基于智慧交通的可达性与交通出行碳排放——理论与实证

信息与通信技术的发展推动了智慧交通建设,并将进一步提高城市交通可达性和影响居民出行。在全球应对气候变化、减少碳排放的共识背景下,由于"智慧交通—可达性—出行行为(碳排放)"间复杂的影响作用机制,已有的研究难以判断智慧化背景下的城市交通可达性提高将增加抑或减少出行碳排放。如何通过提高城市交通可达性来缓解交通拥堵,保障城市交通运输系统的有效运行,并提高居民出行效率,减少出行碳排放是当前中国智慧交通发展面临的关键问题之一。针对以上科学问题,本文尝试提出基于智慧交通的可达性与交通出行碳排放的理论框架,并以广州为研究案例地,研究了社区居民通勤碳排放特征及其影响机理,社区出行低碳指数格局及其影响因素的空间异质性,以及基于碳排放—位置分配模型的公共中心规划支持系统设计与应用,可为今后的相关研究提供借鉴。     

基于游客视角的智慧景区评价体系及实证分析——以南京夫子庙秦淮风光带为例

建设智慧景区,对于景区实现精细化、低碳化、移动化的管理方向,并最终实现"智慧旅游"的发展战略具有重要意义。目前对于智慧景区的探讨多集中于技术、管理和服务层面,尚无文献从游客视角对智慧景区评价指标体系进行研究。本文运用因子分析方法,构建了基于游客的智慧景区评价指标体系,提出应该从景区智能管理系统、信息服务智能系统、智慧游览系统、智慧预报系统、旅游电子支付、景区综合智能系统、景区安全救助智能系统、景区智能交通系统和景区资源保护智能系统9个方面进行智慧景区的建设。在此基础上,运用模糊综合评价方法,以南京夫子庙秦淮风光带为例,进行了智慧景区的实证评价。最后,对夫子庙秦淮风光带智慧景区建设的优势、不足以及对策进行了分析。     

中央气象台决策气象服务智能移动终端的设计与实现

中央气象台决策气象服务智能移动终端(简称为“决策服务App”)是在发展智慧气象的大背景下,针对桌面应用、网站等传统天气预报业务系统存在时效性、移动性不足的问题,利用最新的移动互联技术,面向决策服务领导和一线决策服务人员设计研发的手机应用程序。在研发过程中实现了基于WebGIS和OpenGL ES技术的渲染引擎、基于大数据分析技术的用户行为统计,以及高安全性等多项关键技术。在此基础上实现了包括基于精细化智能网格的城市天气预报、决策服务产品、实况监测及其统计查询,以及预警预报在内的主要功能。决策服务App在日常天气预报业务、国家重大会议与活动,以及特大灾害的气象保障服务中提升了中央气象台和各级台站的精细化分析和决策服务水平。     

SOFTWARE REVIEWS

software reviews are in this article BMDP/PC CONCEPTS COMPUTERIZED ATLAS EVALUATION OF ALTERNATE PROPOSALS (EAP) EXTRA: EXPRESS TRANSIT ANALYSIS MATC CAD MOTORS TRANSPORTATION PACKAGE PCMAP: Thematic Mapping Software for the IBM Personal Computer, Version 2.3 PLACE TRANPLAN . US-ATLAS     

“圣帕”与“桑美”台风成功决策服务对比分析

从台风影响长沙前的气候背景、预报服务背景、预报依据结论、决策服务及效果等方面分析了"圣帕""桑美"影响期间的决策服务情况.结论表明:两个台风影响前气候背景不同,但预报服务背景相似;对长沙造成的影响、决策气象服务的重点不同;把握好当前的气候背景与预报服务背景,做好做准台风预报,是台风决策服务的关键.     

