Evaluation and Improvement Strategies for Slow Traffic Systems Based on Multi-source Big Data:A Case Study of Shijingshan District of Beijing City

The slow traffic system is an important component of urban transportation,and the prerequisite and necessary condition for Beijing to continue promoting“green priority”are establishing a good urban slow traffic system.Shijingshan District of Beijing City is taken as a research object.By analyzing and processing population distribution data,POI data,and shared bicycle data,the shortcomings and deficiencies of the current slow traffic system in Shijingshan District are explored,and corresponding solutions are proposed,in order to provide new ideas and methods for future urban planning from the perspective of data.

Classification of Beijing Line 10 Subway Living Circle Based on Multi-source Big Data

In the first-tier cities,subway has become an important carrier and life focus of people’s daily travel activities.By studying the distribution of POIs of public service facilities around Metro Line 10,using GIS to quantitatively analyze the surrounding formats of subway stations,discussing the functional attributes of subway stations,and discussing the distribution of urban functions from a new perspective,this paper provided guidance and advice for the construction of service facilities.

Multi-source Data-driven Identification of Urban Functional Areas:A Case of Shenyang,China

Urban functional area(UFA)is a core scientific issue affecting urban sustainability.The current knowledge gap is mainly reflected in the lack of multi-scale quantitative interpretation methods from the perspective of human-land interaction.In this paper,based on multi-source big data include 250 m×250 m resolution cell phone data,1.81×105 Points of Interest(POI)data and administrative boundary data,we built a UFA identification method and demonstrated empirically in Shenyang City,China.We argue that the method we built can effectively identify multi-scale multi-type UFAs based on human activity and further reveal the spatial correlation between urban facilities and human activity.The empirical study suggests that the employment functional zones in Shenyang City are more concentrated in central cities than other single functional zones.There are more mix functional areas in the central city areas,while the planned industrial new cities need to develop comprehensive functions in Shenyang.UFAs have scale effects and human-land interaction patterns.We suggest that city decision makers should apply multi-sources big data to measure urban functional service in a more refined manner from a supply-demand perspective.

Cycle-by-Cycle Queue Length Estimation for Signalized Intersections Using Multi-Source Data

In order to estimate vehicular queue length at signalized intersections accurately and overcome the shortcomings and restrictions of existing studies especially those based on shockwave theory,a new methodology is presented for estimating vehicular queue length using data from both point detectors and probe vehicles. The methodology applies the shockwave theory to model queue evolution over time and space. Using probe vehicle locations and times as well as point detector measured traffic states,analytical formulations for calculating the maximum and minimum( residual) queue length are developed. The proposed methodology is verified using ground truth data collected from numerical experiments conducted in Shanghai,China. It is found that the methodology has a mean absolute percentage error of 17. 09%,which is reasonably effective in estimating the queue length at traffic signalized intersections. Limitations of the proposed models and algorithms are also discussed in the paper.

Longitudinal Performance Assessment of Traffic Signal System Impacted by Long-Term Interstate Construction Diversion Using Connected Vehicle Data

Local arterials can be significantly impacted by diversions from adjacent work zones. These diversions often occur on unofficial detour routes due to guidance received on personal navigation devices. Often, these routes do not have sufficient sensing or communication equipment to obtain infrastructure-based traffic signal performance measures, so other data sources are required to identify locations being significantly affected by diversions. This paper examines the network impact caused by the start of an 18-month closure of the I-65/70 interchange (North Split), which usually serves approximately 214,000 vehicles per day in Indianapolis, IN. In anticipation of some proportion of the public diverting from official detour routes to local streets, a connected vehicle monitoring program was established to provide daily performances measures for over 100 intersections in the area without the need for vehicle sensing equipment. This study reports on 13 of the most impacted signals on an alternative arterial to identify locations and time of day where operations are most degraded, so that decision makers have quantitative information to make informed adjustments to the system. Individual vehicle movements at the studied locations are analyzed to estimate changes in volume, split failures, downstream blockage, arrivals on green, and travel times. Over 130,000 trajectories were analyzed in an 11-week period. Weekly afternoon peak period volumes increased by approximately 455%, split failures increased 3%, downstream blockage increased 10%, arrivals on green decreased 16%, and travel time increase 74%. The analysis performed in this paper will serve as a framework for any agency that wants to assess traffic signal performance at hundreds of locations with little or no existing sensing or communication infrastructure to prioritize tactical retiming and/or longer-term infrastructure investments.

