A Two-Stage Algorithm Based on Variable Distance Threshold for Estimating Alighting Stops Using Smart Card Data
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 150, Issue 1
Abstract
A reasonable method for estimating alighting stops can provide a data guarantee for schedule design and vehicle scheduling. However, few studies, to our best knowledge, have addressed this problem using the variable distance threshold for different types of passengers. This paper is based on available smart card data, and a two-stage algorithm is designed to obtain missing alighting stations in the entry-only urban public transportation system. This algorithm divides passengers into seven types, determines varying distance thresholds for different passengers, and includes five assumptions. Based on the first three assumptions, the corresponding subalgorithms are designed, respectively, constituting the first-stage algorithm, and the first match for all passengers’ alighting stations is carried out. The second-stage algorithm is designed on the basis of the latter two assumptions. A secondary match is conducted on data that were not successfully matched in the first stage to ensure that alighting stops for all passengers are identified. The proposed methods are verified using urban public transport data from the city of Zhuhai as an example. Research results show that the drop-off stops where approximately 74% of passengers can be determined after matching the first-stage algorithm. The distance thresholds for various passengers are different, and the distance thresholds for the same type of passengers over different days are also different. Compared with the traditional fixed distance threshold, the variable distance threshold for various passengers proposed in this paper can improve the match rate of alighting station estimate, about 9%. By appropriately extending the distance thresholds for different passengers, the match rate for passengers who work or live in the suburbs can be further improved, which is in line with the actual travel status of such passengers. The second-stage algorithm can identify alighting sites for a single or unlinked trip. The efficiency of the methodology proposed in this study can meet the needs for practical applications, which may be applied to urban public transport systems.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
Some or all data, models, or code generated or used during the study are proprietary or confidential in nature and may only be provided with restrictions, e.g., GPS data of bus stops, bus arrival and departure, IDs of IC cards, and IDs of vehicles.
Acknowledgments
This research is funded by the Department of Education of Liaoning Province, China (20-A834).
References
Adjeroh, D., T. Bell, and A. Mukherjee. 2008. The burrows-wheeler transform: Data compression, suffix arrays, and pattern matching. Berlin: Springer.
Alexander, L., S. Jiang, M. Murga, and M. González. 2015. “Origin-destination trips by purpose and time of day inferred from mobile phone data.” Transp. Res. Part C Emerging Technol. 58 (Sep): 240–250. https://doi.org/10.1016/j.trc.2015.02.018.
Alsger, A., B. Assemi, M. Mesbah, and L. Ferreira. 2016. “Validating and improving public transport origin–destination estimation algorithm using smart card fare data.” Transp. Res. Part C Emerging Technol. 68 (Jul): 490–506. https://doi.org/10.1016/j.trc.2016.05.004.
Alsger, A., M. Mesbah, L. Ferreira, and H. Safi. 2015. “Use of smart card fare data to estimate public transport origin–destination matrix.” Transp. Res. Rec. 2535 (1): 88–96. https://doi.org/10.3141/2535-10.
Assemi, B., A. Alsger, M. Moghaddam, M. Hickman, and M. Mesbah. 2020. “Improving alighting stop inference accuracy in the trip chaining method using neural networks.” Public Transp. 12 (1): 89–121. https://doi.org/10.1007/s12469-019-00218-9.
Barry, J., R. Newhouser, A. Rahbee, and S. Sayeda. 2002. “Origin and destination estimation in New York city with automated fare system data.” Transp. Res. Rec. 1817 (1): 183–187. https://doi.org/10.3141/1817-24.
Cai, H. X., S. R. Kulkarni, and S. Verdú. 2004. “Universal entropy estimation via block sorting.” IEEE Trans. Inf. Theory 50 (7): 1551–1561. https://doi.org/10.1109/TIT.2004.830771.
Cheng, Z. H., M. Trépanier, and L. J. Sun. 2021. “Probabilistic model for destination inference and travel pattern mining from smart card data.” Transport 48 (4): 2035–2053. https://doi.org/10.1007/s11116-020-10120-0.
Cong, J. M., L. J. Gao, and Z. C. Juan. 2019. “Improved algorithms for trip-chain estimation using massive student behavior data from urban transit systems.” IET Intell. Transp. Syst. 13 (3): 435–442. https://doi.org/10.1049/iet-its.2018.5183.
Cui, H. J., R. Zhao, M. Q. Zhu, and X. Li. 2020. “A method for measuring repeatability of bus passenger travel based on entropy rate.” J. High Transp. Res. Dev. 37 (9): 97–103. https://doi.org/10.3969/j.issn.1002-0268.2020.09.013.
Cui, Z. W., C. Wang, Y. Gao, D. K. Yang, W. Wei, J. W. Chen, and T. He. 2021. “Alighting stop determination of unlinked trips based on a two-layer stacking framework.” Math. Probl. Eng. 2 (2): 1–15. https://doi.org/10.1155/2021/6464980.
