Traffic Parameters Prediction Method Based on Rolling Time Series

Gui Yan Jiang; Cui Liu Kong

doi:10.4028/www.scientific.net/AMR.671-674.2946

Paper Titles

Research on the Integrated Planning of Urban Passenger Transit Network
p.2920

Study on Layout Planning of Transfer Hub Integration of Urban Passenger Transit
p.2928

The Characteristic of Viewpoint’s Time-Transients in Traffic Operation
p.2937

The Design of Intelligent Street Lighting Control System
p.2941

Traffic Parameters Prediction Method Based on Rolling Time Series
p.2946

Prediction for Traffic Flow of BP Neural Network based on DE Algorithm
p.2951

The Research of Optimal Allocation of Port Berth and Quay Crane Based on Heuristic Algorithm
p.2956

Analysis on Important Parameters Design of Aircraft
p.2961

Study on Safety Evacuation Time for Passengers in Subway Station and Its Application
p.2965

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 671-674Traffic Parameters Prediction Method Based on...

Traffic Parameters Prediction Method Based on Rolling Time Series

Abstract:

The technologies of traffic parameters prediction provide future traffic information so that management measures for traffic congestion can be made timely and accurately based on the retrieved information. According to the shortcomings of traditional methods for predicting traffic parameters, a rolling time series method is proposed through improving the traditional time series methods. To test the performance of our proposed approach, the rolling time series method is compared with the traditional time series methods using measured traffic flow based on a part road network of a large urban area in China. The results show that the prediction effects by the rolling time series method developed in this study are better than traditional approaches.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 671-674)

Pages:

2946-2950

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.671-674.2946

Citation:

Cite this paper

Online since:

March 2013

Authors:

Gui Yan Jiang, Cui Liu Kong

Keywords:

Jilin Institute of Architecture and Civil Engineering, Predicting, Rolling Time Series, Traffic Parameters

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] J. Dong: Anticipatory Traveler Information and Pricing for Real-time Traffic Management. Northwestern University, Evanston (2008).

Google Scholar

[2] Y.C. Chiu: Generalized Real-time Route Guidance Strategies in Urban Networks. Texas University, Austin (2008).

Google Scholar

[3] V.D. Voortm, M. Dougherty, S. Watson: Combining Kohonen Maps with ARIMA Time Series Models to Forecast Traffic Flow. Transport Res C-Emer, Vol. 4(5) (1996), pp.307-318.

DOI: 10.1016/s0968-090x(97)82903-8

Google Scholar

[4] D. Tikunov and T. Nishimura: Traffic Prediction for Mobile Network Using Holt-Winter's Exponential Smoothing. The 15th International Conference on Software, (2007).

DOI: 10.1109/softcom.2007.4446113

Google Scholar

[5] Gowrishankar and P.S. Satyanarayana: Neural Network based Traffic Prediction for Wireless Data Networks. Int J Comput Intell Sys, Vol. 1 (2008), pp.379-389.

DOI: 10.2991/ijcis.2008.1.4.9

Google Scholar

[6] L. Bucur, A. Florea and B.S. Petrescu: An Adaptive Fuzzy Neural Network for Traffic Prediction. The 18th Mediterranean Conference on Control and Automation (2010).

DOI: 10.1109/med.2010.5547648

Google Scholar