基于改进贝叶斯网络模型的高速公路交通设施风险评估

交通设施对高速公路交通安全起着关键作用,对交通设施进行风险评估是预防交通事故的有效方法之一.为实现对高速公路交通设施风险的评估,分析了交通设施对交通安全的影响机理.通过因子分析法提取了各作用节点和作用网络,根据网络中各节点之间的关系,结合条件概率知识对贝叶斯公式进行了变形与推导,舍去无关变量和无影响常量,构建基于改进贝叶斯网络的高速公路交通设施风险评估模型.用高速公路历史事故数据对模型概率参数进行标定和校准之后,可将高速公路交通设施现状调查结果带入模型进行风险评估.模型计算得到的 值越小,高速公路交通设施风险越大.采用珲乌高速公路实地调查数据,带入模型分析发现,值小于0.5时,事故数相对较高,设施风险较大;值大于0.5时,设施风险较小.      

Impacts of freeway speed limit on operation speed of adjacent arterial roads

Speed limit is one of the essential factors associated with roadway safety. Spillover effect is the tendency of drivers to drive at a higher speed on relatively lower-speed non-freeway roadways after exiting higher-speed freeways. Speed studies were conducted on urban arterial roads with speed limits of 45 mph - all adjacent to freeways with speed limits of 55 mph and 70 mph. A comparison of speed differences between motorists who exited the freeway and those who were already driving on the arterial road was performed. Results showed that the mean speed and 85th percentile of passenger cars on arterials adjacent to 55 mph freeway were 47.3 and 51.1 mph, respectively, while the mean speed and 85th percentile of passenger cars on arterial adjacent to a higher speed 70 mph freeway were 49.5 and 53.4 mph, respectively. Differences in the mean speeds between vehicles exiting from freeways and vehicles already driving on the arterial road were observed at all four sites evaluated. This paper provides researchers, policymakers, and engineers with a better understanding of the effects of speed spillover on adjacent roads when determining whether to increase the speed limit on freeways.     

Customization of Automatic Incident Detection Algorithms for Signalized Urban Arterials

Non-recurrent congestion or incidents are detrimental to the operability and efficiency of busy urban transport networks. There exist multiple automatic incident detection algorithms (AIDAs) to remotely detect the occurrence of an incident in highway or freeway scenarios; however, very little research has been performed to automatically detect incidents in signalized urban arterials. This limited research attention has mostly been focused on developing new urban arterial specific algorithms, rather than identifying alternative methods to synthesize existing freeway-based algorithms for urban conditions. The main hindrance to such synthesis is that the traffic patterns on the signalized urban arterials are significantly different from the same on highways/freeways due to the presence of traffic intersections. This article introduces a new strategy of customizing the existing AIDAs (freeway based or otherwise) to significantly improve their adaptability to signalized urban arterial transport networks. The new strategy focuses on preprocessing the traffic information before being used as input to a freeway/highway-based AIDA to lessen the effect of traffic signals and to imitate the input patterns in highway/freeway-based incident conditions. The effectiveness of this new strategy has been established with the help of four existing AIDAs. The proposed strategy is a simple solution to implement existing algorithms to signalized urban networks without any further instrumentation or operational cost.     

基于ATT-LSTM模型的高速公路交通事件持续时长预测

为揭示交通事件对高速公路运行状态持续时间的影响规律，研究了高速公路交通事件持续时长预测方法。考虑高速公路交通事件时间序列特性，基于循环神经网络理论，从时间序列数据中提取交通事件时间依赖关系；通过引入长短时记忆网络，结合特征、时序注意力层挖掘历史时刻信息和当前时刻数据间的相关性，构建基于注意力机制-长短时记忆网络的高速公路交通事件持续时长预测模型。以2018年西安绕城高速公路交通监测数据集为例，开展了高速公路交通事件持续时长预测模型验证，对比了所提模型与反向传播神经网络、随机森林、支持向量机、长短时记忆网络模型这4种典型算法的预测精度，并分析了事件类型、天气条件、车辆类型、交通量等不同影响因素对持续时长的影响程度。结果表明:使用同一数据集，注意力机制-长短时记忆网络预测模型的预测结果平均绝对误差为24.43，平均绝对百分比误差为25.24%，均方根误差为21.17，预测精度优于其他4种预测方法。在模型的各影响因素权重中，事件类型所占权重最大为0.375，其次分别为车道数、车辆类型、天气等；采用立交出入口小时交通量作为修正参数，可以进一步提升预测精度，预测结果的绝对误差、平均绝对百分比误差和均方根误差可分别降低21.3%、7.5%和16.9%。研究结果能进一步提高高速公路交通事件持续时长预测的精度，为公路安全高效运行提供技术支持。      