Recommendations for Big Data in Online Video Quality of Experience Assessment

Real-time video application usage is increasing rapidly. Hence, accurate and efficient assessment of video Quality of Experience (QoE) is a crucial concern for end-users and communication service providers. After considering the relevant literature on QoS, QoE and characteristics of video trans-missions, this paper investigates the role of big data in video QoE assessment. The impact of QoS parameters on video QoE are established based on test-bed experiments. Essentially big data is employed as a method to establish a sensible mapping between network QoS parameters and the resulting video QoE. Ultimately, based on the outcome of experiments, recommendations/re- quirements are made for a Big Data-driven QoE model.

Mapping cultural ecosystem services in mountain forests using mobile phone data

The aim of the work was to determine the spatial distribution of activity in the forest on the area of the Forest Promotional Complex“Sudety Zachodnie”using mobile phone data.The study identified the sites with the highest(hot spot)and lowest(cold spot)use.Habitat,stand,demographic,topographic and spatial factors affecting the distribution of activity were also analyzed.Two approaches were applied in our research:global and local Moran’s coefficients,and a machine learning technique,Boosted Regression Trees.The results show that 11,503,320 visits to forest areas were recorded in the“Sudety Zachodnie”in 2019.The most popular season for activities was winter,and the least popular was spring.Using global and local Moran’s I coefficients,three small hot clusters of activity and one large cold cluster were identified.Locations with high values with similar neighbours(hot-spots)were most often visited forest areas,averaging almost 200,000 visits over 2019.Significantly fewer visits were recorded in cold-spots,the average number of visits to these areas was about 4,500.The value of global Moran’s I was equal to 0.54 and proved significant positive spatial autocorrelation.Results of Boosted Regression Trees modeling of visits in forest,using tree stand habitat and spatial factors accurately explained 76%of randomly selected input data.The variables that had the greatest effect on the distribution of activities were the density of hiking and biking trails and diversity of topography.The methodology presented in this article allows delineation of Cultural Ecosystem Services hot spots in forest areas based on mobile phone data.It also allows the identification of factors that may influence the distribution of visits in forests.Such data are important for managing forest areas and adapting forest management to the needs of society while maintaining ecosystem stability.

A Multi-Scale Method for PM2.5 Forecasting with Multi-Source Big Data

In the age of big data,the Internet big data can finely reflect public attention to air pollution,which greatly impact ambient PM2.5 concentrations;however,it has not been applied to PM2.5 prediction yet.Therefore,this study introduces such informative Internet big data as an effective predictor for PM2.5,in addition to other big data.To capture the multi-scale relationship between PM2.5 concentrations and multi-source big data,a novel multi-source big data and multi-scale forecasting methodology is proposed for PM2.5.Three major steps are taken:1)Multi-source big data process,to collect big data from different sources(e.g.,devices and Internet)and extract the hidden predictive features;2)Multi-scale analysis,to address the non-uniformity and nonalignment of timescales by withdrawing the scale-aligned modes hidden in multi-source data;3)PM2.5 prediction,entailing individual prediction at each timescale and ensemble prediction for the final results.The empirical study focuses on the top highly-polluted cities and shows that the proposed multi-source big data and multi-scale forecasting method outperforms its original forms(with neither big data nor multi-scale analysis),semi-extended variants(with big data and without multi-scale analysis)and similar counterparts(with big data but from a single source and multi-scale analysis)in accuracy.

城市轨道交通系统的层次化功能结构解析——以上海为例

解析城市轨道交通系统的功能结构对于建立以轨道交通为骨架的城市空间结构至关重要,其有助于优化城市空间布局,促进交通与土地利用融合,进而推动城市可持续发展。本文利用交通刷卡大数据,基于轨道交通站域的功能相似性和邻接关系提出了功能站组的概念,并形成了一套“站域功能分类—站组范围划定—站群结构识别”的方法体系。以上海市轨道交通系统为例,针对单个站域,构建表征站域土地利用功能的连续客流时间序列,并依据时间序列特征分类得到站域功能类型;将多个具有相似的出行模式和土地利用功能的相邻站域划定为功能站组;以功能站组为基本单元,采用社区发现算法,对功能站组间的客流交互网络进行分析以识别站群。研究结果表明,城市轨道交通系统的“站域—站组—站群”层次化功能结构解析方法综合了场所空间和流空间视角,有助于认识特大城市轨道交通系统的功能结构特征,并为轨道交通系统的发展提供多层次的空间优化建议。

