Modeling Traffic Volume Based on Highway Toll Database Using GM (1,1)

Yi Sheng An; Hua Cui; Shan Guan Wei; Xiang Mo Zhao

doi:10.4028/www.scientific.net/AMM.66-68.563

Paper Titles

Forming of Corrosion Inhibitor Film during Turbulent Flow
p.540

Warp Phenomenon Analysis in Hemming Process of Autobody Panel
p.545

Vibration Characteristics of the Non-Conservative Thermoelastic Coupling Plate
p.551

Study on the Electromagnetic Environment of the Inner and Outer Fuselage and the Hazard of the Environment to Electro-Explosive Systems
p.557

Modeling Traffic Volume Based on Highway Toll Database Using GM (1,1)
p.563

A Simulation Study on the End Milling Operation with Multiple Process Steps of Aeronautical Frame Monolithic Components
p.569

Test Research and Parameter Analysis of Grain Drying Model
p.573

Motor Fault On-Line Diagnosis Based on Innovation Energy Detection
p.577

Research of MH-Ni Battery State of Charge Estimation Based on Grey System Neural Network Model
p.583

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 66-68Modeling Traffic Volume Based on Highway Toll...

Modeling Traffic Volume Based on Highway Toll Database Using GM (1,1)

Abstract:

To circumvent the poor prediction accuracy of traffic volume models available due to the lack of traffic data and inaccurate judgments on the traffic influence factors, in this paper we established a traffic volume prediction model using grey forecasting model GM(1,1) based on the real traffic data from the highway toll database. The GM(1,1) method has advantage of the strong adaptiveness to Complex system, thus getting a great advantage over other methods for modeling such a complex nonlinear traffic volume system with many uncertain influence factors. Simulation results show that our GM(1,1) model has mean relative prediction error of 3.9%, which accomplishes our intended prediction accuracy.

You might also be interested in these eBooks

Mechanical, Materials and Manufacturing Engineering

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 66-68)

Pages:

563-568

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.66-68.563

Citation:

Cite this paper

Online since:

July 2011

Authors:

Yi Sheng An, Hua Cui, Shan Guan Wei, Xiang Mo Zhao

Keywords:

GM(1,1), Highway Toll Database, Mean Error, Traffic Volume Prediction

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] C Quek, M Pasquier, B Lim. POP-traffic: a novel fuzzy neural approach to road traffic analysis and prediction. IEEE Transactions on Intelligent Transportation Systems, 7(2006): 133-146.

DOI: 10.1109/tits.2006.874712

Google Scholar

[2] D Park, L Rilett, G Han. Forecasting multiple-period freeway link travel times using neural networks with expanded input nodes. Proc. of Applications of Advanced Technologies in Transportation, 1998: 325-332.

DOI: 10.3141/1617-23

Google Scholar

[3] B Park. . Clustering-based RBF neural network model for short-term freeway traffic volume forecasting. Proceedings of the 5th International Conference on Applications of Advanced Technologies in Transportation Engineering, 1998: 280-287.

Google Scholar

[4] T Thomas , W Weijermars, E Berkum. Predictions of urban volumes in single time series. IEEE Transactions on Intelligent Transportation Systems, 11(2010): 71-80.

DOI: 10.1109/tits.2009.2028149

Google Scholar

[5] L Corinne. An Urban Traffic Flow Model Integration Neural Networks. Transportation Research, 1997: 287-300.

Google Scholar

[6] R Chrobok, O Kaumann, J. Wahle, and M. Schreckenberg. Different methods of traffic forecasting based on real data. Eur. J. Oper. Res., 155(2004): 558–568.

DOI: 10.1016/j.ejor.2003.08.005

Google Scholar

[7] D Akay, M Atak. Grey prediction with rolling mechanism for electricity demand forecasting of Turkey. J. Energy, 32(2007): 670–675.

DOI: 10.1016/j.energy.2006.11.014

Google Scholar