A Monte-Carlo Analysis between a Microscopic Model and a Mesoscopic Model for Vehicular Ad-Hoc Networks
Year: 2024
Type: article
Abstract: Vehicular Ad-hoc Networks (VANETs) are crucial for advancing intelligent transportation systems,enhancing road safety, and enabling efficient vehicle-to-vehicle and vehicle-to-infrastructurecommunications. However, accurately simulating vehicular environments' dynamic and complex natureremains a significant challenge. This study addresses this gap by benchmarking the performance of amesoscopic model, which incorporates a lane-changing technique, against a microscopic model usingMonte Carlo simulations. The microscopic model focuses on individual vehicle movements, consideringdriver behaviour and interactions, while the mesoscopic model captures traffic flow at the road segment orneighbourhood level. The updated mesoscopic model incorporates a lane change technique to better reflectrealistic vehicle movements. The updated mesoscopic model generated approximately 350 to 400 vehiclesin the simulations, with a narrow distribution and a peak frequency of about 120 vehicles. In contrast, theoriginal microscopic model produced around 800 vehicles and had a wider distribution but exhibited asimilar peak frequency. The revised model demonstrated a slight negative skewness of -0.1019, while theoriginal model showed a slight positive skewness of 0.0618. Both models displayed negative kurtosisvalues, indicating lighter tails than a normal distribution. Notably, the original model had a more negativekurtosis of -0.2931, compared to -0.1742 for the revised model. These findings suggest that themicroscopic model is more adept at capturing the variability of traffic flow, making it a more accuratereflection of real-world scenarios where vehicle interactions significantly impact vehicle dynamics duringdata transmissions.
Author: Hassan, Aslinda
Institution: Universiti Teknikal Malaysia Melaka
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6893227307
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