内蒙古交通环境监测信息处理平台的构建与应用

针对内蒙古自治区公路建设里程逐年快速增长的发展态势,建立与完善交通环境监测信息处理平台,是动态掌握区域内交通环境质量状况与变化趋势的重要途径。基于内蒙古自治区交通运输环境监测网络建设试点工程,通过集成在线监测设备、地理信息技术、数据统计与分析等技术,建成了内蒙古交通环境监测信息处理平台,从而为自治区交通环境监督管理和宏观决策提供了有力的基础数据和技术支撑。     

基于云计算的首都机场基础架构建设模式初探

随着民航业快速发展,首都机场业务不断扩展,机场在航班管理、旅客服务、安全管理等多方面的需求增多,机场间协同发展以及各相关单位的互联互通不断深入,对于信息服务能力要求不断提高,基于云计算概念和方法的信息系统架构建设,将引领机场信息服务能力有质的飞跃。本文分析了云计算的技术特点以及应用情况,结合首都机场信息系统的特点,提出了基于混合云的首都机场基础架构云平台的想法,基于机场业务特点和安全要求,提出云平台架构设计方案和功能特点,并探讨该平台的建设模式,该平台的建设对机场协同发展具有一定的指导意义。     

于德翔:实现充电网、车联网和互联网的融合

<正>青岛特锐德电气股份有限公司利用先进的箱式变电站技术,将云平台移至手机等智能终端,充电层集中于箱式变电站,剩下最底层的电气技术层开发为车挡式终端充电器,并通过低谷充电、电池柔性充电寿命管理系统和云平台终端技术全面解决汽车群充电问题,引领充电网、车联网、互联网三网融合的新能源互联网。有人认为,在过去的5~10年里,中国最好的产业是互联网平台下的电子商务,而在未来的5~10年里,中国最好玩、最有创造力的产业可能是在互联网平台下的新能源汽车。提到新能源汽车,很多人都会想到充电问题。     

交通移动应急通信指挥平台标准体系构建思路

日前,交通运输部已组织开展全国范围内的交通移动应急平台建设,遵循统一标准、统一规范、统一建设、统一运维的原则。为达到本标准体系的时效性和实用性,必须充分考虑交通行业建设移动应急通信指挥平台的实际需求和当前现状,并借鉴其他行业的建设经验。在此基础上,构建符合交通行业特色和社会发展、与其他行业应急平台和国家应急平台相协调的交通移动应急通信指挥平台标准体系。同时,该体系应兼顾日常使用和突发状况使用,具有可持续行和可操作性。1相关标准建设现状当前国家及各部门、各行业正在抓紧建设应急处置体     

广西交通运输视频云联网平台建设分析

文章介绍了广西交通运输视频云联网平台的架构、功能与技术工具,分析了该平台的特点与应用效益。     

基于熵值法-云理论的高铁客运站交通协调评价模型

文章针对高铁客运站交通协调评价因子的特殊性,提出了基于熵值法-云理论高铁客运站交通协调的综合评价方法,介绍了模型建立与应用的具体过程,并结合北京南站客运站进行了实例分析,论证了该模型的效用,为大型高铁客运枢纽站的交通协调性的综合评估研究提供参考。     

城市轨道交通机电云建设方案探讨

云计算技术在我国城市轨道交通领域的应用不断深化,继部分城轨企业建成企业管理领域的私有云后,不少企业已开始探索建设城轨机电云。由于缺乏统一的指引和规范,各地机电云的建设在技术路线和产品选型等方面各不相同。如何因地制宜建设符合企业自身实际的机电云平台,真正实现集约、高效、灵活、可靠的建设目标,促进企业技术进步,是值得城轨行业共同思考和探索的课题。本文提出了城轨机电云三种不同的建设方案,并对它们进行了分析对比,供城轨企业参考借鉴。     

大数据技术在智能交通领域的应用

在新时代背景下,以大数据理念为依据的智能化交通建设成为优化城镇化建设水平,解决城市交通拥堵的有效举措,其不仅能提升获取交通信息的科学性和准确性,而且可以构建多元化的管理系统为智能交通运行提供基础保障。文章以广西为例,基于大数据与智能交通理论概念,介绍了基于大数据技术的处理平台特点与架构,探讨了智能交通领域大数据技术的具体应用。     

基于云平台的移动智能桥隧涵养护巡查系统

文章针对传统桥隧涵养护巡查工作效率低、信息滞后、易出错等问题,提出了基于云平台的移动智能桥隧涵养护巡查系统,提高日常巡查和经常检查的效率,加强相关人员在桥隧涵检查工作中的过程监督、工作考核,实现桥隧涵养护巡查的数字化、标准化、流程化、实时化。     

