Applicability Analysis of an Extended METANET Model in Traffic-State Prediction for Congested Freeway Corridors

Abstract

Macroscopic traffic flow models are often applied as prediction models in proactive traffic control strategies. For model calibration and validation, existing tests have been conducted on simple freeway corridors, meaning that model performance is still unknown for complicated corridors with multiple potential bottlenecks. To address this research gap, this study firstly proposes extensions for a common model from the literature. The fundamental diagram of this model is extended to adapt it to variations in discharge flow that occur when multiple bottlenecks are activated on complex corridors. Subsequently, the extended model is calibrated with traffic data from an actual freeway corridor, called Whitemud Drive, in Edmonton, Canada. The calibration is conducted using segment-specific and global parameters, respectively. Then the extended model is validated to confirm its applicability in real-life conditions. It is concluded that the extended model can predict traffic-state evolutions under complex traffic conditions.