Mathematical Optimization Methods for Urban Traffic Flow Management in Romania: A Data-Driven Approach

Abstract

This paper explores the application of mathematical optimization methods (linear programming, genetic algorithms, and graph-based routing) in urban traffic flow management, with a focus on solving congestion mitigation and route efficiency issues in major Romanian cities. Using multi-source traffic data (2021–2023) from Bucharest, Cluj-Napoca, and Iași, we construct a hybrid optimization model that integrates real-time traffic sensor data and historical travel time records. Empirical results show the model reduces peak-hour congestion duration by 28% in Bucharest’s city center and cuts average travel time by 19% on key arterial roads—outperforming traditional traffic management systems. The research provides a scalable mathematical framework for sustainable urban mobility in Romania, addressing unique challenges such as aging infrastructure and mixed traffic flows.

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