Relationship between Traffic Related Air Pollutants and Cognitive Function among Elderly in Egypt

Background: Cognitive impairment is a major health issue particularly with the increasing aging population. There are around 47.5 million dementia cases globally. Traffic air pollution issue is a chief environmental problem principally in the mega cities such as Cairo. Methodology: In a Cross sectional and comparative research study the study subjects recruited involved 200 individuals, categorized into two research groups: 100 from Cairo’s elderly home residents and 100 from EL-Gharbaya’s elderly home residents. Results: Statistical linear regression analysis revealed that fine particulate matter, carbon monoxide, and nitric oxide have a statistically significant impact on cognitive function (p values Conclusions: Traffic related air pollutants were strongly associated with cognitive impairment within elderly population in geriatric home residents in Egypt. Regarding to statistically significant difference in concentration of traffic related air pollutants between urban and rural areas, urban areas were more polluted than rural areas.

Nonsaturation Throughput Enhancement of IEEE 802.11b Distributed Coordination Function for Heterogeneous Traffic under Noisy Environment

In this paper, we propose a mechanism named modified backoff （MB） mechanism to decrease the channel idle time in IEEE 802.11 distributed coordination function （DCF）. In the noisy channel, when signal-to-noise ratio （SNR） is low, applying this mechanism in DCF greatly improves the throughput and lowers the channel idle time. This paper presents an analytical model for the performance study of IEEE 802.11 MB-DCF for nonsaturated heterogeneous traffic in the presence of transmission errors. First, we introduce the MB-DCF and compare its performance to IEEE 802.11 DCF with binary exponential backoff （BEB）. The IEEE 802.11 DCF with BEB mechanism suffers from more channel idle time under low SNR. The MB-DCF ensures high throughput and low packet delay by reducing the channel idle time under the low traffic in the network. However, to the best of the authors＇ knowledge, there are no previous works that enhance the performance of the DCF under imperfect wireless channel. We show through analysis that the proposed mechanism greatly outperforms the original IEEE 802.11 DCF in the imperfect channel condition. The effectiveness of physical and link layer parameters on throughput performance is explored. We also present a throughput investigation of the heterogeneous traffic for different radio conditions.

Evolution of Air Traffic Management Concept of Operations and Its Impact on the System Architecture

The air traffic management system(ATM)has the task of ensuring safe,orderly and expeditious flow of air traffic.The ATM system architecture is very much dependent on the concept of operations(ConOps).Over the years the evolution in ConOps has resulted in changes in the ATM′s physical architecture,improving its physical infrastructure,increasing the levels of automation and making operational changes to improve air traffic flow,to cope with increasing demand for air travel.However,what is less clear is the impact of such changes in ConOps on the ATM′s functional architecture.This is vital for ensuring optimality in the implementation of the physical architecture components to support the ATM functions.This paper reviews the changes in the ConOps over the years,proposes a temporally invariant ATM functional model,and discusses some of the main key technologies expected to make significant improvements to the ATM system.

Traffic chaos and its prediction based on a nonlinear car-following model

This paper discusses the dynamic behavior and its predictions for a simulated traffic flow based on the nonlinear response of a vehicle to the leading car＇s movement in a single lane. Traffic chaos is a promising field, and chaos theory has been applied to identify and predict its chaotic movement. A simulated traffic flow is generated using a car-following model（ GM model）, and the distance between two cars is investigated for its dynamic properties. A positive Lyapunov exponent confirms the existence of chaotic behavior in the GM model. A new algorithm using a RBF NN （radial basis function neural network） is proposed to predict this traffic chaos. The experiment shows that the chaotic degree and predictable degree are determined by the first Lyapunov exponent. The algorithm proposed in this paper can be generalized to recognize and predict the chaos of short-time traffic flow series

Traffic Sign Recognition Based on CNN and Twin Support Vector Machine Hybrid Model

With the progress of deep learning research, convolutional neural networks have become the most important method in feature extraction. How to effectively classify and recognize the extracted features will directly affect the performance of the entire network. Traditional processing methods include classification models such as fully connected network models and support vector machines. In order to solve the problem that the traditional convolutional neural network is prone to over-fitting for the classification of small samples, a CNN-TWSVM hybrid model was proposed by fusing the twin support vector machine (TWSVM) with higher computational efficiency as the CNN classifier, and it was applied to the traffic sign recognition task. In order to improve the generalization ability of the model, the wavelet kernel function is introduced to deal with the nonlinear classification task. The method uses the network initialized from the ImageNet dataset to fine-tune the specific domain and intercept the inner layer of the network to extract the high abstract features of the traffic sign image. Finally, the TWSVM based on wavelet kernel function is used to identify the traffic signs, so as to effectively solve the over-fitting problem of traffic signs classification. On GTSRB and BELGIUMTS datasets, the validity and generalization ability of the improved model is verified by comparing with different kernel functions and different SVM classifiers.