基于BIM的桥梁施工监控云平台应用研究

文章以BIM、云平台、物联网等先进技术为基础,建立桥梁施工监控云平台,解决了制定桥梁BIM实施标准和工程分解结构编码、基于TEKLA的桥梁快速建模、桥梁模型碰撞检测和轻量化处理、桥梁施工进度控制可视化等关键技术应用等问题,实现了桥梁施工进度控制可视化以及以物联网为基础的桥梁健康监测。     

Integrating mobile agent technology with multi-agent systems for distributed traffic detection and management systems

Agent technology is rapidly emerging as a powerful computing paradigm to cope with the complexity in dynamic distributed systems, such as traffic control and management systems. However, while a number of agent-based traffic control and management systems have been proposed and the multi-agent systems have been studied, to the best of our knowledge, the mobile agent technology has not been applied to this field. In this paper, we propose to integrate mobile agent technology with multi-agent systems to enhance the ability of the traffic management systems to deal with the uncertainty in a dynamic environment. In particular, we have developed an IEEE FIPA compliant mobile agent system called Mobile-C and designed an agent-based real-time traffic detection and management system (ABRTTDMS). The system based on Mobile-C takes advantages of both stationary agents and mobile agents. The use of mobile agents allows ABRTTDMS dynamically deploying new control algorithms and operations to respond unforeseen events and conditions. Mobility also reduces incident response time and data transmission over the network. The simulation of using mobile agents for dynamic algorithm and operation deployment demonstrates that mobile agent approach offers great flexibility in managing dynamics in complex systems.     

Special issue on intelligent transportation systems,big data and intelligent technology

ABSTRACT

Based on the increasing demands of transportation development, the concept of an Intelligent Transportation System (ITS) has received increasing attention in both academic and industry arenas. It integrates information, communications, computers and other technologies, and applies them in the field of transportation to build an integrated system of people, roads and vehicles by utilizing advanced data communication technologies. It can establish a large, fully functioning, real-time, accurate and efficient transportation management system. Intelligent transportation systems shift the focus from road managers to road users. In order to achieve this purpose, intelligent transportation systems use advanced technology to provide drivers with convenient information to help reduce traffic congestion and to increase available road capacity. This special issue is dedicated to exploring the most recent advances in intelligent transportation systems and big data based on intelligent technology.     

Simple analytical models for estimating the queue lengths from probe vehicles at traffic signals

As mobile traffic sensor technology gets more attention, mathematical models are being developed that utilize this new data type in various intelligent transportation systems applications. This study introduces simple analytical estimation models for queue lengths from tracked or probe vehicles at traffic signals using stochastic modeling approach. Developed models estimate cycle-to-cycle queue lengths by using primary parameters such as arrival rate, probe vehicle proportions, and signal phase durations. Valuable probability distributions and moment generating functions for probe information types are formulated. Fully analytical closed-form expressions are given for the case ignoring the overflow queue and approximation models are presented for the overflow case. Derived models are compared with the results from VISSIM-microscopic simulation. Analytical steady-state and cycle-to-cycle estimation errors are also derived. Numerical examples are shown for the errors of these estimators that change with probe vehicle market penetration levels, arrival rates, and volume-to-capacity ratios.     

Fuel economy improvements for urban driving: Hybrid vs. intelligent vehicles

The quest for more fuel-efficient vehicles is being driven by the increasing price of oil. Hybrid electric powertrains have established a presence in the marketplace primarily based on the promise of fuel savings through the use of an electric motor in place of the internal combustion engine during different stages of driving. However, these fuel savings associated with hybrid vehicle operation come at the tradeoff of a significantly increased initial vehicle cost due to the increased complexity of the powertrain. On the other hand, telematics-enabled vehicles may use a relatively cheap sensor network to develop information about the traffic environment in which they are operating, and subsequently adjust their drive cycle to improve fuel economy based on this information – thereby representing ‘intelligent’ use of existing powertrain technology to reduce fuel consumption. In this paper, hybrid and intelligent technologies using different amounts of traffic flow information are compared in terms of fuel economy over common urban drive cycles. In order to develop a fair comparison between the technologies, an optimal (for urban driving) hybrid vehicle that matches the performance characteristics of the baseline intelligent vehicle is used. The fuel economy of the optimal hybrid is found to have an average of 20% improvement relative to the baseline vehicle across three different urban drive cycles. Feedforward information about traffic flow supplied by telematics capability is then used to develop alternative driving cycles firstly under the assumption there are no constraints on the intelligent vehicle’s path, and then taking into account in the presence of ‘un-intelligent’ vehicles on the road. It is observed that with telematic capability, the fuel economy improvements equal that achievable with a hybrid configuration with as little as 7 s traffic look-ahead capability, and can be as great as 33% improvement relative to the un-intelligent baseline drivetrain. As a final investigation, the two technologies are combined and the potential for using feedforward information from a sensor network with a hybrid drivetrain is discussed.     

