Prediction the level of service by using neural network algorithms
Publish place: دومین همایش سیستم های حمل و نقل هوشمند جاده ای
Publish Year: 1395
نوع سند: مقاله کنفرانسی
زبان: English
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شناسه ملی سند علمی:
RMTO02_073
تاریخ نمایه سازی: 13 شهریور 1396
Abstract:
Traffic congestion might be considered as a severe problem that faced the high population cities around the world. Transportation engineering tends to investigate its behaver based on information gathered different devices. In this paper basing on the GPS data, we tried to predict the congestion in the streets. For this aim neural network and regression models have been studied. The results show that NARX neural network, MLP preceptor neural network provides an accurate prediction. As the case of this work were areal historical GPS data gathered from the roads of Beijing – the Chinese city- during 2008 was considered. The proposed approach achieved a high level of prediction accuracy. The traffic congestion has been predicted with 90-94% accuracy
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Authors
Elaf Al Hashmi
Department of Computer Science Amirkabir University of Technology Tehran, Iran
Noor Al olawi
Department of Computer Science Amirkabir University of Technology Tehran, Iran
Mehdi ghatee
Department of Computer Science Amirkabir University of Technology Tehran, Iran
hamidreza eftekhari
Department of Computer Science Amirkabir University of Technology Tehran, Iran