A recurrent neural network based microscopic car following model to predict traffic oscillation
Artikel i vetenskaplig tidskrift, 2017

This paper proposes a recurrent neural network based microscopic car following model that is able to accurately capture and predict traffic oscillation. Neural network models have gained increasing popularity in many fields and have been applied in modelling microscopic traffic flow dynamics due to their parameter-free and data-driven nature. We investigate the existing neural network based microscopic car following models, and find out that they are generally accurate in predicting traffic flow dynamics under normal traffic operational conditions. However, they do not maintain accuracy under conditions of traffic oscillation. To bridge this research gap, we first propose four neural network based models and evaluate their applicability to predict traffic oscillation. It is found that, with an appropriate structure and objective function, the recurrent neural network based model has the capability of perfectly re-establishing traffic oscillations and distinguish drivers characteristics. We further compare the proposed model with a classical car following model (Intelligent Driver Model). Based on our case study, the proposed model outperforms the classical car following model in predicting traffic oscillations with different driver characteristics.



Recurrent neural networks

Traffic flow dynamics


M. F. Zhou

University of Technology Sydney

Griffith University

Xiaobo Qu

Arkitektur och samhällsbyggnadsteknik

X. P. Li

University of South Florida Tampa

Tramsportation Research, Part C: Emerging Technologies

0968-090X (ISSN)

Vol. 84 245-264