摘要
针对短时交通流预测中存在的训练数据过于依赖时间序列训练数据,对空间关联性考虑不足,且模型结构参数选取形式过于固定等问题,提出一种结构优化的深度信任网络短时交通流预测方法,该模型可同时训练3种与预测节点交通量相关的交通数据,增强预测的时空关联性,克服训练数据过于依赖时间序列的缺陷;同时,优化深度信任网络短时交通流预测模型结构,提出一种改进的花朵授粉算法对预测模型的隐层结构参数进行优化,避免因模型结构参数选取形式过于固定所导致的模型预测结果陷入局部最优解及实用性降低的问题。通过采集福州市两个交叉口的相关交通量数据,分别对预测模型的可行性进行评估。同时,将MFPA-DBN模型分别与不同隐层结构的深度信任网络模型及GA-LSTM、CNN-SVR、TGWO-BP 3种模型进行对比。实验结果表明:在相同训练数据的条件下,结构优化的深度信任网络(MFPA-DBN)短时交通流预测方法可行、有效,预测精度优于其他深度学习预测模型,实时性能也能满足实际要求。
To address the issues of excessive reliance on time series training data,insufficient consideration of spatial correlation,and overly fixed selection forms of model structural parameters in short-term traffic flow prediction,a structure-optimized short-term traffic flow prediction method for deep belief networks was proposed.The proposed model could simultaneously train three kinds of traffic data related to the traffic volume of the predicted nodes,enhance the spatial and temporal correlation of the prediction,and overcome the defect that the training data relied too much on time series.Meanwhile,the proposed model optimized the structure of the deep belief network short-term traffic flow prediction model and proposed an improved flower pollination algorithm to optimize the hidden layer structure parameters of the prediction model,avoiding the problem that the prediction results of the model were trapped in local optimal solutions and the practicality was reduced due to the overly fixed selection forms of model structural parameters.The feasibility of the proposed prediction model was evaluated by collecting the relevant traffic volume data of two intersections in Fuzhou.At the same time,the MFPA-DBN model was compared with the deep belief network model with different hidden layer structures,GA-LSTM,CNN-SVR and TGWO-BP models.The experimental results show that under the same training data conditions,the short-term traffic flow prediction method based on MFPA-DBN is feasible and effective,its prediction accuracy is better than that of other depth learning prediction models,and the real-time performance can also meet the actual requirements.
作者
张阳
廖晓烨
杨书敏
辛东嵘
ZHANG Yan;LIAO Xiaoye;YANG Shumin;XIN Dongrong(School of Transportation,Fujian University of Technology,Fuzhou 350118,Fujian,China;School of Transportation Engineering,Tongji University,Shanghai 201804,China;School of Civil Engineering,Fujian University of Technology,Fuzhou 350118,Fujian,China)
出处
《重庆交通大学学报（自然科学版）》
CAS
CSCD
北大核心
2023年第11期126-133,共8页
Journal of Chongqing Jiaotong University(Natural Science)
基金
福建省自然科学基金资助项目(2020J05194)。
关键词
交通运输工程
交通大数据
交通预测
深度学习
交通流
深度信任网络
花朵授粉算法
traffic and transportation engineering
traffic big data
traffic forecast
deep learning
traffic flow
deep belief network
flower pollination algorithm
