffic crashes occurring on the rural sections of the highways in the Ashanti Region of Ghana. The model was developed for all injury crashes occurring on selected rural highways in the Region over the three (3) year period 2005–2007. Data was collected from 76 rural highway sections and each section varied between 0.8 km and 6.7 km. Data collected for each section comprised injury crash data, traffic flow and speed data, and roadway characteristics and road geometry data. The Generalised Linear Model (GLM) with Negative Binomial (NB) error structure was used to estimate the model parameters. Two types of models, the ‘core’ model which included key exposure variables only and the ‘full’ model which included a wider range of variables were developed. The results show that traffic flow, highway segment length, junction density, terrain type and presence of a village settlement within road segments were found to be statistically significant explanatory variables (p < 0.05) for crash involvement. Adding one junction to a 1 km section of road segment was found to increase injury crashes by 32.0% and sections which had a village settlement within them were found to increase injury crashes by 60.3% compared with segments with no settlements. The model explained 61.2% of the systematic variation in the data. Road and Traffic Engineers and Planners can apply the crash prediction model as a tool in safety improvement works and in the design of safer roads. It is recommended that to improve safety, highways should be designed to by-pass village settlements and that the number of junctions on a highway should be limited to carefully designed ones.     

高速公路与城市道路沿线交通对环境的污染及治理措施浅析

在我国一些中心大城市 ,道路交通量迅猛增长 ,交通污染的问题也日益突出 ,如不加治理 ,必然严重干扰沿线居民、学校和企事业单位的正常生活和工作秩序 ,也会因此引起一系列的社会问题。本文在简要叙述了汽车交通造成的环境污染 (交通噪声、大气污染、水质污染 )概况的基础上 ,主要阐述了交通噪声对道路沿线环境污染的危害 ,并提出了以降噪为主要目标的综合治理方法以及措施 ,以期能引起社会和市民重视该问题的重要性和紧迫性 ,进一步推动治理交通污染的工作 ,还道路沿线周围人们一个优良的生活环境     

道路因素对典型较严重道路交通事故严重性的影响分析

基于道路交通事故数据探究事故影响因素对于认识事故的影响因素、提高交通安全水平具有重要意义。利用近年来国内典型较严重道路交通事故数据,应用泊松模型和负二项模型,以区分事故形态的方式建立追尾事故、侧碰事故及撞行人事故的事故死亡率的道路影响因素分析模型。这些模型以三类事故中涉及人员的死亡数为因变量,以一系列道路因素为自变量,将事故涉及人数作为偏移变量。模型的具体形式以过离散系数及赤池信息量准则(AIC)为依据进行选择。结果显示,追尾事故的死亡率与道路等级、路侧防护设施显著相关;侧碰事故则与天气、路表情况、路口路段位置、坡度以及道路结构有关;撞行人事故与路表情况、道路等级、车道数、平曲线半径有关。本文拓展了事故严重性研究的深度,其研究成果对于更好地利用重特大事故的深入调查数据有现实意义,也可为事故分析及道路设计等提供借鉴。