道路交通标志逆反射性能与夜间视认性

为了规范和引导道路交通标志及其逆反射材料的使用,提高道路交通标志的夜间视认效果,对交通标志及其逆反射材料的等级、类别、结构、光度性能、色度性能以及国内外相关标准进行分析,研究交通标志的逆反射性能及与夜间视认性的关系。通过对国内交通标志及其逆反射材料的使用现状分析,认为夜间视认性与逆反射性能的关系较为复杂,应针对逆反射材料的类别、结构,以及在交通标志中的搭配使用等进行深入研究,并将研究成果用于修订现行交通标志的相关标准和规范。     

LED主动发光交通标志视认性能研究（双语出版）

为了给道路交通标志的设计与应用提供参考,针对LED点阵式发光标志、LED半透式发光标志和传统逆反射标志,考虑光照强度、闪烁频率、发光形式等因素的影响,进行了交通标志视认性能的驾驶试验研究。采用方差分析研究各个因素对标志视认距离影响的显著性及其交互作用。基于驾驶模拟器进行视认性能对比试验,采用配对样本t检验比较了半透式发光标志与逆反射标志的视认距离,并根据试验数据分析不同光照强度下半透式发光标志的最佳亮度区间。结果表明：在光照强度为200 lx的下午,LED主动发光标志相对于逆反射标志只增加了2~4 m的视认距离;而在光照强度为0.5 lx的晚间,视认效果则提升显著,点阵式和半透式发光标志的视认距离相比逆反射标志分别提升了39.7%和80.4%。闪烁发光无论快慢,对于LED主动发光标志的视认效果均无提升。另外,半透式发光标志的亮度过高时会产生眩光效应,不利于驾驶人辨识标志内容,且最佳亮度区间随着环境光照强度的变化而变化。因此,在光照条件较差的情况下,推荐采用常亮LED半透式发光标志。     

How effective is training for improving traffic sign comprehension? Examining the interaction between training and sign type among motorcyclists

Previous research has demonstrated that training is an effective intervention to increase traffic sign comprehension among road users. However, the effectiveness of training may interact with the characteristics of the sign, and the evidence is still limited. The present study aimed to examine the effects of training and sign type on sign comprehension among motorcyclists. An experiment with 2 (Training: training vs control) x 3 (Sign Type: warning signs vs command signs vs regulatory signs) x 2 (Time: pre-test vs post-test) mixed design was conducted on seventy-nine motorcyclists aged 18 to 27 years old in the greater area of Jakarta. Participants were randomly divided into two groups (training and control), and the experiment was conducted individually using a computer. Sign comprehension level was then measured. Analysis of pre-test and post-test scores revealed that comprehension was lowest for warning signs across groups. A mixed factorial ANOVA showed a significant interaction effect between training and sign type on sign comprehension before and after training, with the group that received training showing the largest improvement in comprehending warning signs. This study implies that training is effective for improving sign comprehension, especially for warning signs, among motorcyclists. Development of training programs should thus take into account the differences among sign types to ensure their effectiveness.     

Comparing the efficiency of different computation intelligence techniques in predicting accident frequency

Until now, considerable efforts have been made to determine which modelling technique performs the best for predicting accident frequency based on crash data. In this regard, the presented study seeks to compare four types of Computational Intelligence (CI) modelling techniques in accident frequency prediction in urban segments, including Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), Hybrid ANFIS-Genetic Algorithm (H-ANFIS-GA), and Hybrid ANFIS-Particle Swarm Optimization (H-ANFIS-PSO). Accordingly, different variables relating to traffic condition and road specifications were employed as independent variables, using the dataset consisting of 1370 crash occurred in Mashhad (Iran), in 2014. According to the results, H-ANFIS-GA exhibited the best performance in forecasting accident frequency. In contrast, PSO did not improve ANFIS performance, and it caused a negative influence on its prediction accuracy. Although the ANFIS model performed better than the developed ANN, it came in the third most accurate models. Additionally, the effect of each independent variable on predicted crash frequency was evaluated using sensitivity analysis.     

