Decentralised Traffic Incident Detection via Network Lasso
Qiyuan Zhu A. K. Qin Prabath Abeysekara Hussein Dia Hanna Grzybowska
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.