基于大数据技术的LNG加注站智能选址方案设计

为实现“双碳”目标,国家大力推动交通清洁能源的使用,因此合理布局和建设LNG加注站就非常重要。基于交通车辆卫星定位数据的算法技术,运用机器学习模型和大数据架构等先进技术,实现对LNG加注站的智能识别,并对路段流量和LNG重卡加注行为进行智能统计分析,搭建站点价值分析模型,进行LNG加注站的规划布局和建站选址。根据对交通大数据的分析,确定了全国6000余个LNG加注站的位置分布和其中的高价值站点,借助地图进行标注并打造加注站数据系统,大幅度提高了LNG加注站选址的科学性及效率。

广州市交通治理数字化转型实践

信息技术发展推动了交通行业数字化转型,数字化如何在交通治理方面发挥作用有待深入研究。结合数字化发展情况,提出交通拥堵治理是城市治理的关键板块,数据赋能和动态管理是城市交通治理的重要抓手和必然要求。基于交通时空大数据处理、一体化交通仿真、道路网阈值界定和交通态势分析等关键技术探讨交通治理如何进行数字化转型。以广州市为例,总结在交通运行评价、交通管理政策、信号控制优化、交通组织和占道施工管理等方面的数字化治理经验和主要成效。结果表明,数字化在交通治理中起到了监测、体检、评估、反馈、修订等作用,使得治理手段更加丰富、应对措施更加精准有效。

基于流计算和大数据平台的实时交通流预测

目前交通流预测实时性差,很难满足在线分析和预测任务的需求,基于此提出一种Flink流计算框架和大数据平台结合的实时交通流预测方法。基于流计算框架实时捕捉和预处理数据,包括采用Flink的transform算子对数据进行校验和处理,将处理后的数据sink到大数据的HDFS文件系统,交由下一步的大数据并行框架进行分析建模与训练,实现基于流计算和大数据平台的实时交通流预测。实验结果表明,Flink能够实时捕捉和预处理交通流数据,把数据准时无误送入分布式文件系统中,在此基础上借助大数据框架下的并行分析和建模优势,在实时性数据分析与预测方面取得了较好的效果。

新兴数字技术驱动的太空态势感知研究

面对未来日益严峻复杂的空间环境和空间任务,现有的太空态势感知手段和能力无法满足需求。而以大数据、云计算、区块链、人工智能为代表的新兴技术在海量数据的高效处理、大规模目标识别、动态模式监测、数据和图像可视化等方面具有显著优势,运用这些技术来改进和增强现有的太空态势感知手段成为可能。重点开展基于大数据、云计算、区块链和人工智能的太空态势感知关键技术研究,通过分析以上技术在太空态势感知上的切入点和应用前景,总结太空态势感知与以上技术相结合的体系架构、处理模式和智能算法,并给出未来展望。

公路货物运输量统计测算方法综述

为全面总结公路货物运输量统计测算问题的研究进展,根据在统计过程中是否对货车运输装载和运输路径进行分离处理,分为“集计”和“非集计”测算,对各方法的优缺点进行了评述,并归纳了官方公路货物运输量统计测算方法发展的历史阶段。研究表明:计量经济和基于车型、车轴、路网结构的估算方法属于“集计”测算,是以某一特定类型的车辆群体为单位,通过建立模型将货车流量整体化,将运输载荷平均化后测算总量的方法。抽样调查法属于“非集计”测算,是以每一个车辆样本为单位,建立模型测算总量的方法。我国公路货物运输量统计测算经历了四个历史阶段,即计划经济体制下的全面调查、市场经济条件下的抽样调查、信息化背景下的抽样调查和有限数据下的融合调查,即将迎来多源异构大数据与统计数据的融合应用阶段,未来研究方向将聚焦在打破统计“数据孤岛”,开发大数据处理新工具,构建多重抽样框等方面。

基于e交通学的交通大数据MetaData交换构架

基于e交通学的交通大数据系统是通过构建由大型高性能计算机组成的集群系统来处理海量的交通数据的存储以及计算服务,不仅所需的环境十分严格,而且成本高、部署周期长、维护困难;不仅如此,随着数据量的增长,业务复杂度的增加,以及计算强度的加大,通过增加Server数量来增加其处理对海量交通数据的能力会变的十分困难,甚至需要对集群的结构进行重新的设计和部署,这不仅需要大量的人力成本和财力,而且造成了巨大的浪费。MetaData交换及部署能力成为当今大数据驱动的智能交通系统研究的重点。面对海量交通数据,如何存储、管理、处理和应用MetaData是十分关键的问题。本文提出的交通大数据MetaData交换系统(Traffic Big Data Metadata Exchange System,TBMES)实现分布式交通信息交换与互访。该构架通过实时交通数据与交通信息大数据平台实时对接,让交通信息传递具有连续性、真实性;宏观交通数据和微观交通数据无缝对接,既可分析路网交通运行态势,又可评价重要道路节点的交通效率,全面掌握区域交通运营状态;使得交通组织管理可视化、可量化、系统化、自动化;系统的输出结果,可为决策者提供决策的理论支持,促进交通决策科学化。

