文章提出了一种基于物联网和软件定义网络(Software Defined Network,SDN)的智能交通管理系统的设计方案。该系统的目标是通过监控视频分析道路交通流量,动态调整交通信号灯的倒计时,以提高道路利用率。首先,提出智能交通管理系统的整...文章提出了一种基于物联网和软件定义网络(Software Defined Network,SDN)的智能交通管理系统的设计方案。该系统的目标是通过监控视频分析道路交通流量,动态调整交通信号灯的倒计时,以提高道路利用率。首先,提出智能交通管理系统的整体架构,包括交通信号灯、监控视频、光纤收发器、传输网络以及中心平台;其次,研究SDN在系统中的关键技术;最后,研究系统的实施方案,并讨论和分析了系统的特点和优势。分析表明,该系统架构具有一定的实用性,能为智能交通管理提供一种新思路。展开更多
On-road Vehicular traffic congestion has detrimental effect on three lifelines: Economy, Productivity and Pollution (EPP). With ever increasing population of vehicles on road, traffic congestion is a major challenge t...On-road Vehicular traffic congestion has detrimental effect on three lifelines: Economy, Productivity and Pollution (EPP). With ever increasing population of vehicles on road, traffic congestion is a major challenge to the economy, productivity and pollution, notwithstanding continuous developments in alternative fuels, alternative sources of energy. The research develops accurate and precise model in real time which computes congestion detection, dynamic signaling algorithm to evenly distribute vehicle densities while ensuring avoidance of starvation and deadlock situation. The model incorporates road segment length and breadth, quality and achievable average speed to compute road capacity. Vehicles installed with GPS enabled devices provide their location, which enables computing road occupancy. Road occupancy is evaluated based on number of vehicles as well as area occupied by vehicles. Ratio of road occupancy and road capacity provides congestion index important to compute signal phases. The algorithm ensures every direction is serviced once during a signaling cycle ensuring no starvation. Secondly, the definition of minimum and maximum signal timings ensures against dead lock situation. A simulator is developed to validate the proposition and proves it can ease congestion by more than 50% which is better than any of the contemporary approaches offering 15% improvement. In case of higher congestion index, alternate routes are suggested based on evaluation of traffic density graphs for shortest route or knowledge database. The algorithm to compute shortest route is optimized drastically, reducing computation cost to 3*√2N vis-à-vis computation cost of N2 by classical algorithms. The proposal brings down the cost of implementation per traffic junction from USD 30,000 to USD 2000.展开更多
文摘文章提出了一种基于物联网和软件定义网络(Software Defined Network,SDN)的智能交通管理系统的设计方案。该系统的目标是通过监控视频分析道路交通流量,动态调整交通信号灯的倒计时,以提高道路利用率。首先,提出智能交通管理系统的整体架构,包括交通信号灯、监控视频、光纤收发器、传输网络以及中心平台;其次,研究SDN在系统中的关键技术;最后,研究系统的实施方案,并讨论和分析了系统的特点和优势。分析表明,该系统架构具有一定的实用性,能为智能交通管理提供一种新思路。
文摘On-road Vehicular traffic congestion has detrimental effect on three lifelines: Economy, Productivity and Pollution (EPP). With ever increasing population of vehicles on road, traffic congestion is a major challenge to the economy, productivity and pollution, notwithstanding continuous developments in alternative fuels, alternative sources of energy. The research develops accurate and precise model in real time which computes congestion detection, dynamic signaling algorithm to evenly distribute vehicle densities while ensuring avoidance of starvation and deadlock situation. The model incorporates road segment length and breadth, quality and achievable average speed to compute road capacity. Vehicles installed with GPS enabled devices provide their location, which enables computing road occupancy. Road occupancy is evaluated based on number of vehicles as well as area occupied by vehicles. Ratio of road occupancy and road capacity provides congestion index important to compute signal phases. The algorithm ensures every direction is serviced once during a signaling cycle ensuring no starvation. Secondly, the definition of minimum and maximum signal timings ensures against dead lock situation. A simulator is developed to validate the proposition and proves it can ease congestion by more than 50% which is better than any of the contemporary approaches offering 15% improvement. In case of higher congestion index, alternate routes are suggested based on evaluation of traffic density graphs for shortest route or knowledge database. The algorithm to compute shortest route is optimized drastically, reducing computation cost to 3*√2N vis-à-vis computation cost of N2 by classical algorithms. The proposal brings down the cost of implementation per traffic junction from USD 30,000 to USD 2000.