基于小波神经网络的隧道变形预测模型研究

针对单一模型在隧道变形预测上精度不高的问题,提出了一种基于小波分析理论的神经网络模型,该模型克服了BP神经网络模型存在的收敛速度慢、结构设计盲目、易陷入局部极小点的缺陷,通过将该模型与时间序列模型、Levenberg-Marquardt法BP神经网络模型、遗传神经网络模型预测的结果比较,可以看出小波神经网络在隧道的变形预测中网络结构更简单、收敛速度更快、预测精度更高。     

基于灰色理论的道路交通事故预测模型综述

交通事故预测模型是进行交通安全控制的关键问题之一。重点介绍了目前几种主要的基于灰色系统理论的道路交通事故预测模型:基本灰色预测GM(1,1)模型、残差灰色预测模型、灰色Verhulst预测模型、灰色马尔可夫预测模型、灰色神经网络预测模型,并指出各模型的适用条件和优缺点,为相关研究提供参考。     

基于径向基函数神经网络的城市道路路段行程时间实时预测模型

提出利用径向基函数（RBF）神经网络方法对城市道路路段行程时间进行建模NN,并结合线圈和视频实测数据进行仿真分析,以实际行程时间和模型输出的行程时间预测值比较验证了模型的合理性。并将RBF神经网络方法与BP神经网络方法进行比较,结果表明RBF神经网络相对于BP神经网络训练时间短,且预测精度更高。     

固定检测器和移动检测器的交通信息融合方法

固定检测器和移动检测器在检测参数、数据精度、覆盖范围、采集成本等方面存在较大差异,具有很强的互补性.分析了固定检测器与移动检测器进行信息融合的必要性,提出了交通信息融合的总体框架.在分别阐述了基于固定检测器和基于移动检测器的区间平均速度估计方法基础上,采用BP神经网络对区间平均速度进行信息融合.以上海市南北高架道路为对象,利用Vissim仿真软件对基于BP神经网络的交通信息融合方法进行了实例分析,结果表明,该方法可以明显提高区间平均速度的精度.     

标志设置的路侧安全性考虑及对策

交通标志是交通安全设施的一种,合理地设置标志有利于交通安全。但它同时又是路侧障碍物的一种,路侧净区内的标志柱对驶出路外的车辆是一个潜在的危害。本文介绍国外路侧净区的概念以及对路侧安全的要求,并建议在满足视认性要求前提下从路侧安全角度出发交通标志设置的考虑和对策。交通标志应首先考虑设置在不易发生车辆驶出路外事故的地方,标志柱可以使用解体消能结构,或者对标志柱进行必要的防护和标识。     

基于遗传算法和神经网络的高路堤沉降预测研究

依据高路堤填土施工期路基沉降实测资料,运用遗传算法和人工神经网络构造了预测路基沉降的等时距遗传神经网络模型, 并且对该网络进行了训练以及仿真,对预测结果运用3次样条插值可得到预测时间段内的任一时刻沉降结果,通过和实测沉降结果对比可知遗传神经网络比人工神经网络节省大量的调试时间和计算时间,而且其预测精度优于BP算法和指数拟合方法,显示其明显的优越性.     

车身反光标识的交通安全效果分析

粘贴车身反光标识能增强车辆的可识别性,提高行车安全,对交通事故主动预防工作有重要意义。在实地调查车身反光标识使用前后交通事故情况的基础上,运用对比分析的方法研究其交通安全效果,希望研究结果能对相关工作提供参考。     

Association of reduced visibility with crash outcomes

Most of the information necessary for driving a vehicle is regarded as visual information. In spite of its importance, visibility conditions at the time of a crash are often not documented at a high level of detail. Past studies have not examined the quantified values of visibility and its association with crashes. The current study merged data collected from the National Oceanic and Atmospheric Administration (NOAA) with 2010–2012 Florida crash data. From the thousands of logged weather events compiled by the NOAA, the researchers isolated periods of normal visibility and comparable periods of reduced visibility in a matched-pairs study. The NOAA data provided real visibility score based on the spatiotemporal data of the crashes. Additionally, the crash data, obtained from Roadway Information Database (RID), contains several geometric and traffic variables that allow for effects of factors and visibility. The study aims to associate crash occurrence under different levels of visibility with factors included in the crash database by developing ordinal logistic regression. The intent is to observe how different visibility conditions contribute to a crash occurrence. The findings indicate that the likelihood of a crash increase during periods of low visibility, despite the tendency for less traffic and for lower speeds to prevail during these times. The findings of this study will add valuable knowledge to the realm of the impact of visibility in the way of using and designing appropriate countermeasures.     