A control method applied to mixed traffic flow for the coupled-map car-following model

In light of previous work [Phys. Rev. E 60 4000 （1999）], a modified coupled-map car-following model is proposed by considering the headways of two successive vehicles in front of a considered vehicle described by the optimal velocity function. The non-jam conditions are given on the basis of control theory. Through simulation, we find that our model can exhibit a better effect as p = 0.65, which is a parameter in the optimal velocity function. The control scheme, which was proposed by Zhao and Gao, is introduced into the modified model and the feedback gain range is determined. In addition, a modified control method is applied to a mixed traffic system that consists of two types of vehicle. The range of gains is also obtained by theoretical analysis. Comparisons between our method and that of Zhao and Gao are carried out, and the corresponding numerical simulation results demonstrate that the temporal behavior of traffic flow obtained using our method is better than that proposed by Zhao and Gao in mixed traffic systems.

Fuzzy Logic Inference Applications in Road Traffic and Parking Space Management

In modern motoring, many factors are considered to realize driving convenience and achieving safety at a reasonable cost. A drive towards effective management of traffic and parking space allocation in urban centres using intelligent software applications is currently being developed and deployed as GPS enabled service to consumers in automobiles or smartphone applications for convenience, safety and economic benefits. Building a fuzzy logic inference for such applications may have numerous approaches such as algorithms in Pascal or C-languages and of course using an effective fuzzy logic toolbox. Referring to a case report based on IrisNet project analysis, in this paper Matlab fuzzy logic toolbox is used in developing an inference for managing traffic flow and parking allocation with generalized feature that is open for modification. Being that modifications can be done within any or all among the tool’s universe of discourse, increment in the number of membership functions and changing input and output variables etc, the work here is limited within changes at input and output variables and bases of universe of discourse. The process implications is shown as plotted by the toolbox in surface and rule views, implying that the inference is flexibly open for modifications to suit area of application within reasonable time frame no matter how complex. The travel time to the parking space being an output variable in the current inference is recommended to be substituted with distance to parking space as the former is believed to affect driving habits among motorist, whom may require the inference to as well cover other important locations such as nearest or cheapest gas station, hotels, hospitals etc.

Utility-based and fairness-aware radio resource allocation in OFDMA cellular relay networks with traffic prioritization

The combination of orthogonal frequency division multiple access(OFDMA) with relaying techniques provides plentiful opportunities for high-performance and cost-effective networks.It requires intelligent radio resource management schemes to harness these opportunities.This paper investigates the utility-based resource allocation problem in a real-time and non-real-time traffics mixed OFDMA cellular relay network to exploit the potentiality of relay.In order to apply utility theory to obtain an efficient tradeoff between throughput and fairness as well as satisfy the delay requirements of real-time traffics,a joint routing and scheduling scheme is proposed to resolve the resource allocation problem.Additionally,a low-complexity iterative algorithm is introduced to realize the scheme.The numerical results indicate that besides meeting the delay requirements of real-time traffic,the scheme can achieve the tradeoff between throughput and fairness effectively.

基于国内外实际案例的中运量公共交通功能定位分析

公共交通优先是交通强国建设的重要组成部分,相较于地铁昂贵的建设和运营费用,以及常规公交的低速,可靠性差等问题,中运量公共交通正受到越来越多的关注。文章通过巴黎和东京的实例,对照上海和北京等城市的相关线路,深入分析其功能定位及运营表现,揭示其背后的成功或失败原因。文章旨在为我国城市公共交通规划、设计与管理提供借鉴,推动中运量公共交通的健康发展。

基于轻量化YOLOv5的交通标志检测

为了提高道路交通标志的检测速度,提出一种基于轻量化YOLOv5的改进模型。首先,使用Ghost卷积和深度分离卷积(DWConv)构建新的主干模块,减少计算量和参数量;引入加权特征融合网络(BiFPN)结构,增强特征融合能力;将CIoU损失函数替换为SIoU损失函数,关注真实锚框与预测的角度信息,提升检测精度。其次,对TT100K数据集进行优化,筛选出标签个数大于200的交通标志图片和标注信息共24类。最后,实验结果取得84%的准确率、81.2%的召回率和85.4%的所有类别平均精确率的平均值mAP@0.5,相比原始YOLOv5,参数量减少29.0%,计算量减少29.4%,mAP@0.5仅下降0.1百分点,检测帧率提升了34帧/s。使用改进后的模型进行检测,检测速度有了明显提升,基本达到了在保持检测精度的基础上压缩模型的目的。

