Traffic incident detection plays a key role in intelligent transportation systems, which has gained great attention in transport engineering. In the past, traditional machine learning (ML) based detection methods achieved good performance under a centralised computing paradigm, where all data are transmitted to a central server for building ML models therein. Nowadays, deep neural networks based federated learning (FL) has become a mainstream detection approach to enable the model training in a decentralised manner while warranting local data governance. Such neural networks-centred techniques, however, have overshadowed the utility of well-established ML-based detection methods. In this work, we aim to explore the potential of potent conventional ML-based detection models in modern traffic scenarios featured by distributed data. We leverage an elegant but less explored distributed optimisation framework named Network Lasso, with guaranteed global convergence for convex problem formulations, integrate the potent convex ML model with it, and compare it with centralised learning, local learning, and federated learning methods atop a well-known traffic incident detection dataset. Experimental results show that the proposed network lasso-based approach provides a promising alternative to the FL-based approach in data-decentralised traffic scenarios, with a strong convergence guarantee while rekindling the significance of conventional ML-based detection methods.

翻译：交通事件检测在智能交通系统中起着关键作用，近年来在交通工程领域受到广泛关注。过去，基于传统机器学习（ML）的检测方法在集中式计算范式下取得了良好性能，所有数据均传输至中央服务器以构建机器学习模型。如今，基于深度神经网络的联邦学习（FL）已成为主流检测方法，可在保障本地数据治理的同时实现去中心化模型训练。然而，这种以神经网络为中心的技术却掩盖了成熟机器学习检测方法的实用价值。本研究旨在探索传统高效机器学习检测模型在现代分布式数据交通场景中的潜力。我们利用一种精巧但较少探索的分布式优化框架——网络套索（Network Lasso），该框架可保证凸问题公式的全局收敛性，将高效的凸机器学习模型与其集成，并与集中式学习、本地学习及联邦学习方法在知名交通事件检测数据集上进行对比。实验结果表明，所提出的基于网络套索的方法为数据去中心化交通场景提供了一种有前景的替代联邦学习方法的方案，具有强收敛保证，同时重新彰显了传统机器学习检测方法的重要意义。