高速公路沥青路面试验检测技术研究

沥青路面是高速公路的重要组成部分,其需要承担道路荷载、车辆重量等,因此对其承载力、性能、质量要求较高,需要在高速公路工程建设中对路面进行科学、合理的检测,及时发现路面存在的质量问题并进行维护和处理,从而节约后期维修成本,确保后期车辆的安全、稳定运行。基于此,对高速公路沥青路面试验检测技术进行介绍,并对高速公路沥青路面试验检测技术的具体应用进行分析,以期为高速公路沥青路面施工提供参考。     

近场水平波动下局部坡地地形对场地反应的影响规律

文章采用人工边界从半无限土体中截取出局部坡地作为计算区域,并从边界的底部垂直输入水平方向振动的脉冲位移波,验证了人工边界的设置及水平波动输入法的正确可行性。分析了不同的土质边坡在同一坡角下,坡高和观测点位置对场地反应产生的不同影响。     

高速公路监控系统

高速公路监控系统主要包括交通监控、交通信息和气象信息的采集以及交通疏导.通过在高速公路沿线、立交、收费广场等设置的摄像机,将信号传输至监控中心集中监控,从而实现交通状况图像监控;通过在沿线关键位置设置车辆计数器、车辆测速器、气象资料采集器,并把信号传输至监控中心集中处理,实现交通信息和气象信息的采集;通过安装于道路中央分隔带或路边的可变限速标志和可变情报板,从监控中心发布交通疏导和交通控制信息.随着道路交通智能化的发展,监控系统是高速公路交通管理中不可缺少的重要手段.     

基于全景展开图像的隧道衬砌渗漏水数字化识别方法研究

文章通过隧道衬砌展开影像生成器软件对隧道衬砌渗水区域采集影像进行特征点提取、图像匹配、模型识别、图像展开与拼接处理,得到隧道衬砌的全景展开图像。利用图像分割与处理技术,对全景图像采用灰度图像转换、阀值选取、二值图像开闭运算等原理实现对图像渗水区域信息的识别与修正,并基于贵州某运营公路隧道衬砌渗漏水实例,经过若干算法程序提取出该隧道渗水病害的位置、面积及病害长度等特征信息,建立了一套隧道衬砌渗漏水病害的数字化识别系统,可为隧道衬砌渗漏水检测提供一种便捷、高效、全面的综合检测手段。     

基于货运车辆预警数据的高速公路危险驾驶热点路段识别

本文以货运车辆车联网数据中的车辆碰撞预警、疲劳预警和超速预警数据为研究对象,针对G4高速公路北京和河北境内道路上的三种预警热点路段进行识别。运用全局空间自相关性分析方法得出三种预警数据在道路空间中不是随机分布,而是集中在某一个或几个路段上;进一步采用局部空间相关性分析方法识别出三种不同预警类型下对应的热点路段。交通管理部门可根据识别出的预警热点路段提出针对性的管控措施,有助于缓解交通事故的发生,提高货运安全水平。     

公路纵面视距行车安全性分析

驾驶员应有足够的视距来判断运行车辆前方环境发生的变化,如交通状况、道路线形组合形式及沿线景观设计等,以便采取相应措施确保汽车安全行驶。文章从道路纵面竖曲线设计原理出发,讨论了汽车安全行驶的条件,推导出停车视距与竖曲线设计长度、变坡角的关系,同时结合某高速公路安全性评价,对事故多发路段提出了合理化建议。     

高速公路附属区污水处理技术

针对高速公路附属区污水的特点分析其处理存在问题,并对现有高速公路附属区污水处理工艺和多点进水A/O工艺进行比较分析,发现多点进水A/O工艺具有运行稳定、去除率高、运行成本低等特点,适用于高速公路附属区的污水处理。     

基于三维探地雷达的襄荆高速路面病害现状分析

作为一种新型无损道路检测技术,三维探地雷达在道路病害检测方面具有更高的准确性和可靠性,对于道路施工质量评估以及养护方案设计等方面具有重要的参考价值。本论文对三维探地雷达及其工作原理进行简要阐述,就襄荆高速病害整体检测项目中所得出的检测路段病害信息,分析总结出各条检测路段的病害数量、种类、分布特点、分布位置等,详细讨论其在道路病害检测中的实际应用。     

视频事件检测和图像筛选技术在永武高速公路视频监控系统的运用

事件检测是智能交通系统中的重要组成部分和关键技术。文中结合当前我省高速公路监控系统已有监控设施,对基于视频图像的交通事件检测进行了研究,并完成了系统的开发和运用。     

