智慧公交为疫情防控时期的公共出行保驾护航

本文以新冠肺炎疫情防控为例,将国内疫情及其防控分为疫情突发时期,遏制疫情流行时期,防疫精准化、动态化、常态化时期三个阶段,并通过回顾性研究方法评述重大突发公共卫生事件影响下城市地面公共交通的信息化防控策略,分析公交防疫措施在智慧感知层、交换共享层、应用服务层存在的不足,提出了基于多元融合感知技术、加速自动驾驶场景应用、创新数据共享融通机制,持续深入推广大数据示范应用以及优化定制公交线路,丰富突发公共事件下交通行业应急策略体系的建议。     

舰载作战指挥系统软件构建技术探讨

针对舰载作战指挥系统软件构建技术，重点分析了软件构件，Web技术，给出多客户机／多服务器加浏览器模式的多层次可以配置的分布式系统体系结构、软件体系结构，分析软件构件化设计原则，并给出一个构件化的指控系统结构简图。     

Measuring the impact of urban policies on transportation energy saving using a land use-transport model

Various projects all over the world are attempting to build smart cities in hopes of achieving energy-efficient and livable communities, but most of them are aiming to fulfill their goals technologically. However, the energy efficiency and livability of a city are affected by not only these technological factors but also urban structures that encompass residential areas, offices, transportation networks, and other facilities. Urban policies intervene in transportation and land-use conditions and thereby change how citizens consume energy and go about their daily lives as the actors in the urban system alter their behavior. This means that energy efficiency and quality of life share close ties. Assessments of urban policies thus need to consider the reactions of actors to the intervention.     

用Java实现OPC客户端

提出利用Java语言、J-Interop和Utgard开源工程来实现OPC(OLE for Process Control)客户端,同时解决了J-Interop本身不能连接不支持IID_IRemUnknown2接口OPC服务器的问题。用Java语言实现的OPC客户端能够在Linux和Windows下连接Windows下的OPC服务器。为在跨平台实现OPC应用提供了一种方法。     

基于.Net平台和Smart Client的高速公路公司信息管理系统

描述了一个基于Microsoft.Net平台和Smart Client架构的高速公路公司信息管理系统,用Web Service解决已有系统的数据层集成和业务逻辑的封装,同时,通过Smart Client为用户提供一个部署方便、管理集中,响应速度快、界面丰富和能脱网使用的应用程序,使整个系统更加实用和可靠.     

进化中的ITS，新一代道路（SmartWay）服务出台

日本对以交通安全为首的交通堵塞缓解,以及相关联的二氧化碳排量的抑制和削减等问题．一直在寻求解决方法。除了以往新建道路的方法外,还致力于最大限度有效利用现有道路,通过运用最尖端的信息通讯技术（ICT）解决道路交通的课题。日本国土交通省国土技术政策综合研究所（国综研）ITS研究室的室长岛中秀人先生对国综研运用ITS（Intelligent Transport Systems;     

A Real-Time Parking Prediction System for Smart Cities

A methodological framework for multiple steps ahead parking availability prediction is presented. Two different types of predictions are provided: the probability of a free space to continue being free in subsequent time intervals, and the short-term parking occupancy prediction in selected regions of an urban road network. The available data come from a wide network of on-street parking sensors in the “smart” city of Santander, Spain. The sensor network is segmented in four different regions, and then survival and neural network models are developed for each region separately. Findings show that the Weibull parametric models best describe the probability of a parking space to continue to be free in the forthcoming time intervals. Moreover, simple genetically optimized multilayer perceptrons accurately predict region parking occupancy rates up to 30 minutes in the future by exploiting 1-minute data. Finally, the real time, Web-based, implementation of the proposed parking prediction availability system is presented.     

基于移动协同的智能客户端研究

支持移动的智能客户端的研究是移动协同研究领域的又一新课题,本文描述了移动环境下的网络自适应、协作数据传输、移动终端的人机界面、异步协作整合等关键技术,探讨了.NET智能客户端技术的特点及其优越性.在此基础上,提出了基于移动CSCW的智能客户端的系统应用架构,并分析了这种架构的特点以及实现.     

Smart charging management for shared autonomous electric vehicle fleets: A Puget Sound case study

Increasingly, experts are forecasting the future of transportation to be shared, autonomous and electric. As shared autonomous electric vehicle (SAEV) fleets roll out to the market, the electricity consumed by the fleet will have significant impacts on energy demand and, in turn, drive variation in energy cost and reliability, especially if the charging is unmanaged. This research proposes a smart charging (SC) framework to identify benefits of active SAEV charging management that strategically shifts electricity demand away from high-priced peak hours or towards renewable generation periods. Time of use (TOU), real time pricing (RTP), and solar generation electricity scenarios are tested using an agent-based simulation to study (1) the impact of battery capacity and charging infrastructure type on SAEV fleet performance and operational costs under SC management; (2) the cost reduction potential of SC considering energy price fluctuation, uncertainty, and seasonal variation; (3) the charging infrastructure requirements; and (4) the system efficiency of powering SAEVs with solar generation. A case study from the Puget Sound region demonstrates the proposed SC algorithm using trip patterns from the regional travel demand model and local energy prices. Results suggest that in the absence of electricity price signals, SAEV charging demand is likely to peak the evening, when regional electricity use patterns already indicate high demand. Under SC management, EVs with larger battery sizes are more responsive to low-electricity cost charging opportunities, and have greater potential to reduce total energy related costs (electricity plus charging infrastructure) for a SAEV fleet, especially under RTP structure.     

Can regional transportation and land-use planning achieve deep reductions in GHG emissions from vehicles?

The Intergovernmental Panel on Climate Change estimates that greenhouse gas emissions (GHG) must be cut 40–70% by 2050 to prevent a greater than 2 °Celsius increase in the global mean temperature; a threshold that may avoid the most severe climate change impacts. Transportation accounts for about one third of GHG emissions in the United States; reducing these emissions should therefore be an important part of any strategy aimed at meeting the IPCC targets. Prior studies find that improvements in vehicle energy efficiency or decarbonization of the transportation fuel supply would be required for the transportation sector to achieve the IPCC targets. Strategies that could be implemented by regional transportation planning organizations are generally found to have only a modest GHG reduction potential. In this study we challenge these findings. We evaluate what it would take to achieve deep GHG emission reductions from transportation without advances in vehicle energy efficiency and fuel decarbonization beyond what is currently expected under existing regulations and market expectations. We find, based on modeling conducted in the Albuquerque, New Mexico metropolitan area that it is possible to achieve deep reductions that may be able to achieve the IPCC targets. Achieving deep reductions requires changes in transportation policy and land-use planning that go far beyond what is currently planned in Albuquerque and likely anywhere else in the United States.