Advertisement

Transportation

, Volume 44, Issue 6, pp 1535–1554 | Cite as

Isolating high-priority metro and feeder bus transfers using smart card data

  • De Zhao
  • Wei Wang
  • Amber Woodburn
  • Megan S. Ryerson
Article
  • 480 Downloads

Abstract

Fixed-rail metro (or ‘subway’) infrastructure is generally unable to provide access to all parts of the city grid. Consequently, feeder bus lines are an integral component of urban mass transit systems. While passengers prefer a seamless transfer between these two distinct transportation services, each service’s operations are subject to a different set of factors that contribute to metro-bus transfer delay. Previous attempts to understand transfer delay were limited by the availability of tools to measure the time and cost associated with passengers’ transfer experience. This paper uses data from smart card systems, an emerging technology that automatically collects passenger trip data, to understand transfer delay. The primary objective of this study is to use smart card data to derive a reproducible methodology that isolates high priority transfer points between the metro system and its feeder-bus systems. The paper outlines a methodology to identify transfer transactions in the smart card dataset, estimate bus headways without the aid of geographic location information, estimate three components of the total transfer time (walking time, waiting time, and delay time), and isolate high-priority transfer pairs. The paper uses smart card data from Nanjing, China as a case study. The results isolate eight high priority metro-bus transfer pairs in the Nanjing metro system and finally, offers several targeted measures to improve transfer efficiency.

Keywords

Smart card Public transit China Transfer 

Notes

Acknowledgments

This research is supported by Fundamental Research Funds for the Central Universities (CXZZ13_0118), the Scientific Research Foundation of Graduate School of Southeast University (YBJJ1457), the key project of National Natural Science Foundation of China (51338003), and Projects of International Cooperation and Exchange of the National Natural Science Foundation of China (5151101143).

GLOSSARY

Metro-bus transfer transaction

A transfer transaction is a smart card transaction that occurred during the process of a metro-bus transfer. This may be either a metro transaction or a bus transaction.

Metro-bus transfer trip

One transfer trip is comprised of one metro transaction and one subsequent bus transaction. Two smart card transactions are required to identify one metro-bus transfer trip. The transfer trips are identified according to the two ‘recognition’ rules outlined in the methodology section.

Metro-bus transfer pair

One transfer pair is comprised of one metro station ID and one bus line ID.