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。      

团雾天气下高速公路交通事故特征分析

近年来团雾天气下高速公路交通事故频繁发生,对交通安全管理提出了新的挑战.文中以沪宁高速公路沿线4 174站次的团雾过程和74起团雾天气下的交通事故数据为基础,分析了团雾的发生规律和团雾天气下高速公路交通事故特征.研究发现:秋、冬、春三季团雾发生频率高,发生时段主要是在后半夜的00:00时至凌晨06:00时之间,临湖环境和临江路段由于特殊的地形和水汽输送通道而成为团雾高发的区域.1年中的10~11月、1~2月、4~5月,1d中的早上07:00~08:00是团雾天气下交通事故高发期;事故形态主要为追尾,伤亡程度十分严重,死亡人数3人及以上的事故数占55.41%.事故涉及车辆多且损毁严重,涉及10辆以上的事故占75%左右.70%左右的事故造成的交通中断或滞留时间在5h以上.      

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...     

Behavioral responses to pre-planned road capacity reduction based on smartphone GPS trajectory data: A functional data analysis approach

Abstract

Pre-planned events such as constructions or special events lead to road capacity reductions and create bottlenecks in the traffic network. The traffic impact of such events goes beyond local areas, as informed drivers may detour to alternative corridors and consequently the traffic congestion may divert or propagate to other corridors. Due to the lack of real observation data, traditional traffic impact analyses are typically based on simulation models, fixed-location sensor data or survey questionnaires. In this research, we use high-resolution vehicle trajectory data collected via a smartphone app, which is capable of keeping track of individual driver’s behavior before and after road capacity reduction, to investigate travelers’ behavioral responses to pre-planned events and the contribution factors. For this purpose, a functional data analysis (FDA) approach-based clustering method is firstly proposed to cluster trajectory data and identify detour patterns, and two logistic and a least absolute shrinkage and selection operator (LASSO) regression models are used to explain drivers’ detour behavior choice for each pattern with spatial and temporal features of interest. A case study based on a lane closure event on MoPac expressway in Austin, TX is used as an example in this research. The case study demonstrates that: (1) the freeway capacity reduction triggered heterologous behavior responses, (2) driver detour behavior exhibits three major patterns and (3) each detour pattern highly depends on spatial features such as trip length, distance to freeway entrance and distance to other alternative freeways, in addition to the temporal features when the trip happens.     

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.     

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.     

Effects of the autonomous vehicle crashes on public perception of the technology

In March 2018, an Uber-pedestrian crash and a Tesla's Model X crash attracted a lot of media attention because the vehicles were operating under self-driving and autopilot mode respectively at the time of the crash. This study aims to conduct before-and-after sentiment analysis to examine how these two fatal crashes have affected people's perceptions of self-driving and autonomous vehicle technology using Twitter data. Five different and relevant keywords were used to extract tweets. Over 1.7 million tweets were found within 15 days before and after the incidents with the specific keywords, which were eventually analyzed in this study. The results indicate that after the two incidents, the negative tweets on “self-driving/autonomous” technology increased by 32 percentage points (from 14% to 46%). The compound scores of “pedestrian crash”, “Uber”, and “Tesla” keywords saw a 6% decrease while “self-driving/autonomous” recorded the highest change with an 11% decrease. Before the Uber-incident, 19% of the tweets on Uber were negative and 27% were positive. With the Uber-pedestrian crash, these percentages have changed to 30% negative and 23% positive. Overall, the negativity in the tweets and the percentage of negative tweets on self-driving/autonomous technology have increased after their involvement in fatal crashes. Providing opportunities to interact with this developing technology has shown to positively influence peoples' perception.     

基于检测器脉冲数据的高速公路事件自动检测算法研究

提出了一种全新的高速公路事件自动检测算法,直接利用车辆检测器输出的脉冲宽度和脉冲间隔作为参数,运用LVQ神经网络对获得的脉冲宽度数据和脉冲间隔数据进行处理来判断是否有事件发生,从而大大减少了检测时间,为交通事件的快速处理提供了可靠的依据。此外,该算法利用神经网络的自学习能力,可以很好地确定各条道路发生交通事件的门限值。仿真结果表明:该算法具有较高的事件检测率(约为97%)、较短的检测时间和较低的误警率(约为0 41%),具有很好的应用前景。     

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.     

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.     

