Chapter
Jan 13, 2020
Sixth International Conference on Transportation Engineering

Calculation of Affected Passenger Flow for Rail Transit Operation Disruption Based on Timetable

Publication: ICTE 2019

ABSTRACT

Quantitative calculation of the affected passenger flow scale under the operation disruption of urban rail transit is of great significance for the formulation of emergency strategies. The different impact of operation disruption on passengers may lead to various affected passenger flow. Based on the timetable, the duration of disruption, and the shortest path time, this paper considers the affected departure time range of origin stations in affected OD (origin-destination) station pairs to calculate the OD matrix of the affected passenger flow with historical data. Various affected passenger flow and the total amount of affected passenger flow can be calculated by the OD matrix of affected passenger flow. The analysis and calculation for the local network of Nanjing Metro proves the feasibility of the proposed method, which can quantify the scale of affected passengers, and is helpful to the emergency management.

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ACKNOWLEDGEMENT

This research is supported by the National Key Research and Development Program of China (No.2016YFE0206800).

REFERENCES

CHOU, J. H., and SHANG, K. (2019). “Prediction of urban rail transit sudden large passenger flow based on relevant vector machine.” Journal of Dalian Jiaotong University, 40(01): 13-17.
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LI, X. Y. (2015). “Research on bus dispatching in disruption of urban rail network.” Changchun: Jilin University.
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Information & Authors

Information

Published In

Go to ICTE 2019
ICTE 2019
Pages: 956 - 964
Editors: Xiaobo Liu, Ph.D., Southwest Jiaotong University, Qiyuan Peng, Ph.D., Southwest Jiaotong University, and Kelvin C. P. Wang, Ph.D., Oklahoma State University
ISBN (Online): 978-0-7844-8274-2

History

Published online: Jan 13, 2020

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Authors

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Professor, Jiangsu Province Key Laboratory of Urban Intelligent Transportation, School of Transportation, Southeast Univ., Jiulonghu Campus, Nanjing 211189, P.R. China (corresponding author). E-mail: [email protected]
Jiangsu Province Key Laboratory of Urban Intelligent Transportation, School of Transportation, Southeast Univ., Jiulonghu Campus, Nanjing 211189, P.R. China. E-mail: [email protected]

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