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First published online January 1, 2017

Investigating Potential Transit Ridership by Fusing Smartcard and Global System for Mobile Communications Data

Karin de Regt, Oded Cats [email protected], Niels Van Oort, and Hans van LintView all authors and affiliations
Volume 2652, Issue 1
https://doi.org/10.3141/2652-06

Abstract

The public transport industry faces challenges in catering to the variety of mobility patterns and corresponding needs and preferences of passengers. Travel habit surveys provide information on overall travel demand as well as its spatial variation. However, that information often does not include information on temporal variations. By applying data fusion to smartcard and Global System for Mobile Communications (GSM) data, researchers were able to examine spatial and temporal patterns of public transport usage versus overall travel demand. The analysis was performed by contrasting different spatial and temporal levels of smartcard and GSM data. The methodology was applied to a case study in Rotterdam, Netherlands, to analyze whether the current service span is adequate. The results suggested that there is potential demand for extending public transport service on both ends. In the early mornings, right before transit operations are resumed, an hourly increase in visitor occupancy of 33% to 88% was observed in several zones, showing potential demand for additional public transport services. The proposed data fusion method was shown to be valuable in supporting tactical transit planning and decision making and can easily be applied to other origin-destination transport data.

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References

1. Guedes M. C. M., Oliveira N., Santiago S., and Smirnov G. On the Evaluation of a Public Transportation Network Quality: Criteria Validation Methodology. Research in Transportation Economics, Vol. 36, No. 1, 2012, pp. 39–44. https://doi.org/10.1016/j.retrec.2012.03.013.
Google Scholar
2. Cats O., Wang Q., and Zhao Y. Identification and Classification of Public Transport Activity Centers in Stockholm Using Passenger Flows Data. Journal of Transport Geography, Vol. 48, 2015, pp. 10–22. https://doi.org/10.1016/j.jtrangeo.2015.08.005.
Google Scholar
3. Gutierrez J., and Garcia-Palomares J. C. New Spatial Patterns of Mobility Within the Metropolitan Area of Madrid: Toward More Complex and Dispersed Flow Networks. Journal of Transport Geography, Vol. 15, No. 1, 2007, pp. 18–30. https://doi.org/10.1016/j.jtrangeo.2006.01.002.
Google Scholar
4. Pelletier M. P., Trepanier M., and Morency C. Smart Card Data Use in Public Transit: A Literature Review. Transportation Research Part C: Emerging Technologies, Vol. 19, No. 4, 2011, pp. 557–568. https://doi.org/10.1016/j.trc.2010.12.003.
Google Scholar
5. Elias D., Nadler F., Stehno J., Krosche J., and Lindorfer M. SOMOBIL—Improving Public Transport Planning Through Mobile Phone Data Analysis. Transportation Research Procedia, Vol. 14, 2016, pp. 4478–4485. https://doi.org/10.1016/j.trpro.2016.05.370.
Google Scholar
6. Durand C. P., Tang X., Gabriel K. P., Sener I. N., Oluyomi A. O., Knell G., Porter A. K., Hoelscher D. M., and Kohl H. W. III. The Association of Trip Distance with Walking to Reach Public Transit: Data from the California Household Travel Survey. Journal of Transport and Health, Vol. 3, No. 2, 2016, pp. 154–160. https://doi.org/10.1016/j.jth.2015.08.007.
PubMed
Google Scholar
7. Long Y., and Thill J. C. Combining Smart Card Data and Household Travel Survey to Analyze Jobs–Housing Relationships in Beijing. Computers, Environment and Urban Systems, Vol. 53, 2015, pp. 19–35. https://doi.org/10.1016/j.compenvurbsys.2015.02.005.
Google Scholar
8. Del Castillo J. M., and Benitez F. G. A Methodology for Modeling and Identifying Users Satisfaction Issues in Public Transport Systems Based on Users Surveys. Procedia: Social and Behavioral Sciences, Vol. 54, 2012, pp. 1104–1114. https://doi.org/10.1016/j.sbspro.2012.09.825.
Google Scholar
9. Frias-Martinez V., Soguero C., and Frias-Martinez E. Estimation of Urban Commuting Patterns Using Cellphone Network Data. In Proceedings of 6th Transport Research Arena, April 18–21, 2016, Warsaw, Poland.
Google Scholar
10. Van Oort N., Sparing D., Brands T. and Goverde R. M. P. Data-Driven Improvements in Public Transport: The Dutch Example. Public Transport, Vol. 7, No. 33, 2015, pp. 369–389. https://doi.org/10.1145/2346496.2346499.
Google Scholar
11. Van der Mede P. Over Het Meten van Mobiliteit met GSM-data: Mogelijkheden en Onmogelijkheden. Contribution for Colloquium Vervoersplanologisch Speurwerk, Eindhoven, