经营性道路运输驾驶员安全管理怎么管?

正要确保经营性道路运输安全,驾驶员的安全管理工作不能忽视:要严控道路运输驾驶员从业资格考试准入机制,加强道路运输驾驶员从业资格证管理,提升道路运输驾驶员日常管理水平。道路运输企业必须采取行之有效的科学手段,制定切实可行措施,只有把交通事故消灭在萌芽状态,才能确保道路运输安全。而驾驶员作为道路运输的主体,他们的整体素     

我国应建立驾驶适应性检测制度

<正> 机动车驾驶员的驾驶适应性是指人们从不是驾驶员到成为驾驶员的过程中,或随驾驶年限增长的过程中客观条件的改变对驾驶员个体克服影响安全驾驶因素的适应能力所表现的某些倾向性特征。由于驾驶适应性的存在,驾驶员所具有的心理和生理条件千差万别,有些个性就具备了自然的、生物学的先决条件,因此使得驾驶员即便是在相同条件的教育和训练下,车祸往往集中于某一类驾驶员身上而多次发生交通肇事:相反,另有一些驾驶员则很少肇事或从不肇事。     

运输企业安全管理忧思

科学评价机制尚缺 国有运输企业现有从业人员大专以上或中级职称的比例相对匮乏,尤其是安全管理人员,大部分是驾驶员出身,凭经验管理者多,不能完全适应道路运输安全系统化、科学化管理新要求.     

城市轨道交通全自动运行线路的乘务管理创新研究

为保证城市轨道全自动化运营效果,明确全自动运行系统的优点,对列车驾驶员岗位职能转变后的管理,以及列车驾驶员正线复训管理等进行探讨,以期保证列车在UTO模式下的行车安全、管理有效,有序、有效地提升列车驾驶员的生产力,节省企业运营费用,以供参考。     

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？     

客车驾驶员安全信用考核及评价标准研究

叙述了建立客车驾驶员安全信用考核的意义,分析了现有的客车驾驶员考核与管理方式中存在的不足与缺点,对构建客车驾驶员信用评价考核标准进行了研究。     

抓好驾驶员培训和管理方能成就安全

很多事故都充分说明一点,无论车辆多么高档,无论设备多么现代,人的因素始终是安全生产的决定因素。目前,我们道路运输企业的驾驶员状况较为复杂—一是驾驶员来源渠道不一、成分复杂。现在运输企业的车辆有集约管理和承包经营多种形式,但90%以上的运输企业没有自己的驾驶培训机构,企业无法对上岗的驾驶员有一个系统的培训和管理过程,一线驾驶员的来源基     

驾驶员队伍建设之我见

<正> 驾驶员是运输企业的主力军,驾驶队伍素质的好坏,关系到社会政治影响,国家人民生命财产的安全,企业的声誉和经济效益。因此,汽车运输企业如何狠抓安全的源头管理,抓好驾驶员队伍建设是个值得探索的问题。对此,我们认为应抓好以下几点: 一、深化教育,提高安全认识 1、讲究教育质量。目前,各基层车队安全活动周周搞,安全喊话天天有,安全标牌处处见,形式虽有,而效果欠佳,少部分驾驶员安全思想扎根不牢,事故隐患时有发生,可见教     

山区公路营运客车安全技术研究

《山区公路营运客车安全技术研究》项目基于山区公路营运客车安全技术的研究现状，研究营运客车发生相关交通事故的规律、客运企业安全评价体系、客车驾驶员行为特性、营运客车安全通行适应性、营运客车GPS监管体系的应用技术以及相关营运客车在山区公路安全运行的相关保障措施等。本篇介绍了该项目研究的现状与内容，总结了项目研究获得的成果与创新点。     

虎跃快客三级管理把牢安全关

辽宁虎跃快速汽车客运股份有限公司(简称"虎跃")自1998年成立以来,从管理体系建设,驾驶员管理和技术管理等层面,不断探索、提高安全预防管理能力.努力为旅客提供最安全的出行服务.     

Adaptive cruise control design for active congestion avoidance

We present an adaptive cruise control (ACC) strategy where the acceleration characteristics, that is, the driving style automatically adapts to different traffic situations. The three components of the concept are the ACC itself, implemented in the form of a car-following model, an algorithm for the automatic real-time detection of the traffic situation based on local information, and a strategy matrix to adapt the driving characteristics (that is, the parameters of the ACC controller) to the traffic conditions. Optionally, inter-vehicle and infrastructure-to-car communication can be used to improve the accuracy of determining the traffic states. Within a microscopic simulation framework, we have simulated the complete concept on a road section with an on-ramp bottleneck, using empirical loop-detector data for an afternoon rush-hour as input for the upstream boundary. We found that the ACC vehicles improve the traffic stability and the dynamic road capacity. While traffic congestion in the reference scenario was completely eliminated when simulating a proportion of 25% ACC vehicles, travel times were already significantly reduced for much lower penetration rates. The efficiency of the proposed driving strategy even for low market penetrations is a promising result for a successful application in future driver assistance systems.     