基于大数据统计的船舶通信网络流量估计数学模型设计

估计船舶通信网络流量,可以更好地了解网络性能,有助于优化网络配置,提高船舶通信网络的性能和可靠性,确保船舶航行过程中的通信畅通。为此,设计基于大数据统计的船舶通信网络流量估计数学模型,以提升网络流量估计效果。利用C-C算法计算船舶通信网络流量的延迟时间,通过G-P算法计算船舶通信网络流量的嵌入维数;依据延迟时间与嵌入维数,转换原始船舶通信网络流量时间序列数据,得到多维船舶通信网络流量时间序列数据;利用Map机制为各节点上的极限学习机分配数据子集,建立船舶通信网络流量估计数学模型;通过Reduce机制,汇总全部网络流量估计数学模型,得到最终的网络流量估计结果。实验证明,该模型可有效确定延时时间与嵌入维数,分别为5 min与6维;该模型可精准估计船舶通信网络流量。

基于交通大数据的成都市中心城区驾驶等时圈划分及特征分析

为提升交通出行分析中心城区交通可达性的表达精度与可视化水平,实践交通出行类大数据在国土空间规划编制及城市体检评估过程中的应用,文章以成都市作为研究对象,通过Python获取腾讯地图开放平台工作日及休息日14:00和18:00时段的实时驾驶通行信息,分别生成4个时段的中心城区等时圈范围。通过本次研究可以看出:以成都市人民政府为出发点的60min等时圈几乎可以覆盖中心城区全部范围,60min~90min等时圈则可覆盖成都市市域近半区域;成都市中心城区向南方向的交通条件最好,中心城区以南大部分区域都位于60min等时圈内,北向交通则稍弱;从4个不同时段的等时圈划分结果来看,成都市中心城区在工作日的14:00(平峰)时段交通可达性最好,其次为休息日的18:00时段,工作日的18:00(晚高峰)时段交通可达性最低。

双一流新工科背景下交通类通识课程“大数据与智能交通”的多实验教学设计

为响应创新驱动发展、“交通强国”等一系列国家战略号召,实现新工科、双一流教学改革目标,交通运输领域亟须培养一批实践与创新兼优、具备国际竞争力的高素质复合型新工科人才,交通运输专业传统课程体系亟待调整。以新型交通类通识课程“大数据与智能交通”为例,以提升交通运输专业学生实践能力、创新能力、计算机能力等素质为目标,对课程提出新要求,为培养交通运输行业需要的高素质复合型新工科人才建立坚实基础。将传统交通运输工程与大数据技术有机结合,开展多实验教学,每组实验均采用实际案例,并使用多源数据,包括列车旅行时间估计的时空数据、轨道扣件状态分类的二维图像数据以及地铁内噪声的一维音频数据等,有效提高学生创新实践能力。

面向精准预测的交通大数据处理方法与技术探究——以惠州市为例

交通大数据的质量对于精准交通流量预测和智能交通系统的效能提升至关重要。以惠州市为例,利用Python语言对1个月的运营车辆数据进行处理,采用基于位置和速度阈值的方法过滤错误值,利用均值修复法修复异常值,基于时间间隔分割轨迹,并采用隐马尔可夫模型进行路网匹配,以获取用于预测的数据成果并进行可视化分析,选定特定路段和时间段进行测试。结果显示,交通大数据处理方法在实际应用中展现精准预测的潜力和较高的实用性。

交通运输安全生产大数据分析研判系统研究与设计

交通运输安全生产大数据分析研判系统以安全生产风险、隐患、信用、危险货物动态信息、监督检查记录等各类业务数据为基础,运用大数据分析、知识图谱等技术,构建企业安全用户画像,辅助交通安全生产监督管理人员直观、清晰地把握当前交通运输行业安全生产态势,准确把握交通领域安全监管重点,科学制定交通安全监管计划,切实解决监管监督难以准确定位的难题,有效提高交通安全生产管理决策的科学性。