Publisher of Iranian Journals and Conference Proceedings

Please waite ..
Publisher of Iranian Journals and Conference Proceedings
Login |Register |Help |عضویت کتابخانه ها

Modeling Average Daily Traffic Volume using Neural Network-Wavelet Hybrid Method

Year: 1393
COI: JR_ACSIJ-3-3_008
Language: EnglishView: 521
This Paper With 9 Page And PDF Format Ready To Download

Buy and Download

با استفاده از پرداخت اینترنتی بسیار سریع و ساده می توانید اصل این Paper را که دارای 9 صفحه است به صورت فایل PDF در اختیار داشته باشید.
آدرس ایمیل خود را در کادر زیر وارد نمایید:


Shahin Shabani - Department of Civil Engineering, Payam Noor University, Tehran, Iran
Mahdi Motamedi sedeh - Department of Civil Engineering, Payam Noor University, Tehran, Iran


Forecasting traffic volume accurately and in a timely manner plays an important role to providing real-time traffic information, reducing congestion in pathways, and improving traffic safety. A combination of multi-layer back-propagation neural networks (BPNN) and wavelet transform is used for forecasting average daily traffic volume. Real data used in modeling are taken from the Qom-Tehran road during 2006-2008. Given the proposed method (WBPNN), the traffic volume data were initially preprocessed using wavelet transform. The input signal (the daily traffic volume time series) is decomposed into low- and highfrequency components up to 5 levels using the mother wavelet function Haar, so that more complete information would be obtained regarding the problem dynamics. The processed data are then fed to the neural network as training and test data. The trained network is validated considering evaluation functions such as MAE, MAPE, and VAPE. The results indicate that the proposed method predicts daily traffic volume with great precision and puts forward a model using native parameters, in addition to increased prediction accuracy.


Paper COI Code

This Paper COI Code is JR_ACSIJ-3-3_008. Also You can use the following address to link to this article. This link is permanent and is used as an article registration confirmation in the Civilica reference:

How to Cite to This Paper:

If you want to refer to this Paper in your research work, you can simply use the following phrase in the resources section:
Shabani, Shahin and Motamedi sedeh, Mahdi,1393,Modeling Average Daily Traffic Volume using Neural Network-Wavelet Hybrid Method,

Research Info Management

Certificate | Report | من نویسنده این مقاله هستم
این Paper در بخشهای موضوعی زیر دسته بندی شده است:

اطلاعات استنادی این Paper را به نرم افزارهای مدیریت اطلاعات علمی و استنادی ارسال نمایید و در تحقیقات خود از آن استفاده نمایید.


The specifications of the publisher center of this Paper are as follows:
Type of center: پیام نور
Paper count: 63,760
In the scientometrics section of CIVILICA, you can see the scientific ranking of the Iranian academic and research centers based on the statistics of indexed articles.

New Papers

Share this page

More information about COI

COI stands for "CIVILICA Object Identifier". COI is the unique code assigned to articles of Iranian conferences and journals when indexing on the CIVILICA citation database.

The COI is the national code of documents indexed in CIVILICA and is a unique and permanent code. it can always be cited and tracked and assumed as registration confirmation ID.