Efficient identification of critical links in urban road networks is essential for optimizing traffic management, infrastructure planning, and resource allocation. Existing methods, such as simulation-based approaches, are computationally expensive and often impractical for large-scale networks. This study proposes a scalable machine learning framework capable of training on a subset of network links (20%) and predicting the criticality of remaining links with approximately 7% percentage mean error. The framework integrates structural, functional, and newly proposed features, offering a comprehensive representation of road network dynamics. Validated on two diverse datasets, namely, Luxembourg (LuST) and Monaco (MoST), the framework achieves high precision (∼72% and ∼73% in single-city scenarios) and robust cross-city performance (∼70% for LuST → MoST and ∼66% for MoST → LuST). Random Forest and Gradient Boosting emerged as the top-performing models, consistently delivering the best precisions and lowest number of errors. The inclusion of dynamic traffic metrics and advanced preprocessing techniques further enhanced predictive accuracy and generalization capabilities. This study highlights the potential of machine learning for scalable critical link evaluation, demonstrating its applicability to large-scale networks with limited data. The findings provide actionable insights for urban traffic management and open pathways for future research, including domain adaptation, temporal modeling, and integration with real-time systems.

中文翻译：

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可扩展的机器学习框架，用于预测城市网络中的关键链路

有效识别城市道路网络中的关键环节对于优化交通管理、基础设施规划和资源分配至关重要。现有方法（例如基于仿真的方法）计算成本高昂，并且对于大规模网络来说通常不切实际。本研究提出了一个可扩展的机器学习框架，能够在网络链接的子集 （20%） 上进行训练，并以大约 7% 的平均误差百分比预测剩余链接的重要性。该框架集成了结构、功能和新提出的功能，提供了道路网络动态的全面表示。该框架在卢森堡 （LuST） 和摩纳哥 （MoST） 两个不同的数据集上进行了验证，实现了高精度（在单个城市场景中为 ∼72% 和 ∼73%）和强大的跨城市性能（LuST → MoST 为 ∼70%，MoST → LuST 为 ∼66%）。Random Forest 和 Gradient Boosting 成为性能最好的模型，始终提供最佳精度和最低错误数。动态流量指标和高级预处理技术的加入进一步增强了预测准确性和泛化能力。本研究强调了机器学习在可扩展关键链路评估方面的潜力，证明了其对数据有限的大规模网络的适用性。这些发现为城市交通管理提供了可作的见解，并为未来的研究提供了开放途径，包括域适应、时间建模以及与实时系统的集成。