考虑高速公路运行风险的雨天可变限速方法

为保证雨天环境下高速公路行驶安全，降低道路整体运行风险，结合雨天风险特征，开展考虑运行风险的雨天可变限速研究。首先应用随机森林模型，标定雨天环境下高速公路动静态风险因素的特征重要度，并结合熵值理论，建立高速公路风险模型、计算风险系数、划分风险等级;之后，以空域自适应算法中可变限速推演变化规律为基础，考虑大、小型车的行驶特征，结合预期风险、雨天停车视距、水膜厚度等因素，优化可变限速模型，细化大、小型车辆的初始控制值，进而提出不同降雨强度、不同能见度下的可变限速推荐值；在此基础上，利用驾驶模拟实景仿真系统、心理生理检测设备、微观交通流仿真软件，开展可变限速系统控制值合理性及驾驶人行车适应性与交通流运行状态的实证分析。研究结果表明：随着能见度降低和降雨量增大，在可变限速控制下，驾驶人呈现出交感神经兴奋性减弱、副交感神经兴奋性增强、心理紧张度降低的状态，其平均心率、心率变异性高频值、心率变异性低频值、心率变异性差异值分别由74.13、0.121、0.643、2.37变为78.23、0.192、0.567、2.01，驾驶人对限速方案的适应性良好；同时，可变限速可保证道路整体通行效率，不会造成交通流风险震动，在小型车、大型车限速分别为80、60 km·h^-1和小型车限速60 km·h^-1、大型车禁止驶入场景下，碰撞时间均值、中值大于未限速场景，各车道的行车安全性均能得到保障;提出的雨天可变限速控制方法合理，且具有一定工程适应性，能为异常天气高速公路宏观车流主动防控提供理论支撑。     

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。      

Age and road safety performance: Focusing on elderly and young drivers

The existing literature on young and elderly drivers indicates that they have the highest crash risks compared to other age groups of drivers. This study improves our understanding of the risk factors contributing to young and elderly drivers' elevated crash risk by examining self-report data from the E-Survey of Road User's Safety Attitudes (ESRA). The primary objective of this study is to compare the attitudes and behaviours of young, elderly, and middle-age drivers in Canada, the United States, and Europe. The main focus is on the practice of driving while distracted by mobile phones and driving while fatigued, as these are two dangerous behaviours that demonstrate the impact age may have. The analyses consistently showed that there are differences in the responses attributable to age. In all regions, drivers aged 18–21 years consistently reported higher rates of distracted and fatigued driving and higher rates of perceived social and personal acceptability of these behaviours than drivers aged 35–54 years. Elderly drivers aged 65+ years reported even lower rates of these behaviours and acceptability. Young drivers were also the least likely to believe that distraction and fatigue are frequent causes of road crashes, while elderly drivers were the most likely to believe this. This pattern with respect to age repeats in the support for policy measures as well; young drivers are least likely to support zero tolerance policies for mobile phone use when driving, while elderly drivers are the most likely to support this measure. Multivariate logistic regression modeling confirmed that elderly drivers were the least likely to engage in the use of mobile phones while driving or driving while fatigued. Statistically significant results showed that the middle-age group was less likely than young drivers to read a text message/email or check social media while driving and driving while fatigued.     

A meta-analysis of the impacts of operating in-vehicle information systems on road safety

This study aims to estimate the overall impact of distraction due to operating in-vehicle information systems (IVIS) and similar devices while driving on road crashes. While similar research has been undertaken investigating the issue, varying results have been reported so far. Therefore a two-step approach was adopted: initially a review of the literature was conducted to identify key high quality studies and the parameters that they examined. Afterwards, meta-analyses were applied in order to estimate the overall effects of operating IVIS while driving on the absolute proportion of crashes (i.e. the proportion of total crashes due to IVIS). After applying a random effects meta-analysis to the findings of existing studies, it was found that 1.66% of crashes occur due to operating devices in total. In addition, it is indicated that about 0.6% of safety-critical incidents for professional drivers are due to in-vehicle device operation. The odds of crashes influenced by IVIS operation were also estimated and were found to be very low. From the findings of the present review and the meta-analysis, it is suggested that device operation as a risk factor while driving is a less researched aspect of driver distraction than others, and more studies would improve result estimates and transferability, especially for professional drivers. This study summarizes concisely the current effect of driver interaction with in-vehicle information systems on crashes, which might become considerably pertinent in view of the increasing deployment of vehicles with increasing levels of automation.     

2006年雾对高速公路影响规律

高速公路雾天行车存在着严重的安全隐患,而且雾天交通安全也是一个世界性的问题。目前我国通行的做法是在雾天封闭交通,这样虽然避免了交通事故的发生,但是却以完全丧失高速公路的通行能力为代价。本文通过对2006年全国主要高速公路受雾影响预报的分析,得出全国范围内各地雾发生频率,主要高速公路受雾影响特征及各路段影响频率,总结出雾对高速公路的影响规律。为今后雾天气下全国高速公路交通气象预报提供参考和指导,为驾驶员雾天行车的安全性提供保障,并为高速公路管理部门在日常工作中,重点监控雾的频发路段,有针对性采取有效地管控措施提供依据。     

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.