浅谈宁夏沥青路面维修施工平整度控制

路面平整度是衡量高等级道路性能的一项重要指标,文章结合宁夏国道211高速沥青路面加铺工程,采用车载式路面激光平整度仪(利普勒斯LIPRES平整度仪)对国道211高速公路已铺下面层进行了分幅分车道加密(40m/点)检测,并依据该检测结果进行人工现场调查、分析,提出相应的平整度控制措施,为宁夏地区沥青路面加铺维修工程平整度控制提供了技术支持。     

Where are the dangerous intersections for pedestrians and cyclists: A colocation-based approach

Pedestrians and cyclists are vulnerable road users. They are at greater risk for being killed in a crash than other road users. The percentage of fatal crashes that involve a pedestrian or cyclist is higher than the overall percentage of total trips taken by both modes. Because of this risk, finding ways to minimize problematic street environments is critical. Understanding traffic safety spatial patterns and identifying dangerous locations with significantly high crash risks for pedestrians and cyclists is essential in order to design possible countermeasures to improve road safety. This research develops two indicators for examining spatial correlation patterns between elements of the built environment (intersections) and crashes (pedestrian- or cyclist-involved). The global colocation quotient detects the overall connection in an area while the local colocation quotient identifies the locations of high-risk intersections. To illustrate our approach, we applied the methods to inspect the colocation patterns between pedestrian- or cyclist-vehicle crashes and intersections in Houston, Texas and we identified among many intersections the ones that significantly attract crashes. We also scrutinized those intersections, discussed possible attributes leading to high colocation of crashes, and proposed corresponding countermeasures.     

Distinguishing the land use effects of road pricing based on the urban form attributes

This paper studies the effects of road pricing on land use under different development scenarios (business as usual scenario and transit oriented development scenario) by a quantitative method, which combines the integrated land use and transport interaction model (TRANUS model) with the scenario-planning techniques. Moreover, in order to further analyze the differences of the land use effects of road pricing on traffic analysis zones (TAZs) with different urban form attributes, a quantitative classification method combining factor analysis and cluster analysis is then used to quantitatively classify TAZs. The results demonstrate that the effects of road pricing on the land use of a specific region depend on the urban form attributes of the region. The higher the densities of employments and population, and better street design (high densities of street and intersections) and public transportation condition, the less the region is negatively affected by road pricing, and vice versa. More importantly, rail transit can alleviate the negative impact of road pricing on commercial development and population concentration of the region. Therefore, before introducing a road pricing policy, it is necessary to develop public transport system, especially rail transit.     

Modelling greenhouse gas emissions from cars in Great Britain

This paper outlines the issues involved in the problem of global warming. The road transport sector's contributions to this problem are then detailed and various policy options to reduce greenhouse gas emissions from private cars are discussed. The paper then describes a model which forecasts greenhouse gas emissions from cars. The effects of various policy options are then modelled and the results compared. Policies considered include: raising fuel prices in terms of the UK government's commitment to increase road fuel duties; subsidising public transport in terms of reduced public transport fares; and a tax differentiated by engine size.     

Global optimization method for network design problem with stochastic user equilibrium

In this paper, we consider the continuous road network design problem with stochastic user equilibrium constraint that aims to optimize the network performance via road capacity expansion. The network flow pattern is subject to stochastic user equilibrium, specifically, the logit route choice model. The resulting formulation, a nonlinear nonconvex programming problem, is firstly transformed into a nonlinear program with only logarithmic functions as nonlinear terms, for which a tight linear programming relaxation is derived by using an outer-approximation technique. The linear programming relaxation is then embedded within a global optimization solution algorithm based on range reduction technique, and the proposed approach is proved to converge to a global optimum.     