眩光对交通标志视认性影响研究

通过场地实验的方法,研究眩光对交通标志视认性的影响,得出视认性与眩光距离、标志反光膜等级的关系,提出防止和降低眩光对交通标志视认性的影响的措施。     

标志视认性研究中的主观评价

人对道路交通标志的视认性研究是一个涉及多种学科的交叉课题,因此,通常的研究手段和方法不能完全的满足科研的需求。本文在探讨、介绍了人眼视觉系统的结构,归纳、总结了静态、动态视觉的基本特性的基础上,针对试验仪器在标志视认性客观评价上的局限以及人的主观心理作用在视认过程中的影响,制定了几套以道路交通标志上汉字笔画粗细为研究对象的主观评价试验,并得出了一些值得进一步探究的结论。     

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.     

基于图时空神经网络的多充电站负荷协同预测方法

针对传统充电站负荷预测方法只能实现对单一站点预测的问题，提出一种基于图时空神经网络 （Graph Spatiotemporal Neural Network， GSTNN） 模型的多充电站负荷协同预测方法。定义时空信息图，描述充电站负荷之间的时空关系；构建时空特征提取网络，分别利用图卷积神经网络和门控序列卷积网络提取信息图的空间和时间维度信息，并使用长短期记忆网络 （Long Short Term Memory Networks，LSTM） 挖掘影响负荷预测的外部特征信息；融合提取的所有特征，进行负荷预测。算例结果表明，基于 GSTNN模型的方法能充分考虑时空特征和外部特征的影响，协同多个充电站的负荷数据进行预测，并同时输出各充电站的预测结果，有效提高预测准确度，有助于电网稳定运行。     

交通标志牌风荷载计算

该文主要研究了交通标志牌上风荷载的作用,以及通过受力计算来验算交通标志牌基础的稳定性.该文旨在提出一种计算交通标志牌基础稳定性的方法,可供相关专业人员参考.     

上汽自主品牌产品开发的架构策略

分析产品、平台、架构和架构策略的关系;指出架构的推出使平台概念得以拓展和向前延伸;共同工程解决方案和模块化概念使得车型在带宽上生成;架构策略保证资源和效益最优化与产品个性化之间最好的平衡。通过推进自主品牌产品开发的架构策略,上汽最大限度地把握好高效率、高质量和低成本的开发与产品个性化间的平衡,确保在市场竞争中形成荣威、MG系列多种产品。     

基于风险分析的公路工程保险费率厘定

在考虑工程风险及保险实际理赔情况的基础上,形成了含自然灾害、项目环境等7个指标维度的风险评价体系,利用粒子群(PSO)算法优化BP神经网络的初始阈值及权值,建立了公路工程保险费率厘定模型。将该模型应用于34个公路工程保险实际案例,通过PSO-BP神经网络拟合保险样本中风险指标因素与费率之间的关系,实现费率预测。对比分析PSO-BP神经网络与BP神经网络的仿真效果,结果表明,PSO-BP神经网络模型能较好地反映公路工程实际风险水平,预测准确度高,收敛速度快,适用于保险费率厘定。     

逆光条件下交通标志的可视距离研究

交通标志的视认直接影响行车安全。将眼动仪EMR-8B应用到交通安全研究领域中，从影响交通标志视认性的外部因素出发，以大量的现场行车实验为研究手段，探讨了在不同行车速度下，光线条件对交通标志可视距离的影响。并进行了相关性分析。研究结果表明，交通标志的可视距离随车速的提高而降低；同一实验车速，顺光条件下标志的视认性最佳，其次为夜间反光标志，逆光条件下的标志视认性较差。