Abstract
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.
Highlights
R OAD traffic conditions and flow management continue to be an important area of research with many practical implications
This paper proposed a new smart traffic management platform to capture dynamic patterns from traffic data streams and to integrate artificial intelligence (AI) modules for real-time traffic analysis and adaptive traffic control
The main benefit of the proposed platform is that its AI modules are designed to efficiently cope with the key challenges of future transportation systems where Internet of Things (IoT) devices are widely adopted, analysis and control technologies must be more responsive and self-evolved, and social behaviors need to be taken into consideration
Summary
R OAD traffic conditions and flow management continue to be an important area of research with many practical implications. Real-time concept drift detection is crucial for effective decision making in transportation, feedback on the type of traffic incident is only received following an unknown delay This severely limits the applicability of the supervised learning nature of these algorithms. We postulate that concept drift detection in road traffic requires unsupervised online incremental machine learning to address the challenges of real-time, unlabeled, volatile data streams. It is essential that non-recurrent concept drifts are identified and utilized for updated traffic propagation and traffic flow prediction models in a real-time manner To this end, we further address several key concerns which are underexplored in current ITS, to support the development. STMP is demonstrated in Section III, and Section IV concludes the paper following a discussion on implications and potential for future research
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