交通防护服优化设计

本文通过对交通防护服现状进行分析,提出了目前防护服存在的被动保护、安全性能低等问题,通过研究提出智能化、多功能化、轻量化的智能化多功能交通安全防护服代替普通防护服,来使穿戴者采取主动保护措施,降低安全事故。

大型医院交通集散空间布局规划及优化研究

近年来医院发展朝着大型化和综合化形式迈进,同时也带来了一系列交通问题。本文就医院外部集散空间布局形式进行研究并改进,以缓解大型医院外部的交通拥堵混乱问题。通过分析大型医院外部空间的使用需求,重新界定了医院交通集散空间的范围与功能;结合其空间流线需求与建筑广场、城市道路设计理论,分析了医院集散空间的交通特征与规划要点;最后提出了各功能区布局原则,并从医院内外临时停车、交通设施布置、道路功能划分等方面阐明了医院交通集散空间的规划方法。针对不同形式医院的布局,分别设置了复合立体型、平面广场型和用地紧缺性三种形式,为城市规划设计者提供参考。

基于易损性的铁路桥梁通行功能评估研究

为对铁路桥梁震后状况进行合理评估,基于易损性构建了一套铁路桥梁震后通行能力评估方法,以国内常见铁路桥梁为参考,使用OpenSees建立了20座不同构造参数的4跨简支梁桥全桥有限元模型,基于耐震时程地震分析进行易损性与通行功能的评估,厘清了影响铁路桥梁地震损伤与通行能力的主要因素。研究表明:纵筋配筋率和剪跨比是影响铁路桥梁地震损伤的核心因素,轴压比是影响铁路桥梁地震损伤的主要因素,体积配箍率是影响铁路桥梁地震损伤的次要因素,为控制桥梁地震损伤,可以提高纵筋配筋率,降低剪跨比;配筋率增加通行能力会明显提升,剪跨比增加通行能力明显下降,配箍率和轴压比增加通行能力会小幅提升;纵筋配筋率与剪跨比是影响桥梁通行能力的主要因素,体积配箍率与轴压比是影响桥梁通行能力的次要因素,为保证通行能力最优,应适当提高纵筋配筋率或减小桥墩剪跨比,也可适当提高体积配箍率与轴压比。本研究提出的通行能力评估方法可以较好地评估桥梁结构通行功能,用于震后评估工作。

时空相关的道路网络短时交通流预测模型

为有效解决复杂路网短时交通流预测问题中涉及的时空特征挖掘问题,提出一种基于改进长短时记忆神经网络(Improved Long Short-Term Memory, ILSTM)的交通流预测模型.首先,通过改进的遗传算法对长短时记忆神经网络(Long Short-Term Memory, LSTM)模型初始参数进行优化获得最优参数组合,解决LSTM初始参数设置对输出结果影响较大的问题.其次,针对复杂路网多路段交通流预测中遇到的空间特征提取问题,通过挖掘相关路段对目标路段交通流预测的影响程度,重新构建LSTM模型的损失函数,采用路网中相关路段对目标路段的影响系数,以损失函数输出值最小为终止条件,构建ILSTM模型.最后,选择加州公路局交通数据进行模型验证实验,采用遗传算法优化LSTM模型(Genetic Algorithm-LSTM, GA-LSTM)和单纯LSTM模型,以及皮尔森相关系数与LSTM组合模型(Pearson Correlation Coefficient-LSTM,PCC-LSTM),对工作日和周末数据的多次实验结果进行对比分析.实验结果表明:ILSTM模型能够充分考虑复杂路网交通流的时间和空间特征,预测平均误差约为1.16%,在收敛效率和预测精度方面均优于其他模型.

