基于LSTM神经网络的智慧交通管理系统设计

Design of Intelligent Traffic Management System Based on LSTM Neural Network

随着城市化进程的加快,交通拥堵问题日益严重,传统交通管理系统已难以满足现代交通管理的需求。为了提升交通效率、减少拥堵并优化出行体验,文章设计了一种基于长短期记忆(Long Short-Term Memory,LSTM)神经网络的智慧交通管理系统。该系统旨在通过深度学习和大数据分析技术,实现对交通状况的精准预测与智能调控。文章先构建了智慧交通管理系统的总体框架,然后详细分析了系统的各功能层设计。在业务层深入探讨了LSTM神经网络模型的选择与优化策略,包括网络结构的调整、损失函数的优化等,以确保模型能够准确捕捉交通流数据的时序特征与非线性关系,并通过实验证明该系统具有良好的稳定性和预测精度,可为智慧交通管理提供更好的支持。

With the acceleration of urbanization,traffic congestion has become increasingly severe,and traditional traffic management systems have struggled to meet the demands of modern traffic management.To enhance traffic efficiency,reduce congestion,and optimize travel experiences,this paper designs an intelligent traffic management system based on Long Short-Term Memory(LSTM)neural networks.This system aims to achieve precise prediction and intelligent regulation of traffic conditions through deep learning and big data analytics.The paper first constructs the overall framework of the intelligent traffic management system and then provides a detailed analysis of the design of each functional layer.In the business layer,the selection and optimization strategies of the LSTM neural network model are thoroughly discussed,including adjustments to the network structure and optimization of the loss function,to ensure that the model can accurately capture the temporal characteristics and nonlinear relationships in traffic flow data.Furthermore,experiments demonstrate that the system exhibits excellent stability and prediction accuracy,providing better support for intelligent traffic management.