2019 •
Prediction of Road Congestion Diffusion based on Dynamic Bayesian Networks
Authors:
Xinyue Fan, Jiao Zhang, Qi Shen
Abstract:
Based on the passing data and floating car data (FCD) collected by the traffic police of Shenzhen Public Security Bureau, China. A dynamic Bayesian network (DBN) model is constructed to describe the change and dissipation of road congestion. The prediction model of road congestion diffusion is established by integrating Internet traffic data and FCD data. To provide a theoretical basis for solving urban traffic congestion, the experimental results show that the prediction results coincide with the actual state of the Internet road conditions, w (...)
Based on the passing data and floating car data (FCD) collected by the traffic police of Shenzhen Public Security Bureau, China. A dynamic Bayesian network (DBN) model is constructed to describe the change and dissipation of road congestion. The prediction model of road congestion diffusion is established by integrating Internet traffic data and FCD data. To provide a theoretical basis for solving urban traffic congestion, the experimental results show that the prediction results coincide with the actual state of the Internet road conditions, which proves the feasibility and practicability of the prediction method. (Read More)
We have placed cookies on your device to help make this website and the services we offer better. By using this site, you agree to the use of cookies. Learn more