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 cons...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.展开更多
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 rout...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 sufficien<span style="font-family:Verdana;">t sensing or communication equipment to obtain infrastructure-based tra</span><span style="font-family:Verdana;">ffic 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.</span>展开更多
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...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。展开更多
文摘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.
文摘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 sufficien<span style="font-family:Verdana;">t sensing or communication equipment to obtain infrastructure-based tra</span><span style="font-family:Verdana;">ffic 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.</span>
文摘基于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。
