通过混合卷积和图神经网络集成加强城市交通管理

Karrar S. Mohsin, Jhansilakshmi Mettu, Chinnam Madhuri, Gude Usharani, Silpa N, P. Yellamma
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引用次数: 0

摘要

由于城市发展迅速,车辆数量不断增加,交通拥堵问题给城市规划和市民福祉带来了困难。传统的交通管理无法解决城市交通不断变化的问题。交通预测和控制系统对于提高交通流量(TF)和减少拥堵至关重要。随着交通管理变得越来越复杂,智能城市需要先进的预测模型来调节城市交通流量。本文介绍了一种混合卷积神经网络(CNN)和图神经网络(GNN)模型,以实现更好的实时交通管理。该混合模型将 CNN 的空间特征提取与 GNN 的结构和关系数据处理相结合,用于分析和预测交通状况。对交通摄像头图像进行预处理,以提取空间特征。交通网络图构建用于结构研究。该模型准确捕捉了交通拓扑结构和空间。所提出的方法利用 CNN 依次处理空间数据,并将其与 GNN 集成。最终的混合模型是在不同环境和事件的一年交通数据上训练出来的。利用 MAE、RMSE 和 MAPE,将混合模型与 CNN、GNN 和传统交通预测模型(TPM)(如 ARIMA 和 SVM)进行了比较。在多个预测区间内,GNN+CNN 混合模型的 MAE、RMSE 和 MAPE 均低于基准模型。
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Enhancing Urban Traffic Management Through Hybrid Convolutional and Graph Neural Network Integration
Traffic congestion has made city planning and citizen well-being difficult due to fast city growth and the increasing number of vehicles. Traditional traffic management fails to solve urban transportation's ever-changing issues. Traffic prediction and control systems are vital for enhancing Traffic Flow (TF) and minimizing congestion. Smart cities need advanced prediction models to regulate urban TF as traffic management becomes more complex. This paper introduces a hybrid Convolutional Neural Networks (CNN) and Graph Neural Networks (GNN) model for better real-time traffic management. The hybrid model combines CNNs' spatial feature extraction with GNNs' structural and relational data processing to analyze and predict traffic conditions. Traffic camera images are pre-processed to extract spatial characteristics. Traffic network graph construction is used for structural research. The model accurately captures traffic topology and space. The proposed method sequentially processes spatial data with CNNs and integrates them with GNNs. The final hybrid model is trained on one year of traffic data from diverse circumstances and events. The hybrid model is compared to CNN, GNN, and traditional Traffic Prediction Models (TPM) like ARIMA and SVM utilizing MAE, RMSE, and MAPE. The hybrid GNN+CNN model outperforms benchmark models with lower MAE, RMSE, and MAPE across several prediction intervals.
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