科学管理货运驾培

货物运输驾驶员是我国道路交通安全的主要源头,也是我国发展道路运输的主力军。那么,如何优化货运驾驶员职业培训及评价和管理,从而进一步提升货运驾驶员综合素质呢？     

视觉分析在道路设计中的应用

道路交通是社会经济发展、人们出行的重要基础,安全、舒适的道路设计是提升驾驶员行车效率的关键.从诸多道路交通安全事故原因中进行分析,发现为数不少的交通事故与驾驶员的视错觉、视觉疲劳、通视范围受限等情况有关.视觉分析是帮助设计师提高道路设计质量、减少道路错觉的一种手段,应用在道路设计中能够起到使行车更安全、更舒适的效果.本文主要分析常见的道路视觉问题,探讨视觉分析在道路设计中的具体应用.     

驾驶员判断决策失误风险评估方法的研究

在分析目前道路交通安全研究现状的基础上,确立了评价驾驶员判断决策失误风险的指标体系,利用AHP法确定了各评价指标的权重,并运用模糊综合评估理论建立了驾驶员判断决策失误风险的模糊综合评估模型.     

道路交通安全与驾驶员的因素探索

通过对道路交通安全与驾驶员的因素探索,分析道路交通事故产生的主要原因即人的因素,提出了安全行车的措施和建议。     

基于IDM的车辆弯道运动状态预测研究

弯道是道路交通安全事故频发的隐患路段之一,为快速准确预测车辆弯道运动状态,本研究基于智能驾驶员微观车辆跟驰模型(Intelligent Driver Model,IDM),通过弯道适应性改进,构建了车辆弯道运动状态预测算法.模型的弯道适应性改进考虑了弯道曲率半径、道路纵向坡度及驾驶员反应时间因素,并对改进优化后的预测算法进行参数标定,以保证预测算法能达到预期效果.     

交通仿真在道路安全评价中的应用

我国道路交通的安全问题由于其严重程度的加剧正越来越受到人们的关注,因此,对于道路安全性的评价非常必要.事故统计方法和交通冲突方法是现在应用较多的道路安全评价方法,但这两种方法都存在着一定的不足.鉴于此,提出了一种基于交通仿真的道路安全评价方法并用一个范例介绍了应用交通仿真对道路进行安全性评价的基本过程.     

运用新技术落实企业安全生产主体责任—对驾驶员不安全行为控制技术的探讨

大量的不安全驾驶行为是造成道路交通安全事故的重要原因，加强道路运输企业驾驶员不安全行为的控制技术的运用和研究，能有效减少和杜绝不安全行为，从而减少交通安全事故的发生。本文在分析驾驶员不安全行为的基础上，从驾驶员不安全行为的控制技术运用角度入手，分别就提高驾驶员安全意识、技能和加强外部监督管理两个方面的控制技术进行了初步的探讨，对进一步落实企业安全生产主体责任具有一定的现实意义。     

道路交通安全何不一家管

治超中暴露出的管理体制问题,削弱了道路交通安全监管的力度和效果.只有理顺管理体制,才能进一步加强道路交通安全管理.管理体制掣肘道路运输系统.     

GPS在智能运输系统中的应用

1.前言 智能运输系统(ITS)是把先进的检测、通信和计算机技术综合应用于由汽车和道路构成的道路交通运输系统中,使道路交通管理全面智能化的一项大规模综合技术.从而,彻底改变道路交通面貌,大大提高道路的通过能力、运输效率和安全性.随着全球定位系统GPS的日益发展,这项高科技在智能运输系统中也得到广泛应用.目前,主要是应用在汽车导航及汽车的运营管理中.     

交叉口出租车变道行为影响因素分析

为了深入了解出租车驾驶员在交叉口的具体变道行为表现,本文基于问卷调查结果数据对出租车驾驶员的基本属性相关信息、驾驶员在交叉口的变道属性、相关道路环境和乘客信息对驾驶员的影响进行分析,使用SPSS软件对得到的数据调查结果进行相关性处理,分析出各因素之间的关系,得出出租车驾驶员在交叉口的相关变道特性。调查结果表明:出租车驾驶员这一职业具有一定稳定性;在变道行为中,乘客下客要求是出租车驾驶员在交叉口变道的最主要原因;交叉口的监控设施对出租车的违法变道行为有着显著的约束效果;部分出租车驾驶员存在习惯性变道行为。     

