高速公路交通事件清除时间模糊逻辑预测模型

清除时间是高速公路交通事件持续时间中的一部分,与事件影响范围密切相关.通过对某高速公路事件数据及交警执勤记录进行分析,提出了救援车辆的清除时间新定义,该定义与事件持续时间有更强的相关性.随后采用含自适应算法的TSK模糊逻辑推理方法,通过对基于事件数据的人工神经网络训练,建立了交通事件清除时间的预测模型,并讨论了模型的适...     

Reprint of: Modelling the impact of traffic incidents on travel time reliability

Traffic incidents are recognised as one of the key sources of non-recurrent congestion that often leads to reduction in travel time reliability (TTR), a key metric of roadway performance. A method is proposed here to quantify the impacts of traffic incidents on TTR on freeways. The method uses historical data to establish recurrent speed profiles and identifies non-recurrent congestion based on their negative impacts on speeds. The locations and times of incidents are used to identify incidents among non-recurrent congestion events. Buffer time is employed to measure TTR. Extra buffer time is defined as the extra delay caused by traffic incidents. This reliability measure indicates how much extra travel time is required by travellers to arrive at their destination on time with 95% certainty in the case of an incident, over and above the travel time that would have been required under recurrent conditions. An extra buffer time index (EBTI) is defined as the ratio of extra buffer time to recurrent travel time, with zero being the best case (no delay). A Tobit model is used to identify and quantify factors that affect EBTI using a selected freeway segment in the Southeast Queensland, Australia network. Both fixed and random parameter Tobit specifications are tested. The estimation results reveal that models with random parameters offer a superior statistical fit for all types of incidents, suggesting the presence of unobserved heterogeneity across segments. What factors influence EBTI depends on the type of incident. In addition, changes in TTR as a result of traffic incidents are related to the characteristics of the incidents (multiple vehicles involved, incident duration, major incidents, etc.) and traffic characteristics.     

广州地铁乘务安全管理及控制对策

围绕地铁运营的核心环节--乘务运作的安全管理,通过对广州地铁历年在乘务方面发生的事故、事件进行归纳分析,从时间、地点、岗位、环节4个方面总结出地铁乘务安全管理控制的关键点.从案例教育、业务培训、实作演练3个方面,探讨和阐述地铁乘务安全管理的内容和措施.     

交通事故下出行者非理性出行行为研究

基于前景理论,研究交通事故下出行者路径选择行为规律.应用前景理论,考虑交通事故的发生概率,以出行者出行总花费时间作为出行效用,提出出行者路径选择的效用函数;然后通过分析出行者效用函数的分布规律,在出行效用连续随机分布的条件下,建立出行者感知效用模型.通过连续函数离散化方法构建出行者路径选择模型.通过一个算例,描述非理性条件下出行者选择行为,并分析交通事故的特征对效用值和选择结果的影响,说明感知效用为0 的参考点并不是路径实际效用分布的期望值,而是比期望值小.     

南京地铁突发事件应急处置机制建设

介绍南京地铁1号线突发事件应急处置体系的建设,重点是组织机构的设置、应急救援队伍的建设、应急救援设备的配备、应急预案的规范管理等。结合在运营过程中的实际应用,对应急处置体系及时修改完善,为建立科学的城市轨道交通突发事件应急处置体系、提升应急处置能力提供参考。     

建立有毒有害物质污染损害赔偿机制 做好履约准备

随着国际贸易运输中化工产品特别是有毒有害物质的日益增多，污染问题已为世界各国所关注，《国际海上运输有毒有害物质损害责任及赔偿公约》随之出台。文中旨在通过分析我国有毒有害物质污染损害赔偿方面一些亟待解决的问题，探讨建立机制的方法和对策，以期做好加入并履行公约的；住备     

试论高校突发群体事件的性质及主要特征

高校突发群体事件是在高校这一特殊的社会生态环境下,由高校师生这一特殊的社会群体为参与主体的,有别于社会一般的突发性群体事件.对于处在社会转型时期的中国来说,当前发生的高校群体突发事件,其基本性质为人民内部矛盾而不是敌我矛盾.这也是我们在处理高校群体突发事件时必须把握一个基本原则.同时,我们在认识和处理这类问题时还必须科学认识其四个主要特征：参与主体的特殊性、矛盾的集聚性、性质的可转变性和影响的社会性.     

Simulator evaluation of incident detection by transponder-equipped vehicles

This paper explores the efficacy of the driver-based incident detection using the vehicle-to-roadside communication (VRC) system. The proliferation of vehicle tags in the US for automatic toll collection, traffic monitoring, and vehicle navigation and information systems has created an infrastructure capable of supporting a driver-based incident detection system. The research reported herein investigated the use of "activatable" vehicle tags by drivers to send an incident signal to the Traffic Management Center through VRC reader stations spaced uniformly on a highway. The simulation results showed that good detection performance was achieved even at lower levels of market penetration of vehicle tags. The results further showed that detection performance is significantly affected by the severity of the incident in terms of number of lanes closed, the spacing of the VRC reader stations, traffic volume at the time of the incident, and the reporting propensity of the traveling public.The performance of the VRC-based incident reporting system was compared to the performance of two incident detection algorithms that rely on traffic data collected through the automatic vehicle identification (AVI) system. The comparison showed that the VRC-based incident reporting system attained shorter detection times and higher detection rates under fairly similar simulated conditions. The paper also discusses issues that need further study through simulation and field experimentation of the VRC-based incident reporting system.     

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提出一种新颖的基于boosting模糊分类的交通事件检测方法。该方法利用Boosting方法和遗传算法以迭代形式获取一组模糊规则及规则对应的权值，分类器以加权投票方式进行分类决策。运用Matlab 进行了仿真分析，结果表明提出的交通事件检测算法利用较少样本数据即可快速实现交通事件检测。