It starts at home? Climate policies targeting household consumption and behavioral decisions are key to low-carbon futures

Through their consumption behavior, households are responsible for 72% of global greenhouse gas emissions. Thus, they are key actors in reaching the 1.5 °C goal under the Paris Agreement. However, the possible contribution and position of households in climate policies is neither well understood, nor do households receive sufficiently high priority in current climate policy strategies. This paper investigates how behavioral change can achieve a substantial reduction in greenhouse gas emissions in European high-income countries. It uses theoretical thinking and some core results from the HOPE research project, which investigated household preferences for reducing emissions in four European cities in France, Germany, Norway and Sweden. The paper makes five major points: First, car and plane mobility, meat and dairy consumption, as well as heating are the most dominant components of household footprints. Second, household living situations (demographics, size of home) greatly influence the household potential to reduce their footprint, even more than country or city location. Third, household decisions can be sequential and temporally dynamic, shifting through different phases such as childhood, adulthood, and illness. Fourth, short term voluntary efforts will not be sufficient by themselves to reach the drastic reductions needed to achieve the 1.5 °C goal; instead, households need a regulatory framework supporting their behavioral changes. Fifth, there is a mismatch between the roles and responsibilities conveyed by current climate policies and household perceptions of responsibility. We then conclude with further recommendations for research and policy.     

It starts at home? Climate policies targeting household consumption and behavioral decisions are key to low-carbon futures

Through their consumption behavior, households are responsible for 72% of global greenhouse gas emissions. Thus, they are key actors in reaching the 1.5 °C goal under the Paris Agreement. However, the possible contribution and position of households in climate policies is neither well understood, nor do households receive sufficiently high priority in current climate policy strategies. This paper investigates how behavioral change can achieve a substantial reduction in greenhouse gas emissions in European high-income countries. It uses theoretical thinking and some core results from the HOPE research project, which investigated household preferences for reducing emissions in four European cities in France, Germany, Norway and Sweden. The paper makes five major points: First, car and plane mobility, meat and dairy consumption, as well as heating are the most dominant components of household footprints. Second, household living situations (demographics, size of home) greatly influence the household potential to reduce their footprint, even more than country or city location. Third, household decisions can be sequential and temporally dynamic, shifting through different phases such as childhood, adulthood, and illness. Fourth, short term voluntary efforts will not be sufficient by themselves to reach the drastic reductions needed to achieve the 1.5 °C goal; instead, households need a regulatory framework supporting their behavioral changes. Fifth, there is a mismatch between the roles and responsibilities conveyed by current climate policies and household perceptions of responsibility. We then conclude with further recommendations for research and policy.     

Identifying primary public transit corridors using multi-source big transit data

ABSTRACT

Effective public transit planning needs to address realistic travel demands, which can be illustrated by corridors across major residential areas and activity centers. It is vital to identify public transit corridors that contain the most significant transit travel demand patterns. We propose a two-stage approach to discover primary public transit corridors at high spatio-temporal resolutions using massive real-world smart card and bus trajectory data, which manifest rich transit demand patterns over space and time. The first stage was to reconstruct chained trips for individual passengers using multi-source massive public transit data. In the second stage, a shared-flow clustering algorithm was developed to identify public transit corridors based on reconstructed individual transit trips. The proposed approach was evaluated using transit data collected in Shenzhen, China. Experimental results demonstrated that the proposed approach is a practical tool for extracting time-varying corridors for many potential applications, such as transit planning and management.     

基于大数据的城市地质环境智能监管思路与方法

随着城市化进程加速,城市范围迅猛扩大,人口急剧膨胀,高层建筑林立,地下空间开发规模化,面临着水资源贫化、地质环境恶化和地质灾害频发等巨大挑战.为了有效地应对挑战,迫切需要在城市多要素地质环境调查并建立完善的信息系统的基础上,开展面向“智慧城市”的精细、全息三维地质建模,即建立新型的城市“玻璃国土”,实现城市地质环境时空透视,然后利用传感器、物联网和云技术,建立城市地质资源、地质环境和地质灾害动态监测数据链,最后基于地质科学大数据的同化、融合和挖掘技术,进行智能预警和管控.     

风电场设计私有云GIS平台架构研究

云计算是目前信息产业最热门的技术之一,GIS厂商纷纷将GIS软件迁移至云计算环境。与国外云计算部署不同,国内更青睐私有云技术,企业、政府机构纷纷搭建私有云GIS平台。针对风电场设计的特点,本文基于目前云计算应用和研究,构建了私有云GIS平台的体系框架,并对平台所涉及的主要关键技术进行了深入探索,对私有云GIS模式下的资源进行了详细的分类,包括核心服务、服务管理、用户访问接口3个部分。其中,核心服务将硬件基础设施、软件运行环境、应用程序抽象成服务,可满足多样化的风电场设计应用需要。