A Model for Predicting Short‐Term Operating Speeds of Compact Passenger Vehicles on Interchange Ramps Within Urban Expressway Networks

Abstract

The prediction of operating speed plays a crucial role in road design and safety assessment, especially on complex urban expressway interchange ramps. This task is challenging due to various influences like road conditions, traffic dynamics, and driver behavior. This study aims to identify the optimal model configuration for predicting operating speeds on urban expressway interchange ramps. Three models are established: a short‐term operating speed model based on a generalized linear model (GLM), a GLM incorporating for spatial correlation (GLMS), and a deep neural network model considering spatial correlation (DNNS). Each model incorporates considerations for the impact of the plan, profile, and other facets of the interchange ramp in urban expressways. Naturalistic driving experiments are conducted in Shanghai, 70% for model calibration and 30% for validation. Comparative analysis shows that the DNNS model outperforms the others, effectively capturing speed fluctuations along the interchange ramp, demonstrating its robustness and generalization capabilities.