Application of Chaos Theory in the Prediction of Motorised Traffic Flows on Urban Networks

Alex Alochukwu,Aderemi Adewumi,Jimmy Kagamba

doi:10.1155/2016/5656734

Abstract

In recent times, urban road networks are faced with severe congestion problems as a result of the accelerating demand for mobility. One of the ways to mitigate the congestion problems on urban traffic road network is by predicting the traffic flow pattern. Accurate prediction of the dynamics of a highly complex system such as traffic flow requires a robust methodology. An approach for predicting Motorised Traffic Flow on Urban Road Networks based on Chaos Theory is presented in this paper. Nonlinear time series modeling techniques were used for the analysis of the traffic flow prediction with emphasis on the technique of computation of the Largest Lyapunov Exponent to aid in the prediction of traffic flow. The study concludes that algorithms based on the computation of the Lyapunov time seem promising as regards facilitating the control of congestion because of the technique’s effectiveness in predicting the dynamics of complex systems especially traffic flow.

Highlights

In recent times, urban traffic road networks are faced with severe congestion problems as a result of the accelerating demand for mobility
A systematic review of Chaos Theory is presented in Section 3 with emphasis on its application to the analysis and prediction of Motorised Traffic Flow in Urban Road Networks based on the Largest Lyapunov Exponent Prediction Method
This is the main reason for proposing an alternative way of addressing complex systems like traffic flows, using effective techniques based on Chaos Theory to analyse and predict traffic flow patterns

Summary

Introduction

Urban traffic road networks are faced with severe congestion problems as a result of the accelerating demand for mobility. Previous studies have shown that ANN has stable and consistent performance even if there is an increase in the travel time interval for the traffic flow prediction This was so evident in [15] by Kumara et al where ANN based model for a neural network was used for short-term prediction of traffic flow with heterogeneous condition for nonurban highway. A systematic review of Chaos Theory is presented in Section 3 with emphasis on its application to the analysis and prediction of Motorised Traffic Flow in Urban Road Networks based on the Largest Lyapunov Exponent Prediction Method.

Motorised Traffic Flow

Chaos Theory Review

Conclusions