Verification and Optimization of Metro Fare Clearing Models Based on Travel Route Reconstruction

Authors

  • Pu Yichao School of Electronics and Information Engineering, Tongji University

DOI:

https://doi.org/10.37819/nm.001.01.0076

Keywords:

Fare clearing model, Cell phone data, Automatic fare collection, Travel route reconstruction

Abstract

How to verify and optimize metro fare clearing models efficiently and accurately is a research focus in metro operations. Metro fare clearing models are mostly based on probability distributions. In such models, the normal distribution of travel time corresponding to the section probabilities is used to calculate the route choice probabilities of passengers on a multi-route metro network. By integrating the operating mileage proportions of each metro line operator and the corresponding route choice probabilities, the fare clearing proportions are calculated for all the operators of the metro network. To verify the accuracy of the fare clearing proportions, we propose a travel route reconstruction approach based on cell phone data acquisition technique. With wireless access point (AP) sensors installed at transfer stations, the unique medium access control (MAC) address of the smart phone with Wi-Fi function turned on is recorded and transmitted to a data analysis platform. After matching the MAC address information with time and location, the travel route of the smart phone user is reconstructed. Then, the parameters in the fare clearing model are verified and optimized according to the travel route choice probabilities. The proposed methodology is applied in Hangzhou metro network for experiment, and the metro fare clearing model is verified and modified by reconstructing the actual travel routs of the local passengers.

References

Aguiléra, V., Allio, S., Benezech, V., Combes, F., & Milion, C. (2014). Using cell phone data to measure quality of service and passenger flows of Paris transit system. Transportation Research Part C: Emerging Technologies, 43, 198-211. https://doi.org/10.1016/j.trc.2013.11.007

Alexander, L., Jiang, S., Murga, M., & González, M. C. (2015). Origin–destination trips by purpose and time of day inferred from mobile phone data. Transportation research part c: emerging technologies, 58, 240-250. https://doi.org/10.1016/j.trc.2015.02.018

Chang, Z. (2013). Public–private partnerships in China: A case of the Beijing No. 4 Metro line. Transport Policy, 30, 153-160. https://doi.org/10.1016/j.tranpol.2013.09.011

de Palma, A., & Picard, N. (2005). Route choice decision under travel time uncertainty. Transportation Research Part A: Policy and Practice, 39(4), 295-324. https://doi.org/10.1016/j.tra.2004.10.001

Dong, H., Wu, M., Ding, X., Chu, L., Jia, L., Qin, Y., & Zhou, X. (2015). Traffic zone division based on big data from mobile phone base stations. Transportation Research Part C: Emerging Technologies, 58, 278-291. https://doi.org/10.1016/j.trc.2015.06.007

Gao, L., Wang, B., & Zhang, C. (2011). Comparison of urban rail transit fare clearing model based on travel survey. Modern urban transit, 11, 97-99.

Gordon, C., Mulley, C., Stevens, N., & Daniels, R. (2013). Public–private contracting and incentives for public transport: Can anything be learned from the Sydney Metro experience?. Transport Policy, 27, 73-84. https://doi.org/10.1016/j.tranpol.2013.01.009

Hoffman, W., & Pavley, R. (1959). A Method for the Solution of the N th Best Path Problem. Journal of the ACM (JACM), 6(4), 506-514. https://doi.org/10.1145/320998.321004

Iqbal, M. S., Choudhury, C. F., Wang, P., & González, M. C. (2014). Development of origin–destination matrices using mobile phone call data. Transportation Research Part C: Emerging Technologies, 40, 63-74. https://doi.org/10.1016/j.trc.2014.01.002

Lu, S. (2012). Analysis of urban rail transit fare clearing algorithm in shenzhen. Southwest Jiaotong University.

Pan, X. (2014). Research on income distribution model of urban rail transit under the network condition. Southwest Jiaotong University.

Phang, S. Y. (2007). Urban rail transit PPPs: Survey and risk assessment of recent strategies. Transport Policy, 14(3), 214-231. https://doi.org/10.1016/j.tranpol.2007.02.001

Raveau, S., Guo, Z., Muñoz, J. C., & Wilson, N. H. (2014). A behavioural comparison of route choice on metro networks: Time, transfers, crowding, topology and socio-demographics. Transportation Research Part A: Policy and Practice, 66, 185-195. https://doi.org/10.1016/j.tra.2014.05.010

Sun, L., Lu, Y., Jin, J. G., Lee, D. H., & Axhausen, K. W. (2015). An integrated Bayesian approach for passenger flow assignment in metro networks. Transportation Research Part C: Emerging Technologies, 52, 116-131. https://doi.org/10.1016/j.trc.2015.01.001

Wang, Z., Xu Y, Zhu D. (2013). Discussion on the multi-route clearing of urban rail transit and its influencing factors. Communication & Audio and Video, 2,17-22.

Yu, J., & Wang, Z. (2013). On ACC clearing model based on travel matching time. Urban Mass Transit, 1, 43-90.

Zhao, F., Zhang, X. C., & Liu, Z. L. (2007). Modelling income distribution of the auto fare collection system. Journal of Transportation Systems Engineering and Information Technology, 7(6), 85-90.

Zhou, L. (2014). Verification and optimization of network clearing model. Urban mass transit, 11, 59-66.

Downloads

Published

2020-12-02

How to Cite

Yichao, P. . (2020). Verification and Optimization of Metro Fare Clearing Models Based on Travel Route Reconstruction. New Metro, 1(1), 34-47. https://doi.org/10.37819/nm.001.01.0076

Issue

Vol. 1 No. 1 (2020): New Metro

Section

Operation Management

License

Copyright (c) 2020 Pu Yichao

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.