Effect of variable message signs on driver speed behavior on a section of expressway under adverse fog conditions—A driving simulator approach

Variable message signs (VMS) are used to provide dynamic information and one current application is to show different speed limits under different conditions. As speed is an important contributor to road accidents and also affects driver speed behavior, the present study focuses on how effective traffic advisory information is when helping drivers to divert from potentially dangerous conditions. Graphical representation of an Expressway section made it easy to isolate the effects of speed etc. by drivers with information provided through VMS under adverse fog conditions. Understanding and reacting to the VMS system by drivers is essential for its success. If drivers do not react by changing speed behavior then the VMS system will fail and further implementation may cease. In this paper an Analysis of Variance model, which is appropriate to the proposed experimental conditions, is used to study how subjects (drivers) will perceive provided information and also to find the effect of VMS on driver speed behavior on the simulated Expressway section.     

Efficient intersection control for minimally guided vehicles: A self-organised and decentralised approach

An important question for the practical applicability of the highly efficient traffic intersection control is about the minimal level of intelligence the vehicles need to have so as to move beyond the traffic light control. We propose an efficient intersection traffic control scheme without the traffic lights, that only requires a majority of vehicles on the road to be equipped with a simple driver assistance system. The algorithm of our scheme is completely decentralised, and takes into full account the non-linear interaction between the vehicles at high density. For vehicles approaching the intersection in different directions, our algorithm imposes simple interactions between vehicles around the intersection, by defining specific conditions on the real-time basis, for which the involved vehicles are required to briefly adjust their dynamics. This leads to a self-organised traffic flow that is safe, robust, and efficient. We also take into account of the driver comfort level and study its effect on the control efficiency. The scheme has low technological barrier, minimal impact on the conventional driving behaviour, and can coexist with the traffic light control. It also has the advantages of being easily scalable, and fully compatible with both the conventional road systems as well as the futuristic scenario in which driverless vehicles dominate the road. The mathematical formulation of our scheme permits large scale realistic numerical simulations of busy intersections, allowing a more complete evaluation of the control performance, instead of just the collision avoidance at the intersection.     

Safety in the road environment: a driver behavioural response perspective

The existing literature on road safety suggests that a driver's perception of safety is an important influence on their driving behaviour. A challenging research question is how to measure the perception of safety given the complex interactions among drivers, vehicles and the road setting. In this paper, we investigate a sample of driver evaluations of the perception of safety associated with a set of typical road environments. A roundabout was selected as the context for the empirical study. Data was obtained by a computerised survey using the video-captured road and traffic situations. A controlled experiment elicited driver responses when faced with a mixture of attributes that describe the roundabout environment. An ordered probit model identified the contribution of each attribute to the overall determination of the perception of safety. An indicator of perceived safety was developed for a number of typical road and traffic situations and for different driver segments.     

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.     

Effects of different factors on drivers’ guidance compliance behaviors under road condition information shown on VMS

It is generally accepted that compliance behavior is affected by many factors. The purpose of this study is to investigate the effects of diverse factors on drivers’ guidance compliance behaviors under road condition information shown on graphic variable message sign (VMS), and based on this to find out a better information release mode. The involved data were obtained from questionnaire survey, and ordinal regression was used to analyze the casual relation between guidance compliance behavior and its influencing factors. Based on an overall analysis of conditions in driver’s route choice, an accurate method was proposed to calculate the compliance rate. The model testing information indicated that ordinal regression model with complementary log–log being the link function was appropriate to quantify the relation between the compliance rate and the factors. The estimation results showed that age, driving years, average annual mileage, monthly income, driving style, occupation, the degree of trust in VMS, the familiarity with road network and the route choice style were significant determinants of guidance compliance behavior. This paper also compared two different guidance modes which were ordinary guidance mode (M1) and predicted guidance mode (M2) through simulation. The average speed fluctuations and average travel time supported that M2 had better effect in improving traffic flow and balancing traffic load and resource. Some detailed suggestions of releasing guidance information were proposed with the explanation by flow-density curve and variation of traffic flows. These findings are the foundation to design and improve guidance systems by assessing guidance effect and modifying guidance algorithm.     

