Intelligent Vehicular Traffic Flow Prediction Using Learning-Based Spatio-Temporal Models for Data-Driven Wireless Transportation and Urban Analytics Systems
DOI:
https://doi.org/10.63282/3050-9246.IJETCSIT-V3I2P112Keywords:
5G-Advanced, Industrial URLLC, Safety-Constrained Reinforcement Learning, Latency-Tail Guarantees, Deterministic Scheduling, AI-Driven RAN, Constrained Optimization, Time-Critical Communications, RAN Intelligence, Risk-Sensitive Optimization, Queue-Aware Scheduling, Lyapunov Optimization, Industrial Wireless NetworksAbstract
The rapid transformation of urban mobility ecosystems demands intelligent traffic prediction systems tightly integrated with next-generation wireless communication infrastructures. The paper introduces a data-driven wireless transportation and urban analytics framework based on learning that provides a spatio-temporal outlook of intelligent vehicular traffic flow prediction. The architecture proposed takes advantage of graph-based deep learning and a temporal convolution process to model the complex spatial correlation as well as dynamic temporal dependence of large scale road networks. The framework is intended to handle time-sensitive communication, which is why it is implemented on top of 5G-Advanced industrial wireless networks and includes the principles of Industrial URLLC, which allows obtaining ultra-reliability and low-latency communication. The AI-based RAN and RAN Intelligence mechanisms can be used to enable adaptive resource allocation and queue-conscious scheduling and deterministic scheduling strategies can be used to offer latency-tail guarantees to mission-critical vehicular coordination. The system combines Safety-Constrained Reinforcement Learning to solve traffic control policies with a severe safety and reliability requirement. Moreover, Lyapunov based and risk-sensitive based constrained optimization methods are used to stabilize network queue and ensure quality-of-service in the changing traffic and wireless environment. Experimental analysis shows that predictive accuracy, end-to-end latency and scalability is higher than time-tested traffic forecasting methods. The proposed framework will facilitate applications of resilient, scalable and real-time analysis of urban traffic through the integration of cutting-edge spatio-temporal learning with smart wireless infrastructure, which will help in the development of smart transportation, as well as the next-generation connected mobility infrastructure.
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