【作者】 张丽娟；

【导师】 张玺君；

【作者基本信息】 兰州理工大学 ， 计算机技术（专业学位）， 2021， 硕士

【摘要】 随着现代科学技术的进步,各种交通工具给人们的出行带来前所未有的快捷、方便。与此同时,人们对交通工具的依赖,致使交通工具的数量和使用频率急剧增加,有限的城市资源无法满足激增的交通需求,造成了严重的交通拥堵问题,阻碍了城市的进一步发展。针对上述问题,本文运用智能交通技术,基于预处理后的交通流数据进行有效的短时交通流预测,为出行者提供实时的路况信息,从而缓解交通拥堵给人们出行带来的不便。首先,为解决原始数据中存在的质量问题,根据交通流特性分析,对原始数据中存在的异常数据进行检测并剔除;然后根据交通数据自身的时空特征,提出基于改进历史趋势法的缺失数据修复方法,该方法利用近邻监测器所采集数据的相似性,通过提出的加权策略,对剔除后的空缺和原有的缺失数据进行修复;最后对数据进行归一化处理。其次,为提高短时交通流预测精度,弥补单个预测模型的弊端,本文提出指数平滑(Exponential Smoothing,ES)与优化的长短期记忆网络(Long Short-Term Memery,LSTM)的组合模型。引入人工鱼群算法(Artificial Fish-Swarm,AFS)解决传统LSTM采用反向传播算法容易陷入局部最优的缺点;考虑到短时交通流具有非线性,随机性和不确定性等基本特征,构建基于ES-AFS-LSTM的短时交通流预测模型。为验证模型的预测效果,在实测数据上进行相关实验,并与多种模型的预测结果进行对比、分析,得出本文提出的预测模型均优于其他模型结论。本文基于实测的交通流数据,对短时交通流预测技术展开研究,旨在为智能交通系统的发展和应用提供坚实的理论依据,对缓解交通拥堵问题和促进城市发展具有十分重要的意义及应用价值。更多还原

【Abstract】 With the progress of modern science and technology,various means of transportation bring unprecedented speed and convenience to people’s travel.Meanwhile,the number and frequency of transportation has increased sharply as people’s dependence.The limited urban resources cannot meet the surging demand for traffic,causing serious traffic congestion and hindering the further development of the city.In view of the above problems,this paper uses the intelligent transportation technology to make effective short-term traffic flow prediction based on the pre-processed traffic flow data,so as to provide real-time traffic information for travelers and alleviate the inconvenience caused by traffic congestion.Firstly,in order to solve the quality problems existing in the original data,according to the analysis of traffic flow characteristics,the abnormal data in the original data were detected and eliminated.Then,according to the spatio-temporal characteristics of traffic data itself,a missing data repair method based on the improved historical trend method is proposed.By taking advantage of the similarity within the data collected by the near neighbor monitor,the eliminated data and the original missing data are repaired by the proposed weighted strategy.Finally,data normalization is carried out.Secondly,in order to improve the accuracy of short-term traffic flow prediction and make up for the shortcomings of single forecasting model,a combined model of Exponential Smoothing(ES)and optimized Long Short-term Memory(LSTM)is proposed.Artificial Fish-Swarm(AFS)algorithm is introduced to solve the shortcoming of traditional LSTM which is easy to fall into local optimum when using back propagation algorithm.Considering the basic characteristics of short-term traffic flow such as nonlinearity,randomness and uncertainty,a short-term traffic flow prediction model based on ES-AFS-LSTM is constructed.To verify the prediction effect of the model,relevant experiments are carried out on the measured data,and the prediction results of various models are compared and analyzed,which demonstrate that ES-AFS-LSTM attains superior performance compared with state-of-the-art methods.In this paper,the short-time traffic flow prediction technology is studied based on the measured traffic flow data,aiming to provide a solid theoretical basis for the development and application of intelligent transportation system,which has a very important significance and application value for alleviating traffic congestion and promoting urban development.更多还原