PREDICTING THE FUNDAMENTAL SPEED - VOLUME RELATIONSHIP DIAGRAM USING NEURAL NETWORK. (CASE STUDY, GHAZVIN-RASHT AXIS)

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

KHODAEE A.; TABIBI M.; BATENI M.

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: ASAS Year:2009 | Volume:11 | Issue:24 Page(s): 28-43

Download Full-Text

Persian Verion

View:

941

Download:

Cites:

Information Journal Paper

Title

PREDICTING THE FUNDAMENTAL SPEED - VOLUME RELATIONSHIP DIAGRAM USING NEURAL NETWORK. (CASE STUDY, GHAZVIN-RASHT AXIS)

Author(s)

KHODAEE A. | TABIBI M. | BATENI M. | Issue Writer Certificate

Keywords

Not Registered.

Abstract

This paper discusses an object- oriented neural network model that was developed for predicting traffic flow- speed fundamental diagram on a section (Gange) of the Ghazvin- Rasht road and 50 kilometers from Rasht, Gilan, Iran. The feasibility of this approach is demonstrated through multi layer perseptron network (MLP) that uses back propagation approach (BP) for training data, which was developed for predicting hourly speed data and Traffic Flow data up to one hour in the future. The results obtained indicate that the MLP is capable of predicting mean speed and traffic flow up to one day in the future with a high degree of accuracy. (99% for predicting mean speed and 97% for predicting traffic flow on non-holidays). Similar models which were developed for predicting mean speed and traffic flow on the similar facility, using regression approach, were successful in short term traffic forecasting with lower degree of accuracy. The results which is obtained in this paper shows the high capability of neural network approach for short term traffic prediction.

Cites

No record.

References

No record.

Cite

APA: Copy

KHODAEE, A., TABIBI, M., & BATENI, M.. (2009). PREDICTING THE FUNDAMENTAL SPEED - VOLUME RELATIONSHIP DIAGRAM USING NEURAL NETWORK. (CASE STUDY, GHAZVIN-RASHT AXIS). ASAS, 11(24), 28-43. SID. https://sid.ir/paper/226870/en

Vancouver: Copy

KHODAEE A., TABIBI M., BATENI M.. PREDICTING THE FUNDAMENTAL SPEED - VOLUME RELATIONSHIP DIAGRAM USING NEURAL NETWORK. (CASE STUDY, GHAZVIN-RASHT AXIS). ASAS[Internet]. 2009;11(24):28-43. Available from: https://sid.ir/paper/226870/en

IEEE: Copy

A. KHODAEE, M. TABIBI, and M. BATENI, “PREDICTING THE FUNDAMENTAL SPEED - VOLUME RELATIONSHIP DIAGRAM USING NEURAL NETWORK. (CASE STUDY, GHAZVIN-RASHT AXIS),” ASAS, vol. 11, no. 24, pp. 28–43, 2009, [Online]. Available: https://sid.ir/paper/226870/en

Related Journal Papers