Abstract: Noise in urban environments is mainly due to road traffic. Whether this outcome can be simulated, the simulation outputs are strongly influenced by localized dynamics, such as in the presence of roundabouts or intersections. In such cases, the classic models encounter difficulties, since they have been built to simulate, at their best, scenarios of free flow. Many alternative models can be used to solve such problems. In this paper, a Cellular Automaton (CA) model for simulating road traffic noise is presented. This model is an extension of a previously developed CA for traffic flow and noise prediction, where a new level of complexity has been introduced. Authors, in fact, report a simplified perpendicular intersection between two single-lane roads, analyzing the traffic and acoustic implications. The model, in fact, has a double nature: on one side, it retains the discrete representation of traffic dynamics and integrates new transition rules governing vehicle crossing, right-turn maneuvers, and conflict resolution between perpendicular flows. The effect of traffic lights is also considered by imposing periodic flow interruptions. On the other side it simulates the Pressure Level (LP) and the acoustic Equivalent Level (LAeq), both weighted A, at variable points of an interactive noise map by referring to CNOSSOS formulas and establishment. A set of simulations is carried out by varying traffic density, turning ratio, and signalization conditions. Results highlight the impact of intersection dynamics on noise levels, showing that both turning maneuvers and signal control significantly affect sound emissions, especially in the proximity of the intersection area. The proposed approach provides a flexible and computationally efficient framework for analyzing the acoustic impact of simplified urban road layouts.

Keywords: Cellular Automata, Signalized Intersections, Road Traffic Noise, CNOSSOS, Python, Dynamic Noise Mapping

Pages: 782-793