弯道诱发道路交通安全事故分析

道路交通安全关乎我们的切身利益,其诱发因素颇多,而弯道是交通安全事故高发地。文章从汽车行驶特性、不良视距、会车超车等方面入手,分析弯道诱因与交通安全事故间的内在关系,并提出了基于GPS、GIS信息系统工程的智能交通预警思路,为交通安全控制提供理论参考依据。     

Connected vehicle penetration rate for estimation of arterial measures of effectiveness

The Connected Vehicle (CV) technology is a mobile platform that enables a new dimension of data exchange among vehicles and between vehicles and infrastructure. This data source could improve the estimation of Measures of Effectiveness (MOEs) for traffic operations in real-time, allowing to perfectly monitor traffic states after being fully adopted. However, as with any novel technology, the CV adoption will be a gradual process. This research focuses on determining minimum CV technology penetration rates that would guarantee accurate MOE estimates on signalized arterials. First, we present estimation methods for various MOEs such as average speed, number of stops, acceleration noise, and delay, followed by an initial assessment of the penetration rates required to accurately estimate them in undersaturated and oversaturated conditions. Next, we propose a methodology to determine the minimum CV market penetration rates to guarantee accurate MOE estimates as a function of traffic conditions, signal settings, sampling duration, and the MOE variability. A correction factor is also provided to account for small vehicle populations where sampling is done without replacement. The methodology is tested in a simulated segment of the San Pablo Avenue arterial in Berkeley, CA. The outcomes show that the minimum penetration rate required can be estimated within 1% for most MOEs under a wide range of traffic conditions. The proposed methodology can be used to determine if MOE estimates obtained with a portion of CV equipped vehicles can yield accurate enough results. The methodology could also be used to develop and assess control strategies towards improved arterial traffic operations.     

Multiagent architectures for intelligent traffic management systems

This paper reports our experiences with agent-based architectures for intelligent traffic management systems. We describe and compare integrated TRYS and TRYS autonomous agents, two multiagent systems that perform decision support for real-time traffic management in the urban motorway network around Barcelona. Both systems draw upon traffic management agents that use similar knowledge-based reasoning techniques in order to deal with local traffic problems. Still, the former achieves agent coordination based on a traditional centralized mechanism, while in the latter coordination emerges upon the lateral interaction of autonomous traffic management agents. We evaluate the potentials and drawbacks of both multiagent architectures for the domain, and develop some conclusions respecting the general applicability of multiagent architectures for intelligent traffic management.     

Development of origin–destination matrices using mobile phone call data

In this research, we propose a methodology to develop OD matrices using mobile phone Call Detail Records (CDR) and limited traffic counts. CDR, which consist of time stamped tower locations with caller IDs, are analyzed first and trips occurring within certain time windows are used to generate tower-to-tower transient OD matrices for different time periods. These are then associated with corresponding nodes of the traffic network and converted to node-to-node transient OD matrices. The actual OD matrices are derived by scaling up these node-to-node transient OD matrices. An optimization based approach, in conjunction with a microscopic traffic simulation platform, is used to determine the scaling factors that result best matches with the observed traffic counts. The methodology is demonstrated using CDR from 2.87 million users of Dhaka, Bangladesh over a month and traffic counts from 13 key locations over 3 days of that month. The applicability of the methodology is supported by a validation study.     

城市轨道交通中个人导向方法研究

文章运用层次分析的方法来阐述个人导向功能的实现原理,提出了利用现阶段飞速发展的移动通信体技术,设计一种应用在城市轨道交通中具有个人导向功能的移动通信体,以改善城市轨道交通中个人导向功能薄弱的现状。     

基于LVQ网络的城市交通安全态势评估模型研究

文章基于城市交通安全态势评估的重要性,提出了一个比较完善的城市道路网交通安全态势评估系统总体框架,构建了基于LVQ网络的宏观城市交通安全态势评估模型,并采用Matlab7.0语言对该模型进行了仿真分析。仿真结果表明该模型能够对城市宏观交通的安全态势进行评价。