Driving Pattern Recognition and Prediction Using Neural Networks
Publish place: 16th Annual Conference on Mechanical Engineering
Publish Year: 1387
نوع سند: مقاله کنفرانسی
زبان: English
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شناسه ملی سند علمی:
ISME16_906
تاریخ نمایه سازی: 20 آبان 1386
Abstract:
In this study, traffic condition recognition and prediction is performed using real velocity data recorded in the city of Tehran. Data gathering was done using the global positioning systems (GPS) which saved vehicle’s velocity each second. Using the velocity time series, traffic groups are defined. Average velocity is used as a characteristic parameter of the traffic groups. The traffic groups are classified into four traffic conditions regarding to their average velocity. After driving condition classification, neural networks are used for driving condition prediction. A radial basis
function (RBF) network is utilized for forecasting the traffic condition in the near future. Using the RBF network, the percent of correct predictions achieve to 95%, 82% and 65% for 1, 5 and 60 seconds ahead respectively.
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Authors
Morteza Montazeri-Gh
Associated Professor, Systems Simulation and Control Lab, Department of Mechanical Engineering, Iran University of Science and Technology, Tehran, Iran
Abbas Fotouhi
PhD Student, Systems Simulation and Control Lab, Department of Mechanical Engineering, Iran University of Science and Technology, Tehran, Iran
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