Adaptive traffic signal control using approximate dynamic programming

This paper presents a study on an adaptive traffic signal controller for real-time operation. The controller aims for three operational objectives: dynamic allocation of green time, automatic adjustment to control parameters, and fast revision of signal plans. The control algorithm is built on approximate dynamic programming (ADP). This approach substantially reduces computational burden by using an approximation to the value function of the dynamic programming and reinforcement learning to update the approximation. We investigate temporal-difference learning and perturbation learning as specific learning techniques for the ADP approach. We find in computer simulation that the ADP controllers achieve substantial reduction in vehicle delays in comparison with optimised fixed-time plans. Our results show that substantial benefits can be gained by increasing the frequency at which the signal plans are revised, which can be achieved conveniently using the ADP approach.     

Adaptive traffic signal control with equilibrium constraints under stochastic demand

This study develops a methodology to model transportation network design with signal settings in the presence of demand uncertainty. It is assumed that the total travel demand consists of commuters and infrequent travellers. The commuter travel demand is deterministic, whereas the demand of infrequent travellers is stochastic. Variations in demand contribute to travel time uncertainty and affect commuters’ route choice behaviour. In this paper, we first introduce an equilibrium flow model that takes account of uncertain demand. A two-stage stochastic program is then proposed to formulate the network signal design under demand uncertainty. The optimal control policy derived under the two-stage stochastic program is able to (1) optimize the steady-state network performance in the long run, and (2) respond to short-term demand variations. In the first stage, a base signal control plan with a buffer against variability is introduced to control the equilibrium flow pattern and the resulting steady-state performance. In the second stage, after realizations of the random demand, recourse decisions of adaptive signal settings are determined to address the occasional demand overflows, so as to avoid transient congestion. The overall objective is to minimize the expected total travel time. To solve the two-stage stochastic program, a concept of service reliability associated with the control buffer is introduced. A reliability-based gradient projection algorithm is then developed. Numerical examples are performed to illustrate the properties of the proposed control method as well as its capability of optimizing steady-state performance while adaptively responding to changing traffic flows. Comparison results show that the proposed method exhibits advantages over the traditional mean-value approach in improving network expected total travel times.     

Adaptive control algorithm to provide bus priority with a pre-signal

In urban areas, where road space is limited, it is important to provide efficient public and private transportation systems to maximize person throughput, for example from a signalized intersection. To this end, this research looks at providing bus priority using a dedicated bus lane which is terminated upstream of the intersection, and placing an additional signal at this location, called a pre-signal. Although pre-signals are already implemented in some countries (e.g. UK, Denmark, and Switzerland), an adaptive control algorithm which responds to varying traffic demands has not yet been proposed and analyzed in the literature. This research aims to fill that gap by developing an adaptive control algorithm for pre-signals tailored to real-time private and public transportation demands. The necessary infrastructure to operate an adaptive pre-signal is established, and guidelines for implementation are provided. The relevant parameters regarding the boundary conditions for the adaptive algorithm are first determined, and then quantified for a typical case using a micro-simulation model. It is demonstrated with case studies that, under all considered scenarios, implementing a pre-signal with the proposed adaptive control algorithm will result in the least average person delay at the intersection. The algorithm is expected to function well with a wide range of car demands, bus frequencies, and bus passenger occupancies. Moreover, the algorithm is robust to errors in these input values, so exact information is not required.     

Development and testing of a fully Adaptive Cruise Control system

Adaptive Cruise Control systems have been developed and introduced into the consumer market for over a decade. Among these systems, fully-adaptive ones are required to adapt their behaviour not only to traffic conditions but also to drivers’ preferences and attitudes, as well as to the way such preferences change for the same driver in different driving sessions. This would ideally lead towards a system where an on-board electronic control unit can be asked by the driver to calibrate its own parameters while he/she manually drives for a few minutes (learning mode). After calibration, the control unit switches to the running mode where the learned driving style is applied. The learning mode can be activated by any driver of the car, for any driving session and each time he/she wishes to change the current driving behaviour of the cruise control system.The modelling framework which we propose to implement comprises four layers (sampler, profiler, tutor, performer). The sampler is responsible for human likeness and can be calibrated while in learning mode. Then, while in running mode, it works together with the other modelling layers to implement the logic. This paper presents the overall framework, with particular emphasis on the sampler and the profiler that are explained in full mathematical detail. Specification and calibration of the proposed framework are supported by the observed data, collected by means of an instrumented vehicle. The data are also used to assess the proposed framework, confirming human-like and fully-adaptive characteristics.     

