Using artificial intelligence tools for level of service classifications within the smart city concept

Abstract

Regulating traffic in cities plays a crucial role in addressing climate change. The rapid advancement of artificial intelligence technologies offers new opportunities for managing urban traffic within the framework of Smart Cities. One common method for classifying traffic is the level of service (LoS) criterion, which evaluates traffic quality based on factors like density, speed, and location-specific characteristics. Because of these variations, LoS must be assessed individually for each location, often with expert assistance. In this article, we propose and compare several approaches for LoS classification using neural networks, fuzzy sets, and high-dimensional random vectors. Our goal is to reduce the reliance of LoS determination on local conditions, making the methodology adaptable across different locations. The results show that all three used methods achieved sufficient accuracy, which supports their potential integration with meteorological and pollution data for further applications.