Forecasting of Congestion in Traffic Neural Network Modelling Using Duffing Holmes Oscillator

World Multidisciplinary Civil Engineering-Architecture-Urban Planning Symposium - WMCAUS

土木建筑工程

Mrgole, Anamarija L.^1 ; elan, Marko^1 ; Mesarec, Beno^1

Faculty of Civil Engineering, Transportation Engineering and Architecture, University of Maribor, Smetanova ulica 17, Maribor

2000, Slovenia^1

关键词: Duffing;    Neural network modelling;    Road infrastructures;    Short-term traffic flow;    Specific time;    Traffic flow;    Traffic flow forecasting;    Traffic loads;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/245/4/042030/pdf
DOI  :  10.1088/1757-899X/245/4/042030

学科分类：土木及结构工程学

来源: IOP

【 摘 要 】

Forecasting of congestion in traffic with Neural Network is an innovative and new process of identification and detection of chaotic features in time series analysis. With the use of Duffing Holmes Oscillator, we estimate the emergence of traffic flow congestion when the traffic load on a specific section of the road and in a specific time period is close to exceeding the capacity of the road infrastructure. The orientated model is validated in six locations with a specific requirement. The paper points out the issue of importance of traffic flow forecasting and simulations for preventing or rerouting possible short term traffic flow congestions.