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

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

基于Tripod-Beta模型的船舶火灾事故风险分析

为深入分析船舶火灾事故风险因素及其后果产生的影响,通过分析1991-2017年全球船舶火灾事故调查报告,从人员、管理、船舶设备、货物、环境5个方面对船舶火灾影响因素进行识别研究;采用三脚架事故致因模型(Tripod-Beta model),构建考虑安全栅的船舶火灾事故情景演化模型,识别船舶火灾关键影响因素;并在样本量较少的情况下,采用信息扩散理论计算船舶火灾发生率;最后,利用布尔函数和风险矩阵,对船舶火灾事故风险进行评价研究。结果表明:船员不安全行为和船舶设备表面过热、设备短路是船舶火灾事故的关键风险因素;事故后果链中安全栅遭到破坏时,船舶火灾风险处于不希望发生范围内。该方法能有效评估船舶火灾风险的等级,满足海事管理部门的监管工作需求。     

E-bikers’ braking behavior: Results from a naturalistic cycling study

Abstract

Objective: The number of e-bike users has increased significantly over the past few years and with it the associated safety concerns. Because e-bikes are faster than conventional bicycles and more prone to be in conflict with road users, e-bikers may need to perform avoidance maneuvers more frequently. Braking is the most common avoidance maneuver but is also a complex and critical task in emergency situations, because cyclists must reduce speed quickly without losing balance. The aim of this study is to understand the braking strategies of e-bikers in real-world traffic environments and to assess their road safety implications. This article investigates (1) how cyclists on e-bikes use front and rear brakes during routine cycling and (2) whether this behavior changes during unexpected conflicts with other road users.

Methods: Naturalistic data were collected from 6 regular bicycle riders who each rode e-bikes during a period of 2 weeks, for a total of 32.5?h of data. Braking events were identified and characterized through a combined analysis of brake pressure at each wheel, velocity, and longitudinal acceleration. Furthermore, the braking patterns obtained during unexpected events were compared with braking patterns during routine cycling.

Results: In the majority of braking events during routine cycling, cyclists used only one brake at a time, favoring one of the 2 brakes according to a personal pre-established pattern. However, the favored brake varied among cyclists: 66% favored the rear brake and 16% the front brake. Only 16% of the cyclists showed no clear preference, variously using rear brake, front brake, or combined braking (both brakes at the same time), suggesting that the selection of which brake to use depended on the characteristics of the specific scenario experienced by the cyclist rather than on a personal preference. In unexpected conflicts, generally requiring a larger deceleration, combined braking became more prevalent for most of the cyclists; still, when combined braking was not applied, cyclists continued to use the favored brake of routine cycling. Kinematic analysis revealed that, when larger decelerations were required, cyclists more frequently used combined braking instead of single braking.

Conclusions: The results provide new insights into the behavior of cyclists on e-bikes and may provide support in the development of safety measures including guidelines and best practices for optimal brake use. The results may also inform the design of braking systems intended to reduce the complexity of the braking operation.     

不同警告方式下驾驶人应激反应能力模拟试验研究

为研究在道路突发危险场景下先进驾驶辅助系统的不同警告方式对驾驶人应激反应能力的影响，利用自主开发的驾驶人应激反应能力测试软件，以计算机模拟与驾驶模拟器为试验平台，以实际驾驶视频为试验场景，选取操作准确率和反应时间为测试指标，分析不同警告方式下驾驶人的应激反应能力。研究结果表明:视觉警告可有效缩短应激反应时间；视听觉组合警告中，视觉警告占主导作用，听觉警告起辅助作用；在真实场景视频试验环境下，驾驶模拟器模拟试验的操作效果优于计算机模拟试验。     

Improving pedestrian safety using combined HOG and Haar partial detection in mobile systems

Objective: The objective of this study is to develop a novel algorithm on a mobile system that can warn drivers about the possibility of a collision with a pedestrian. The constraints of the algorithm are near-real-time detection speed and a good detection rate.

Method: Histogram of gradients (HOG)-based detection is widely used in pedestrian safety applications; however, it has low detection speed for real-time systems. Hence, it has no direct usage for mobile systems. In order to achieve near-real-time detection speed, partial Haar transform predetections are applied to an image before HOG detection. The partial and HOG detections are merged and a score-based confidence level is defined for the final detection phase. In this way, the outcome is prioritized and different warning levels can be issued to warn the driver before a possible pedestrian collision.

Results: The proposed algorithm provides an increase in detection speed (from 46 to 76 fps) and detection rate (from 80 to 91%) with respect to HOG-based pedestrian detection. It also improves confidence of the results by multidetection merging and score assignment to detections.

Conclusions: Performance improvement of the algorithm is compared with respect to state-of-the-art detectors/algorithms. Based on the detection rate and detection speed performance, it can be concluded that the proposed algorithm is suitable to be used for mobile systems to warn drivers about the possibility of collision with a pedestrian.     

