Short-Time Passenger Volume Forecasting of Urban Rail Transit Based on Multiple Fusion

Ji Wei Li

doi:10.4028/www.scientific.net/AMM.641-642.773

Paper Titles

Urban Rail Transit Network Fare Analysis
p.753

Study on Stability of Last Train Connection under Delays on Urban Rail Transit
p.757

Current Situation and Thoughts on the Cultivation of Undergraduate Urban Rail Transit Talents in China
p.763

A GPS Data Based Monitor Model of Unoccupied Taxis at Transportation Hubs
p.767

Short-Time Passenger Volume Forecasting of Urban Rail Transit Based on Multiple Fusion
p.773

Study on NTCIP-Based Architecture of Data Center for Transportation Information System in China
p.777

Logit Model-Based Research on Market Position of Jing-Hu High Speed Rail
p.783

A Comparative Study Reducing Road Noise Using Active Noise Cancelation
p.793

A Method for Reconstructing Vehicle - Vehicle Impact Accidents Based on Pc-Crash
p.799

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 641-642Short-Time Passenger Volume Forecasting of Urban...

Short-Time Passenger Volume Forecasting of Urban Rail Transit Based on Multiple Fusion

Abstract:

Aiming at enhancing the estimation performance of passenger volume for urban transit, based on analysis of horizontal and vertical characteristics and principles of multiple fusion technology, respectively, establishing 3 forecast models, and compared with the measured data. The results show that, the proposed new method has a comparative advantage to reduce prediction error.

You might also be interested in these eBooks

Hydraulic Engineering and Sustainable City Development III

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 641-642)

Pages:

773-776

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.641-642.773

Citation:

Cite this paper

Online since:

September 2014

Authors:

Ji Wei Li*

Keywords:

Multiple Fusion, Passenger Volume, Short-Time Prediction, Urban Rail Transit

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] QinBenxuan: ResearchonUrbanRailPassengerFlow(WuHan university of technology, Wuhan2009).

Google Scholar

[2] JansonBN. Dynamictraffic assignmentforurbanroadnetwork: TRB. Vol. 34(1991), p.143.

Google Scholar

[3] Nanthawichit, C., Nakatsuji, T., and Suzuki, H. . Application of probe vehicle data for real-time traffic state estimation and short term travel time prediction on a freeway[C]. WashingtonD.C.: Presented at the 82nd Annual Meeting (CD-ROM), Transportation Research Board, 2003.

DOI: 10.3141/1855-06

Google Scholar

[4] WangLina: Traffic ForecastingandSchedulingoftheUrbanPassengerRailNetworkBasedonHistoricalData(BeiJingJiaoTong university, BeiJing2011).

Google Scholar

[5] ZHOU Yan, ZHUO Lei-shan, YUE Yi-xiang. Study on short-time passenger volume forecasting of urban rail transit based on combined model[R]. Proceedings of International Conference on Engineering and Business Management, ChengDu: (2000).

Google Scholar

[6] Li Jiwei: Estimation and prediction of link travel time for urban trunk and secondary streets (JiLin university, Changchun 2012).

Google Scholar

[7] Li Mitao: Studyonthe Methods of Located Traffic Parameters Short-term and Multi-steps Prediction for Expressway(JiLin university, Changchun 2009).

Google Scholar

[8] Chen Shuxian, Wang Wei. Grey neural network forecasting for traffic flow: Journal of Southeast University(Natural Science Edition). Vol. 34(2004), p.541.

Google Scholar