城市轨道交通系统的层次化功能结构解析——以上海为例

解析城市轨道交通系统的功能结构对于建立以轨道交通为骨架的城市空间结构至关重要,其有助于优化城市空间布局,促进交通与土地利用融合,进而推动城市可持续发展。本文利用交通刷卡大数据,基于轨道交通站域的功能相似性和邻接关系提出了功能站组的概念,并形成了一套“站域功能分类—站组范围划定—站群结构识别”的方法体系。以上海市轨道交通系统为例,针对单个站域,构建表征站域土地利用功能的连续客流时间序列,并依据时间序列特征分类得到站域功能类型;将多个具有相似的出行模式和土地利用功能的相邻站域划定为功能站组;以功能站组为基本单元,采用社区发现算法,对功能站组间的客流交互网络进行分析以识别站群。研究结果表明,城市轨道交通系统的“站域—站组—站群”层次化功能结构解析方法综合了场所空间和流空间视角,有助于认识特大城市轨道交通系统的功能结构特征,并为轨道交通系统的发展提供多层次的空间优化建议。

基于OpenStreetMap的城市公交专用道设置优化

为合理设置公交专用道,通过OpenStreetMap(OSM)提取可设置公交专用道的路网,采用Stroke道路表示法将OSM路网与百度路网进行融合,结合百度地图实时交通数据实现交通态势可视化,从而准确直观地识别出公交专用道的设置路段。通过构建公交路阻函数模型,计算设置公交专用道前后的时间效益差,确定公交专用道的设置方案。最后以南昌市部分路网为例,通过SUMO软件仿真结果表明:在青山南路设置公交专用道可以使公交车辆延误降低19.6%;社会车辆延误降低10.8%。实例验证所提出的设置优化方法有利于减少交通延误,为合理设置公交专用道提供支持。

城市道路全要素设计研究——以广州市为例

城市道路设计主要强调机动交通的工程性指标,缺乏对人本需求下的街道要素进行系统分析与梳理,无法满足出行者对高品质街道空间的需求。在对国内外街道设计研究进行总结的基础上,提出城市道路全要素设计技术要点,通过构建人群行为与街道品质的交互规律及其与街道要素间的深层映射关系,实现对城市街道空间的精细化设计与优化。《广州市城市道路全要素设计手册》探索建立了一套人本需求下道路分类—功能模块—全要素匹配的城市道路全要素设计体系,形成7大类23小类道路-街道功能分类体系,并总结9个功能模块的推荐形式、适用范围以及设计要点,梳理街道6大系统90项要素的设计指引。以广州市街道改造实践项目为例,阐述了以人为本理念下街道品质评价、步行和非机动车交通优先设计以及协同一体化设计方法的应用。

基于级联卷积神经网络的城市交通汽车目标图像检测方法

由于现有的城市交通汽车目标检测方法主要使用神经网络来定位回归检测区域,这种方法忽略了特征在损失函数中的关联性,易受到场景立体匹配距离变化的影响,进而导致平均检测精度较低。基于级联卷积神经网络,设计一种全新的城市交通汽车目标图像检测方法。提取城市交通汽车目标检测特征,采用逐级匹配法提升检测样本质量。不同类型的检测目标的IOU分配阈值不同,利用Iterative Bbox at Inference级联卷积神经网络进行分类回归处理,得到基于级联神经网络的汽车目标检测损失函数,对于每个栅格,需要预设先验框根据损失函数,计算预测参数,设计城市交通汽车多目标检测算法,从而实现城市交通汽车目标检测。实验结果表明:该设计方法的平均检测精度较高,说明所设计方法的检测效果较好,具有较高的准确性,有一定的应用价值,能够为城市交通安全性的提升作出一定的贡献。

超高速低真空磁浮交通试验基地总平面布置研究

超高速低真空磁浮交通作为一种颠覆性的交通制式,将会在一定程度上改变人类生产和生活方式,成为引领世界交通技术发展的标志性抓手。但同时它也是一项超大型的复杂系统工程,在真正落地前,需要进行反复的工程验证试验。国内目前建成的仅用于仿真模拟的缩比试验线,还远达不到系统性工程验证试验的目的。在此背景下,需要建设功能丰富、配套齐全的专用试验基地来满足超高速低真空磁浮交通系统总体方案及关键技术的试验需求。以山西省大同市阳高县超高速低真空磁浮交通试验基地建设为例,首先论述试验基地的总体概况、不同时期的功能定位;然后根据试验需求确定试验流程,围绕试验流程进一步确定了试验基地的构成;随后进行试验基地总平面布置原则和重难点分析,提出试验基地总平面布置时对房屋、道路、试验线及综合管线等方面的具体技术要求。结合以上控制因素,通过总平面布置方案比选,形成节约投资、用地集中、紧贴试验需求的总平面布置方案,最后对试验基地总平面设计的核心及关键、需要注意的问题及今后的发展研究方向做出了建议与展望。