ITSC 2025 Paper Abstract

Paper VP-VP.7

Feofilova, Anastasia (Don State Technical University), Jiang, Jixiao (Don State Technical University)

TTAO-CNN-SEAttention: A Deep Learning Framework for Short-Term Traffic Flow Prediction

Scheduled for presentation during the Video Session "On-Demand Video Presentations" (VP-VP), Saturday, November 22, 2025, 08:00−18:00, On-Demand Platform

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on April 2, 2026

Keywords AI, Machine Learning for Real-time Traffic Flow Prediction and Management, Real-time Incident Detection and Emergency Management Systems in ITS, Demand-Responsive Transit Systems for Smart Cities

Abstract

摘要 - 城市道路的短期交通流预测在降低交通强度和优化交通管理方面发挥着重要作用。该文利用MATLAB构建了一个基于深度学习的预测模型，旨在高效捕捉短期交通流的非线性特征，提高交通流预测的准确性。该预测模型使用经典的卷积神经网络模型，并集成了三角测量拓扑聚合优化器（TTAO）算法和 SEAttention 机制。用于预测实验的数据来自意大利都灵城市交通路网数据集，采用 TTAO 算法优化原始数据的特征，提高噪声数据的鲁棒性，便于清洗。此外，SEAttention 机制可以准确识别输入数据的特征，并自动调整卷积神经网络（CNNᦀ