基于聚类与贝叶斯网络，预测事故引发的拥堵并解释因果关系。
Accident-Driven Congestion Prediction and Simulation: An Explainable Framework Using Advanced Clustering and Bayesian Networks
- 用AutoML优化深度嵌入聚类，自动标记事故导致的拥堵状态。
- 贝叶斯网络模型准确率达95.6%，能捕捉事故与拥堵的复杂关联。
- 结合SUMO仿真验证，适合交通管理与智能城市决策者使用。
城市交通因事故等不确定性因素常引发严重拥堵，造成延迟加剧、排放上升及安全隐患。为应对这一问题，本文提出一种鲁棒的事故影响拥堵预测框架。通过增强型自动机器学习（AutoML）的深度嵌入聚类（DEC）对事故数据进行拥堵标签划分，并利用贝叶斯网络（BN）预测拥堵概率。采用基于证据的场景在交通仿真平台SUMO中评估模型预测准确性。结果表明，经AutoML优化的DEC优于传统聚类方法；所提BN模型整体准确率达95.6%，展现出对事故致堵复杂关系的理解能力。在SUMO中的验证显示，该模型预测的拥堵状态与仿真结果高度一致，证明其在保障城市交通流畅方面的高可靠性。
原文摘要 · Abstract (English)
Traffic congestion due to uncertainties, such as accidents, is a significant issue in urban areas, as the ripple effect of accidents causes longer delays, increased emissions, and safety concerns. To address this issue, we propose a robust framework for predicting the impact of accidents on congestion. We implement Automated Machine Learning (AutoML)-enhanced Deep Embedding Clustering (DEC) to assign congestion labels to accident data and predict congestion probability using a Bayesian Network (BN). The Simulation of Urban Mobility (SUMO) simulation is utilized to evaluate the correctness of BN predictions using evidence-based scenarios. Results demonstrate that the AutoML-enhanced DEC has outperformed traditional clustering approaches. The performance of the proposed BN model achieved an overall accuracy of 95.6%, indicating its ability to understand the complex relationship of accidents causing congestion. Validation in SUMO with evidence-based scenarios demonstrated that the BN model's prediction of congestion states closely matches those of SUMO, indicating the high reliability of the proposed BN model in ensuring smooth urban mobility.
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