基于云架构的高速公路交通安全预警系统研究

发布时间：2018-01-24 09:20

本文关键词： 高速公路交通安全预警系统云计算交通安全状态评价模型　出处：《重庆交通大学》2015年硕士论文　论文类型：学位论文

【摘要】：我国高速公路交通安全形势因其通车里程及人流物流的迅猛增加而越来越严峻,很多交通问题如突发事件、交通拥堵等出现的频率都呈较快增长的趋势。如何减少高速公路交通事故和提高其安全水平的有效途径就是现实高效的、准确的、及时的判断和预知即将可能发生的交通事故。如何搭建高效、有力的高速公路交通安全预警系统用以有效预测即将可能发生的交通事故,及时、快速、准确的将预警信息发布给用户;利用预警系统对高速公路进行实时监测,获得实时数据,经过处理数据快速的排除潜在的危害,是做到从根源上减少交通事故率的关键。所以,本文针对高速公路,目标在于构建一套完整的、集交通数据采集、数据处理、安全评价和预警信息发布于一体的交通安全预警系统。首先,从云计算的基本概率入手,介绍其概念、特点及模型及其应用;同时,分析总结了现有的高速公路交通安全预警系统框架理论,并分析云计算关键技术及对云计算在交通领域中的应用进行阐述。其次,着重分析了物联网技术在交通安全领域中的应用,介绍物联网相关理论并研究基于物联网的交通安全预警系统架构,包括基于物联网的采集系统和基于GIS的信息处理系统,为构建基于云架构的高速公路交通安全预警系统设计提供经验支撑。再者,分析高速公路预警管理和预警系统的需求,构建合理的基于云架构的高速公路交通安全预警系统,提出了其总体框架和逻辑结构,重点介绍了在物联网基础上的信息采集系统和信息发布系统。然后,本文重点研究基于云计算的交通数据处理系统,主要包括数据的预处理、基于Map Reduce的神经网络算法的交通流预测、交通安全状态评价模型。最后,针对基于MapReduce的神经网络算法的交通流预测和交通安全状态评价模型给出算例,验证云计算在交通安全预警系统中的可行性。
[Abstract]:The traffic safety situation of freeway in our country is more and more serious because of its traffic mileage and the rapid increase of people flow logistics. Many traffic problems such as unexpected events are becoming more and more serious. The frequency of traffic congestion is increasing rapidly. How to reduce highway traffic accidents and improve the safety level of the effective way is realistic, efficient and accurate. How to build an efficient and powerful highway traffic safety warning system to effectively predict the upcoming traffic accidents, timely and quickly. Accurately release the warning information to the user; It is the key to reduce the traffic accident rate by using the early warning system to monitor the freeway in real time and obtain the real time data and eliminate the potential harm quickly after processing the data. The goal of this paper is to build a complete traffic safety early warning system which integrates traffic data collection, data processing, safety evaluation and early warning information. Starting with the basic probability of cloud computing, this paper introduces the concept, characteristics, model and application of cloud computing. At the same time, this paper analyzes and summarizes the existing framework of highway traffic safety early warning system theory, and analyzes the key technologies of cloud computing and the application of cloud computing in the field of transportation. Secondly. This paper analyzes the application of Internet of things technology in the field of traffic safety, introduces the theory of Internet of things and studies the architecture of traffic safety early warning system based on Internet of things. It includes the collection system based on the Internet of things and the information processing system based on GIS, which provides empirical support for the design of highway traffic safety early warning system based on cloud architecture. This paper analyzes the requirements of expressway early warning management and early warning system, constructs a reasonable highway traffic safety early warning system based on cloud structure, and puts forward its general framework and logical structure. Focus on the introduction of the Internet of things based on the information collection system and information distribution system. Then, this paper focuses on cloud computing based traffic data processing system, mainly including data preprocessing. Traffic flow prediction and traffic safety evaluation model based on neural network algorithm of Map Reduce. Finally. An example of traffic flow prediction and traffic safety state evaluation model based on MapReduce neural network algorithm is given to verify the feasibility of cloud computing in traffic safety early warning system.
【学位授予单位】：重庆交通大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：U495

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