Traffic Jam Prediction through Elman Neural Network Based on Monte Carlo Simulation

Ze Yuan Yu

doi:10.4028/www.scientific.net/AMM.551.675

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 551Traffic Jam Prediction through Elman Neural...

Traffic Jam Prediction through Elman Neural Network Based on Monte Carlo Simulation

Abstract:

This paper simulates the traffic flow at upper stream intersection and the actual traffic flow at cross section of accident, and calculates the time for the queue to reach the upper stream intersection through Elman neural network. The result of 500 simulations shows that the probability for the time of the queue length being 140m: in [2.5min~3.5min] is 39.6% and in [3.5min~4min] is 52.0%. The total is 91.6%, which is highly precise. The prediction of queue length of post-accident traffic jam is of great importance to a quick recovery.

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Periodical:

Applied Mechanics and Materials (Volume 551)

Pages:

675-678

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.551.675

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Online since:

May 2014

Authors:

Ze Yuan Yu

Keywords:

Elman Neural Network (ENN), Monte Carlo Simulation, Queue Length, Traffic Jam

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