Deng, H. X., Z. H. Zhao, and W. Q. Wang. 2019. “Research on bus OD estimation based on bus IC card and GPS data.” J. Chongqing Univ. Tech. 33 (6): 220–226. https://doi.org/10.3969/j.issn.1674-8425(z).2019.06.033.
Gao, Y., I. Kontoyiannis, and E. Bienenstock. 2008. “Estimating the entropy of binary time series: Methodology, some theory and a simulation study.” Entropy 10 (2): 71–99. https://doi.org/10.3390/entropy-e10020071.
He, L., and M. Trépanier. 2015. “Estimating the destination of unlinked trips in transit smart card fare data.” Transp. Res. Rec. 2535 (1): 97–104. https://doi.org/10.3141/2535-11.
Hussain, E., A. Bhaskar, and E. Chung. 2021. “Transit OD matrix estimation using smartcard data: Recent developments and future research challenges.” Transp. Res. Part C Emerging Technol. 125 (Apr): 103044. https://doi.org/10.1016/j.trc.2021.103044.
Lee, I., S. H. Cho, K. Kim, S. Y. Kho, and D. K. Kim. 2022. “Travel pattern-based bus trip origin-destination estimation using smart card data.” PLoS One 17 (6): e0270346. https://doi.org/10.1371/journal.pone.0270346.
Lei, D., X. W. Chen, L. Cheng, L. Zhang, P. F. Wang, and K. L. Wang. 2021. “Minimum entropy rate-improved trip-chain method for origin–destination estimation using smart card data.” Transp. Res. Part C Emerging Technol. 130 (Sep): 103307. https://doi.org/10.1016/j.trc.2021.103307.
Li, H. B., X. W. Chen, and Z. R. Chen. 2015. “A method for estimating origin-destination matrix of public transit based on smart card and AVL data.” J. Trans. Inf. Saf. 33 (6): 33–39. https://doi.org/10.3963/j.issn1674-4861.2015.06.005.
Li, T., D. Z. Sun, P. Jing, and K. X. Yang. 2018. “Smart card data mining of public transport destination: A literature review.” Information 9 (1): 18. https://doi.org/10.3390/info9010018.
Liu, W. S., X. D. Zhou, and K. Kuang. 2018. “The method of deriving passenger flow of bus alighting stops based on smart card data and interval uncertainty.” J. Rail Sci. Eng. 15 (11): 2988–2994. https://doi.org/10.19713/j.cnki.43-1423/u.2018.11.032.
Liu, X., L. Liu, and J. Zou. 2021. “Spatial and temporal analysis of urban commuting based on smart bus card data.” Appl. Soft Comput. 12 (5): 69–74. https://doi.org/10.1016/j.asoc.2012.01.016/j.issn.1000-386x.2021.05.011.
Munizaga, M. A., and C. Palma. 2012. “Estimation of a disaggregate multimodal public transport origin–destination matrix from passive smartcard data from Santiago, Chile.” Transp. Res. Part C Emerging Technol. 24 (Oct): 9–18. https://doi.org/10.1016/j.trc.2012.01.007.
Pelletier, M. P., M. Trépanier, and C. Morency. 2011. “Smart card data use in public transit: A literature review.” Transp. Res. Part C Emerging Technol. 19 (4): 557–568. https://doi.org/10.1016/j.trc.2010.12.003.
Yan, F., C. Yang, and S. V. Ukkusuri. 2019. “Alighting stop determination using two-step algorithms in bus transit systems.” Trans. A: Trans. Sci. 15 (2): 1522–1542. https://doi.org/10.1080/23249935.2019.1615578.
Yap, M., and O. Cats. 2021. “Predicting disruptions and their passenger delay impacts for public transport stops.” Transport 48 (4): 1703–1731. https://doi.org/10.1007/s11116-020-10109-9.
Yap, M., O. Cats, and B. van Arem. 2020. “Crowding valuation in urban tram and bus transportation based on smart card data.” Trans. A: Trans. Sci. 16 (1): 23–42. https://doi.org/10.1080/23249935.2018.1537319.
Zhang, W. S., M. Lu, J. J. Zhu, T. Yan, and Z. N. Duan. 2021. “OD calculation of bus passenger flow based on IC card and AVL data.” Appl. Soft Comput. 38 (7): 100–105. https://doi.org/10.3969/j.issn.1000-386x.2021.07.017.
Information & Authors
Information
Published In
Journal of Transportation Engineering, Part A: Systems
Volume 150 • Issue 1 • January 2024
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Oct 6, 2022
Accepted: Aug 28, 2023
Published online: Oct 20, 2023
Published in print: Jan 1, 2024
Discussion open until: Mar 20, 2024
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.