Optimizing Urban Traffic Flow through Advanced Tensor Analysis and Multilinear Algebra: A Computational Approach to Enhancing Smart City Dynamics

Abstract

Growing traffic congestion is a worldwide problem that collides against the aims of environmental sustainability, economic productivity, and the quality of life in cities. This research proposes a new computational framework for traffic management that integrates advanced tensor analysis and methods from multilinear algebra. We have developed and validated a new predictive model that greatly improves the optimization of traffic flows by synthesizing the naturally complex multi-dimensional traffic data analysis. Our results demonstrate that, compared with existing systems, the proposed approach results in higher accuracy of prediction, much improved computational efficiency, and provides scalable and adaptable solutions for application in a wide range of urban habitats. Such research may push the boundaries further on the smart city infrastructures to provide a very well-founded mathematical framework for the dynamics of improved urban mobility through high-level data-oriented information.