Adaptive data dissemination for time-constrained messages in dynamic vehicular networks

With the advent of emerging wireless communication technologies, tremendous efforts have been put on promoting the safety and efficiency of transportation services by developing innovative applications. In particular, there has been significant interest in accessing information stored at RSUs (Roadside Units). The unique characteristics in vehicular networks, such as dynamic traffic factors including vehicle arrival rate, dwell time and data access patterns, bring us new challenges on data dissemination. This work dedicates to the investigation of timely and adaptive data dissemination in the dynamically changing traffic environment. Firstly, we derive an analytical model to explore and examine the effects of the dynamic traffic factors. In light of the theoretical results, an on-line scheduling algorithm is proposed for adaptive data dissemination. Finally, we evaluate performance of the new algorithm in a variety of circumstances. The simulation results demonstrate satisfactory performance of the proposed algorithm.     

基于自适应滤波技术的管道泄漏检测方法

提出了一种基于小波变换和Kalman滤波相结合的自适应滤波方法,采用递推算法逐步修正滤波过程,即根据采样得到的压力信号实时地对滤波参数进行修正,使自适应滤波器能够有效地跟踪信号的变化,形成自适应降噪过程,通过自动寻优,选择最佳尺度,使之能在背景噪声较强或变化的情况下也能对管道压力信号进行有效的滤波处理,为准确检测管道泄漏和定位提供了良好基础.应用结果证明:该方法大大提高了信号处理的效率,在管道泄漏在线监测应用中效果较好.     

基于自适应神经模糊推理的交通状态判别方法

文章针对交通状态具有模糊性和主观性的特点,建立能够真实反映人对交通拥塞程度感觉的自适应-神经模糊推理系统,使具有变化连续的交通流参数模糊化处理,实现了道路交通状态的准确、快速辨别。     

Adaptive choice behaviour of motorists in congested shopping centre parking lots

Currently existing models of parking choice behaviour typically focus on the choice of types of parking spaces. Implicitly these models assume that motorists have a free choice in that spaces are available. The adaptive behaviour which they reveal when faced with congested parking spaces is not explicitly modelled. The aim of this paper is to contribute to the growing literature on parking choice modelling by developing and testing a stated choice model of adaptive behaviour of motorists who are faced with fully occupied parking lots. The findings of the analyses indicate that the model performs satisfactory as indicated by its goodness-of-fit and the fact that all significant parameters were in anticipated directions.     

Adaptive scheduling for real-time and temporal information services in vehicular networks

Vehicular networks represent a research area of significant importance in improving the safety, efficiency and sustainability of transportation systems. One of the key research problems in vehicular networks is real-time data dissemination, which is crucial to the satisfactory performance of many emergent applications providing real-time information services in vehicular networks. Specifically, the two issues need to be addressed in this problem are maintenance of temporal data freshness and timely dissemination of data. Most existing works only considered periodical data update via backbone wired networks in maintaining temporal data freshness. However, many applications rely on passing vehicles to upload their collected information via wireless network, which imposes new challenges as the uplink data update will have to compete with the downlink data dissemination for the limited wireless bandwidth. With such observations, we propose a temporal information service system, in which vehicles are able to collect up-to-date temporal information and upload them to the roadside units (RSU) along their trajectories. Meanwhile, RSU can disseminate its available data items to vehicles based on their specific requests. Particularly, in this paper, we first quantitatively analyze the freshness of temporal data and propose a mathematical model to evaluate the usefulness of the temporal data. Next, we give the formulation of the proposed real-time and temporal information service (RTIS) problem, and prove the NP-hardness of this problem by constructing a polynomial-time reduction from 0–1 knapsack problem. Subsequently, we establish a probabilistic model to theoretically analyze the tradeoff between timely temporal data update and requested data dissemination sharing a common communication resource, which provides a deeper insight of the proposed RTIS. Further, a heuristic algorithm, namely adaptive update request scheduling (AURS), is designed to enhance the efficacy of RTIS by synthesizing the broadcast effect, the real-time service requirement and the service quality in making scheduling decisions. The computational complexity and scalability analysis of AURS is also discussed. Last but not least, a simulation model is implemented and a comprehensive performance evaluation has been carried out to demonstrate the superiority of ARUS against several state-of-the-art approaches in a variety of application scenarios.