Evaluating the benefits of a combined route guidance and road pricing system in a traffic network with recurrent congestion

When drivers do not have complete information on road travel time and thus choose their routes in a stochastic manner or based on their previous experience, separate implementations of either route guidance or road pricing cannot drive a stochastic network flow pattern towards a system optimum in a Wardropian sense. It is thus of interest to consider a combined route guidance and road pricing system. A road guidance system could reduce drivers' uncertainty of travel time through provision of traffic information. A driver who is equipped with a guidance system could be assumed to receive complete information, and hence be able to find the minimum travel time routes in a user-optimal manner, while marginal-cost road pricing could drive a user-optimal flow pattern toward a system optimum. Therefore, a joint implementation of route guidance and road pricing in a network with recurrent congestion could drive a stochastic network flow pattern towards a system optimum, and thus achieve a higher reduction in system travel time. In this paper the interaction between route guidance and road pricing is modeled and the potential benefit of their joint implementation is evaluated based on a mixed equilibrium traffic assignment model. The private and system benefits under marginal-cost pricing and varied levels of market penetration of the information systems are investigated with a small and a large example. It is concluded that the two technologies complement each other and that their joint implementation can reduce travel time more efficiently in a network with recurrent congestion.     

A recursive, cell processing model for predicting freeway travel times

Travel time information influences driver behaviour and can contribute to reducing congestion and improving network efficiency. Consequently many road authorities disseminate travel time information on road side signs, web sites and radio traffic broadcasts. Operational systems commonly rely on speed data obtained from inductive loop detectors and estimate travel times using simple algorithms that are known to provide poor predictions particularly on either side of the peak period. This paper presents a new macroscopic model for predicting freeway travel times which overcomes the limitations of operational ‘instantaneous’ speed models by drawing on queuing theory to model the processing of vehicles in sections or cells of the freeway. The model draws on real-time speed, flow and occupancy data and is formulated to accommodate varying geometric conditions, the relative distribution of vehicles along the freeway, variations in speed limits, the impact of ramp flows and fixed or transient bottlenecks. Field validation of the new algorithm was undertaken using data from two operational freeways in Melbourne, Australia. Consistent with the results of simulation testing, the validation confirmed that the recursive model provided a substantial improvement in travel time predictions when compared to the model currently used to provide real-time travel time information to motorists in Melbourne.     

A model for assessing the effects of dynamic travel time information via variable message signs

In view of the serious traffic congestion during peak hours in most metropolitan areas around the world and recent improvement of information technology, there is a growing aspiration to alleviate road congestion by applications of electronic information and communication technology. Providing drivers with dynamic travel time information such as estimated journey times on major routes should help drivers to select better routes and guide them to utilise existing expressway network. This can be regarded as one possible strategy for effective traffic management. This paper aims to investigate the effects and benefits of providing dynamic travel time information to drivers via variable message signs at the expressway network. In order to assess the effects of the dynamic driver information system with making use of the variable message signs, a time-dependent traffic assignment model is proposed. A numerical example is used to illustrate the effects of the dynamic travel time information via variable message signs. This revised version was published online in June 2006 with corrections to the Cover Date.     

Development and implementation of the areawide Dynamic ROad traffic NoisE (DRONE) simulator

This paper discusses the areawide Dynamic ROad traffic NoisE (DRONE) simulator, and its implementation as a tool for noise abatement policy evaluation. DRONE involves integrating a road traffic noise estimation model with a traffic simulator to estimate road traffic noise in urban networks. An integrated traffic simulation-noise estimation model provides an interface for direct input of traffic flow properties from simulation model to noise estimation model that in turn estimates the noise on a spatial and temporal scale. The output from DRONE is linked with a geographical information system for visual representation of noise levels in the form of noise contour maps.     

Methods using belief functions to manage imperfect information concerning events on the road in VANETs

Different models using belief functions are proposed and compared in this article to share and manage imperfect information about events on the road in vehicular networks. In an environment without infrastructure, the goal is to provide to driver the synthesis of the situation on the road from all acquired information. Different strategies are considered: discount or reinforce towards the absence of the event to take into account messages agings, keep the original messages or only the fusion results in vehicles databases, consider the world update, manage the spatiality of traffic jams by taking into account neighborhood. Methods are tested and compared using a Matlab™ simulator. Two strategies are introduced to tackle fog blankets spatiality; they are compared through an example.