A Comparative Study of Optimized-LSTM Models Using Tree-Structured Parzen Estimator for Traffic Flow Forecasting in Intelligent Transportation  

作　　者：Hamza Murad Khan Anwar Khan Santos Gracia Villar Luis Alonso DzulLopez Abdulaziz Almaleh Abdullah M.Al-Qahtani 

机构地区：[1]Department of Electronics,University of Peshawar,Peshawar,25120,KPK,Pakistan [2]Department of Project Management(L.A.D.L),Higher Polytechnic School(S.G.V),Universidad Europea del Atlantico,Santander,39011,Spain [3]Faculty of Engineering,Universidad Internacional Iberoamericana,Campeche,24560,Mexico [4]Faculty of Engineering,Universidade Internacional do Cuanza,Cuito,EN250,Bie,Angola [5]Department of Project Management,Universidad Internacional Iberoamericana,Campeche,24560,Mexico [6]Department of Project Management,Department of Management Sciences,Universid de La Romana,La Romana,22000,Dominican Reublic [7]Information Systems Department,College of Computer Science,King Khalid University,Abha,61421,Saudi Arabia [8]Department of Electrical and Electronic Engineering,College of Engineering&Computer Science,Jazan University,Jazan,45142,Saudi Arabia

出　　处：《Computers, Materials & Continua》2025年第5期3369-3388,共20页计算机、材料和连续体(英文)

摘　　要：Traffic forecasting with high precision aids Intelligent Transport Systems(ITS)in formulating and optimizing traffic management strategies.The algorithms used for tuning the hyperparameters of the deep learning models often have accurate results at the expense of high computational complexity.To address this problem,this paper uses the Tree-structured Parzen Estimator(TPE)to tune the hyperparameters of the Long Short-term Memory(LSTM)deep learning framework.The Tree-structured Parzen Estimator(TPE)uses a probabilistic approach with an adaptive searching mechanism by classifying the objective function values into good and bad samples.This ensures fast convergence in tuning the hyperparameter values in the deep learning model for performing prediction while still maintaining a certain degree of accuracy.It also overcomes the problem of converging to local optima and avoids timeconsuming random search and,therefore,avoids high computational complexity in prediction accuracy.The proposed scheme first performs data smoothing and normalization on the input data,which is then fed to the input of the TPE for tuning the hyperparameters.The traffic data is then input to the LSTM model with tuned parameters to perform the traffic prediction.The three optimizers:Adaptive Moment Estimation(Adam),Root Mean Square Propagation(RMSProp),and Stochastic Gradient Descend with Momentum(SGDM)are also evaluated for accuracy prediction and the best optimizer is then chosen for final traffic prediction in TPE-LSTM model.Simulation results verify the effectiveness of the proposed model in terms of accuracy of prediction over the benchmark schemes.

关 键 词：Short-term traffic prediction sequential time series prediction TPE tree-structured parzen estimator LSTM hyperparameter tuning hybrid prediction model 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]