Online Incremental Machine Learning Platform for Big Data-Driven Smart Traffic Management

By Dinithi Nallaperuma, Rashmika Nawaratne, T. Bandaragoda, A. Adikari, Su Nguyen, Thimal Kempitiya, Daswin de Silva, Damminda Alahakoon, Dakshan Pothuhera. 2019

246 citations recorded by the source corpus.

The technological landscape of intelligent transport systems (ITS) has been radically transformed by the emergence of the big data streams generated by the Internet of Things (IoT), smart sensors, surveillance feeds, social media, as well as growing infrastructure needs. It is timely and pertinent that ITS harness the potential of an artificial intelligence (AI) to develop the big data-driven smart traffic management solutions for effective decision-making. The existing AI techniques that function in isolation exhibit clear limitations in developing a comprehensive platform due to the dynamicity of big data streams, high-frequency unlabeled data generation from the heterogeneous data sources, and volatility of traffic conditions. In this paper, we propose an expansive smart traffic management platform (STMP) based on the unsupervised online incremental machine learning, deep learning, and deep reinforcement learning to address these limitations. The STMP integrates the heterogeneous big data streams, such as the IoT, smart sensors, and social media, to detect concept drifts, distinguish between the recurrent and non-recurrent traffic events, and impact propagation, traffic flow forecasting, commuter sentiment analysis, and optimized traffic control decisions. The platform is successfully demonstrated on 190 million records of smart sensor network traffic data generated by 545,851 commuters and corresponding social media data on the arterial road network of Victoria, Australia.

Subfields: Building and Construction, Control and Systems Engineering, Transportation

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Youngjae Min
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Namhoon Cho

Citations in the corpus

One-Pass Learning via Bridging Orthogonal Gradient Descent and Recursive Least-Squares (2022, positive): “While these terms are often incon-sistently deﬁned in the literature, one may distinguish them based on whether we learn a single datapoint or a batch of data at a time [14].”
IoT in Smart Cities: A Survey of Technologies, Practices and Challenges (2021, background): “Apart from typical sensing modalities, with smart phones and user participation in social media, smart transportation systems are expected to increasingly include more sensing modalities [219] fused together for use in decision making for trafﬁc management purposes.”