Prediction of Road Congestion Diffusion based on Dynamic Bayesian Networks

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.

[1]  Yuanhua Jia,et al.  Widespread Traffic Congestion Prediction for Urban Road Network Based on Synergetic Theory , 2014 .

[2]  Takayuki Ito,et al.  Adaptive Model for Traffic Congestion Prediction , 2016, IEA/AIE.

[3]  Jiannong Cao,et al.  Exploring traffic congestion correlation from multiple data sources , 2017, Pervasive Mob. Comput..

[4]  Bruno Castanier,et al.  A Two‐Dimension Dynamic Bayesian Network for Large‐Scale Degradation Modeling with an Application to a Bridges Network , 2017, Comput. Aided Civ. Infrastructure Eng..

[5]  Hiroshi Sasaki,et al.  Pheromone model: application to traffic congestion prediction , 2005, AAMAS '05.

[6]  Laura Sacerdote,et al.  Using dynamic Bayesian networks to model technical risk management efficiency , 2017, Qual. Reliab. Eng. Int..

[7]  Quan Wang,et al.  Prediction of Urban Road Congestion Using a Bayesian Network Approach , 2014 .

[8]  Haris N. Koutsopoulos,et al.  Traffic state prediction using Markov chain models , 2007, 2007 European Control Conference (ECC).

[9]  Santanu Chaudhury,et al.  Video-based road traffic monitoring and prediction using dynamic Bayesian networks , 2017 .

[10]  Jun Zhao.  Research on Prediction of Traffic Congestion State , 2015 .

[11]  Alexander Skabardonis,et al.  Prediction of Arrival Profiles and Queue Lengths along Signalized Arterials by using a Markov Decision Process , 2005 .

[12]  Marc Parizeau,et al.  Training Hidden Markov Models with Multiple Observations-A Combinatorial Method , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  Gaetano Fusco,et al.  Short-term traffic predictions on large urban traffic networks: Applications of network-based machine learning models and dynamic traffic assignment models , 2015, 2015 International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS).

[14]  Baoxin Zhao,et al.  A data-driven congestion diffusion model for characterizing traffic in metrocity scales , 2017, 2017 IEEE International Conference on Big Data (Big Data).

[15]  Yang Wei.  Acquisition of travel time based on inductive coil detector and probe vehicle , 2009 .