Modelling dual carriageway lane changing using neural networks

Neural networks offer a potential alternative method of modelling driver behaviour within road traffic systems. This paper explores the application of neural networks to modelling the lane-changing decisions of drivers on dual carriageways. Two approaches are considered. The first, preliminary approach uses a prediction type of neural network with a single hidden layer and the back propagation learning algorithm to model the behaviour of an individual driver. A series of consecutive time-scan traffic patterns, which describe the driver's environment and changes over time as the selected vehicle travels along a link, are input to the neural network, which then predicts the new lane and position of the vehicle. Training data are collected from a human subject using an interactive driving simulation. The trained neural network successfully exhibited the rudiments of driving behaviour in terms of lane and speed changes. A major disadvantage of this approach was the difficulty in recording real-life data, which are required to train the neural network, for individual drivers. The second approach concentrates specifically on lane changing and makes use of a learning vector quantization classification type of neural network. Input to the neural network still consists primarily of time-scan traffic patterns, but the format is changed to facilitate the possibility of data acquisition using image processing. The neural network output classifies the input data by determining the new lane for the vehicle concerned. Performance in both testing and training was very good for data generated by the rule-based driver-decision model of a microscopic simulation. Performance in testing was less satisfactory for data taken directly from a road and highlighted the need for extensive data sets for successful training.     

Adapt-Traf: An adaptive multiagent road traffic management system based on hybrid ant-hierarchical fuzzy model

Usually, road networks are characterized by their great dynamics including different entities in interactions. This leads to more complex road traffic management. This paper proposes an adaptive multiagent system based on the ant colony behavior and the hierarchical fuzzy model. This system allows adjusting efficiently the road traffic according to the real-time changes in road networks by the integration of an adaptive vehicle route guidance system. The proposed system is implemented and simulated under a multiagent platform in order to discuss the improvement of the global road traffic quality in terms of time, fluidity and adaptivity.     

Construction of traffic state vector using mutual information for short-term traffic flow prediction

Short-term traffic flow prediction is an integral part in most of Intelligent Transportation Systems (ITS) research and applications. Many researchers have already developed various methods that predict the future traffic condition from the historical database. Nevertheless, there has not been sufficient effort made to study how to identify and utilize the different factors that affect the traffic flow. In order to improve the performance of short-term traffic flow prediction, it is necessary to consider sufficient information related to the road section to be predicted. In this paper, we propose a method of constructing traffic state vectors by using mutual information (MI). First, the variables with different time delays are generated from the historical traffic time series, and the spatio-temporal correlations between the road sections in urban road network are evaluated by the MI. Then, the variables with the highest correlation related to the target traffic flow are selected by using a greedy search algorithm to construct the traffic state vector. The K-Nearest Neighbor (KNN) model is adapted for the application of the proposed state vector. Experimental results on real-world traffic data show that the proposed method of constructing traffic state vector provides good prediction accuracy in short-term traffic prediction.     

GPS-based speed collection method for road traffic noise mapping

One of the most difficult and expensive tasks in making noise pollution maps is the collection and processing of the data needed to create acoustic models. In the case of road traffic noise maps, obtaining speed data for light and heavy vehicles a problem that has usually been avoided by using a road’s speed limit or by making assumptions based on experience from similar road types. Here global positioning systems-based techniques are applied for acquiring vehicle speed data and adapted to fulfill the requirements of noise prediction models.     

Spatial impacts of road pricing: Accessibility, regional spillovers and territorial cohesion

Road pricing policies are gaining prominence in EU countries. These policies have positive impacts leading to mobility patterns which are socially and environmentally more desirable, but they also have negative impacts. One negative impact is to be found in regional accessibility, due to the increase in generalized transport costs. This study presents a methodology based on accessibility indicators and GIS to assess the accessibility impacts of a road pricing policy. The methodology was tested for the Spain’s road network considering two road pricing scenarios. It enables not only the more penalized regions to be identified but also negative road pricing spillover effects between regions. These effects are measured in terms of accessibility changes occurring in one region produced by charges implemented in another region. Finally, the study of accessibility disparities (by calculating inequality indexes for each of the scenarios considered), provides policymakers with useful information regarding the impact of road pricing policies from the point of view of territorial cohesion.     

Commercializing management and financing of roads in developing and transition countries

The road sector reforms undertaken by a growing number of developing and transition countries during the past 10-20 years are described. The problems these countries share with industrialized countries and the additional factors that also affect them--large backlogs of deferred maintenance, an acute shortage of funds and dysfunctional road agencies--are also described. The paper then describes how these countries have restructured road management by separating planning and management of roads from implementation of works, provided road agencies with more autonomy, created more effective oversight, introduced annual performance agreements and turned the road agency into a more commercial organization paying market-based wages. In relation to road financing, road tolls and toll roads are touched on briefly before the new style road funds that have recently been set up in these countries are detailed. The paper then describes how they were established and their basic operating modalities--revenues only from charges related to road use (with one notable exception), no abstraction of revenues from other sectors, proactive management and oversight provided by a board of directors that includes members nominated by road users and the business community.