危险化学品火灾爆炸事故不安全动作分布规律研究

以我国210起典型危险化学品火灾爆炸事故为样本,对造成事故的不安全动作进行归类统计,应用云模型云发生器算法计算大类不安全动作的云模型参数值并绘制隶属云图,得出了危险化学品火灾爆炸事故中不安全动作类型和发出者分布规律及特点。结果表明:造成火源型火灾爆炸事故的违规型不安全动作平均发生次数最多,分布不均匀且不稳定,其中现场工作缺乏指导或监护发生次数最多,发出对象为监理;蓄热型火灾爆炸事故中技术型不安全动作分布表现出较大的随机性,其中物料添加不当和隐患排查不彻底发生次数最多,发出对象分别为基层人员和监理。     

Pediatric electric bicycle injuries and comparison to other pediatric traffic injuries

Objective: The objective of this study was to conduct a comprehensive analysis of demographics, injury characteristics and hospital resource utilization of significant pediatric electric bicycle (e-bike) injuries leading to hospitalization following an emergency department visit in comparison to pediatric injuries caused by other traffic related mechanisms.

Methods: A retrospective review of all pediatric traffic injury hospitalizations following an emergency department visit to a level I trauma center between October 2014 and September 2016 was conducted. Data regarding age, sex, number of computed tomography (CT) scans obtained, number of major procedures, length of hospital stay (LOS), Injury Severity Score (ISS), and number of injuries per patient were collected and compared between e-bike injuries and other traffic injuries.

Results: Three hundred thirty-seven admissions were analyzed: 46 (14%) were due to e-bike injuries (29% of patients >12 years). Age, proportion of brain injuries, and use of CT were significantly increased compared to mechanical bicycle injuries (13.1?±?3.4 vs. 10.6?±?3.6, 13% vs. 3%, 1 [0–3] vs. 1 [0–1], P < .01, P = .03, P = .05). Age, LOS, and use of CT were significantly increased compared to injuries caused to automobile passengers (13.1?±?3.4 vs. 7.4?±?5.3, 1 [1–3] vs. 1 [1–2], 1 [0–3] vs. 0 [0–1], P < .01, P = .03, P = .01), as well as ISS and number of injuries per patient (P = .04, P < .01). Injuries caused by e-bikes were similar to injuries caused to pedestrians, except for age (13.1?±?3.4 vs. 8.5?±?3.7, P < .01). Multivariable analysis revealed a significant association between mechanism of injury and ISS, with increased ISS among e-bike injuries compared to mecahnical bike injuries (OR 2.56, CI 1.1–5.88, P = 0.03) and automobile injuries (OR 4.16, CI 1.49–12.5, (P < .01).

Conclusion: E-bikes are a significant cause of severe injury in children compared to most other traffic injuries, particularly in older children.     

跨区域应急协作视角下化工园区空间布局的失衡与应对策略分析

随着工业化进程加快,我国化工园区呈现出数量逐年递增且集中分布的态势,增加了跨区域重大突发事件的风险,如何实现跨区域的应急协作成为化工园区安全规划所面临的重点与难点问题之一。基于此,搜集近15 a全国化工园区的安全事故,采用自组织特征映射神经网络分类方法与多元回归模型研究全国范围内化工园区安全事故的空间分布规律及影响因素,进而从风险分布与应急协作需求的匹配性角度讨论我国现有应急协作机制存在的潜在问题。研究结果表明:我国化工园区整体空间布局呈现出以区域优势为导向的趋势,其选址大多忽略了化工园区集中区域的高风险与应急能力不足的问题,这种布局的失衡使得政府无法形成高效的跨区域应急协作体制;化工园区数量上的增加并非是导致危机事故的主要原因,强调了化工园区自身特征与危机事故严重程度的相关性,区域间应急联动协作在化工园区危机应对过程中的重要作用;最后,从空间布局、跨区域协作角度提出化工园区区域应急协作的相应政策建议。     

基于动态元网络的地铁施工安全事故致因分析

为有效避免地铁施工安全事故的发生,将动态元网络的分析方法应用于地铁施工安全事故致因分析,建立地铁施工安全事故网络节点体系,提出重点控制因素。以杭州地铁一号线施工安全事故为例,在充分考虑人的主观能动性后,建立以人员、行为、事件和组织为节点的节点体系,确定节点的数量及节点之间的相互关系,建立施工安全事故网络模型并进行分析,对打击效果进行对比分析,找到比较理想的打击策略,确立节点的重要度排序,研究结果表明:应重点控制有效的监督相关节点,即可有效地避免或者减少此类事故的发生。得出的结论与事故通报结果基本相符,这为避免地铁施工安全事故提供了新的研究思路。     

未按规定导向车道行驶行为干预方法研究

为保障信号交叉口的正常交通秩序,充分遏制机动车未按规定导向车道行驶行为,亟需探究该行为的影响因素及干预方法。以北京市内4个信号交叉口处共35 h的1 666条监控视频数据为基础,对未按规定导向车道行驶行为进行定义并将其分为9类,分别对频率较高的5类未按规定导向车道行驶行为构建二元Logit模型,以确定其关键影响因素,并据此提出干预方法。结果表明,排队车辆数、大车比例、时段、车流量、照明条件等因素会不同程度地影响5类未按规定导向车道行驶行为的发生概率,其中排队车辆数及时间因素影响最为显著。在此基础上,从交通工程设施及驾驶人安全意识角度,提出优化交叉口渠化设计及信号配时、采用智能标线、强化监管力度及完善交通管控设施、加强驾驶人安全教育4种未按规定导向车道行驶行为干预方法。