References

  1. Bagchi, M., White, P.R.: The potential of public transport smart card data. Transp. Policy 12, 464–474 (2005). doi: 10.1016/j.tranpol.2005.06.008 CrossRefGoogle Scholar
  2. Barry, J., Newhouser, R., Rahbee, A., Sayeda, S.: Origin and destination estimation in new york city with automated fare system data transportation research record. J. Transp. Res. Board. 1817, 183–187 (2002)CrossRefGoogle Scholar
  3. Chowdhury, S., Ceder, A.: A psychological investigation on public-transport users’ intention to use routes with transfers. Int. J. Transp. 1, 1–20 (2013). doi: 10.14257/ijt.2013.1.1.01 CrossRefGoogle Scholar
  4. Devillaine, F., Munizaga, M., Trépanier, M.: Detection of activities of public transport users by analyzing smart card data transportation research record. J. Transp. Res. Board. 2276, 48–55 (2012). doi: 10.3141/2276-06 CrossRefGoogle Scholar
  5. Diab, E.I., El-Geneidy, A.M.: Understanding the impacts of a combination of service improvement strategies on bus running time and passenger’s perception. Transp. Res. Part. 46, 614–625 (2012). doi: 10.1016/j.tra.2011.11.013 Google Scholar
  6. Ferrari, L., Berlingerio, M., Calabrese, F., Reades, J.: Improving the accessibility of urban transportation networks for people with disabilities. Transp. Res. Part. 45, 27–40 (2014). doi: 10.1016/j.trc.2013.10.005 CrossRefGoogle Scholar
  7. Guo, Z., Wilson, N.H.M.: Assessing the cost of transfer inconvenience in public transport systems: a case study of the London Underground. Transp Res. Part. 45, 91–104 (2011). doi: 10.1016/j.tra.2010.11.002 Google Scholar
  8. Jang, W.: Travel time and transfer analysis using transit smart card data transportation research record. J.Transp. Res. Board. 2144, 142–149 (2010). doi: 10.3141/2144-16 CrossRefGoogle Scholar
  9. Kusakabe, T., Asakura, Y.: Behavioural data mining of transit smart card data: a data fusion approach. Transp. Res. Part. 46, 179–191 (2014). doi: 10.1016/j.trc.2014.05.012 CrossRefGoogle Scholar
  10. Kusakabe, T., Iryo, T., Asakura, Y.: Estimation method for railway passengers’ train choice behavior with smart card transaction data. Transportation 37, 731–749 (2010). doi: 10.1007/s11116-010-9290-0 CrossRefGoogle Scholar
  11. Ma, X., Wang, Y.: Development of a data-driven platform for transit performance measures using smart card and GPS data. J. Transp. Eng. 140, 04014063 (2014). doi: 10.1061/(asce)te.1943-5436.0000714 CrossRefGoogle Scholar
  12. Ma, Z., Xing, J., Mesbah, M., Ferreira, L.: Predicting short-term bus passenger demand using a pattern hybrid approach. Transp. Res. Part. 39, 148–163 (2014). doi: 10.1016/j.trc.2013.12.008 CrossRefGoogle Scholar
  13. Morency, C., Trépanier, M., Agard, B.: Measuring transit use variability with smart-card data. Transp. Policy 14, 193–203 (2007). doi: 10.1016/j.tranpol.2007.01.001 CrossRefGoogle Scholar
  14. Munizaga, M., Devillaine, F., Navarrete, C., Silva, D.: Validating travel behavior estimated from smartcard data. Transp. Res. Part. 44, 70–79 (2014). doi: 10.1016/j.trc.2014.03.008 CrossRefGoogle Scholar
  15. Munizaga, M.A., Palma, C.: Estimation of a disaggregate multimodal public transport Origin-Destination matrix from passive smartcard data from Santiago. Chile. Transp. Res. Par. 24, 9–18 (2012). doi: 10.1016/j.trc.2012.01.007 CrossRefGoogle Scholar
  16. Nanjing Planning Bureau: Nanjing Transport Annual Report. Nanjing (2014)Google Scholar
  17. Osuna, E.E., Newell, G.F.: Central strategies for an idealized public transport system. Transp. Sci. 6, 52–72 (1972)CrossRefGoogle Scholar
  18. Pelletier, M.-P., Trépanier, M., Morency, C.: Smart card data use in public transit: a literature review. Transp. Res. Part. 19, 557–568 (2011). doi: 10.1016/j.trc.2010.12.003 CrossRefGoogle Scholar
  19. Robinson, S., Narayanan, B., Toh, N., Pereira, F.: Methods for pre-processing smartcard data to improve data quality. Transp Res. Part. 49, 43–58 (2014). doi: 10.1016/j.trc.2014.10.006 CrossRefGoogle Scholar
  20. Schmöcker, J.-D., Shimamoto, H., Kurauchi, F.: Generation and calibration of transit hyperpaths. Transp Res. Part. 36, 406–418 (2013). doi: 10.1016/j.trc.2013.06.014 CrossRefGoogle Scholar
  21. Seaborn, C., Attanucci, J., Wilson, N.: Analyzing multimodal public transport journeys in london with smart card fare payment data. Transp. Res. Rec. 2121, 55–62 (2009). doi: 10.3141/2121-06 CrossRefGoogle Scholar
  22. Si, B., Zhong, M., Liu, J., Gao, Z., Wu, J.: Development of a transfer-cost-based logit assignment model for the Beijing rail transit network using automated fare collection data. J. Advan. Transp. 47, 297–318 (2013). doi: 10.1002/atr.1203 CrossRefGoogle Scholar
  23. Sun, L., Jin, J., Lee, D.H., Axhausen, K.W.: (2015a) Characterizing Multimodal Transfer Time Using Smart Card Data: the Effect of Time, Passenger Age, Crowdedness and Collective Pressure. In: Paper presented at the Transportation Research Board, Washington DCGoogle Scholar
  24. Sun, L., Jin, J.G., Lee, D.-H., Axhausen, K.W., Erath, A.: Demand-driven timetable design for metro services. Transp. Res. Part. 46, 284–299 (2014a). doi: 10.1016/j.trc.2014.06.003 CrossRefGoogle Scholar
  25. Sun, L., Lu, Y., Jin, J.G., Lee, D.-H., Axhausen, K.W.: An integrated Bayesian approach for passenger flow assignment in metro networks. Transp. Res. 52, 116–131 (2015b). doi: 10.1016/j.trc.2015.01.001 Google Scholar
  26. Sun, L., Tirachini, A., Axhausen, K.W., Erath, A., Lee, D.-H.: Models of bus boarding and alighting dynamics. Transp. Res. Part. 69, 447–460 (2014b). doi: 10.1016/j.tra.2014.09.007 Google Scholar
  27. Trépanier, M., Habib, K.M.N., Morency, C.: Are transit users loyal? revelations from a hazard model based on smart card data. Can. J. Civ. Eng. 39, 610–618 (2012). doi: 10.1139/l2012-048 CrossRefGoogle Scholar
  28. Trépanier, M., Morency, C., Agard, B.: Calculation of transit performance measures using smartcard data. J. Public. Transp. 12, 79–96 (2009)CrossRefGoogle Scholar
  29. Welde, M.: Are smart card ticketing systems profitable—evidence from the city of Trondheim. J. Public Transp. 15, 133–148 (2012)CrossRefGoogle Scholar
  30. Zhao, J., Frumin, M., Wilson, N., Zhao, Z.: Unified estimator for excess journey time under heterogeneous passenger incidence behavior using smartcard data. Transp. Res. Part. C 34, 70–88 (2013). doi: 10.1016/j.trc.2013.05.009 CrossRefGoogle Scholar
  31. Zhao, J., Rahbee, A., Wilson, N.: Estimating a rail passenger trip origin-destination matrix using automatic data collection systems. Comput. Aided. Civ. Infrastruct. Eng. 22, 376–387 (2007)CrossRefGoogle Scholar
  32. Zhou, J., Murphy, E., Long, Y.: Commuting efficiency in the Beijing metropolitan area: an exploration combining smartcard and travel survey data. J. Transp. Geogr. 41, 175–183 (2014). doi: 10.1016/j.jtrangeo.2014.09.006 CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Jiangsu Key Laboratory of Urban ITSSoutheast UniversityNanjingChina
  2. 2.Department of City and Regional PlanningUniversity of PennsylvaniaPhiladelphiaUSA
  3. 3.School of TransportationSoutheast UniversityNanjingChina
  4. 4.Department of Electrical and Systems EngineeringUniversity of PennsylvaniaPhiladelphiaUSA

Personalised recommendations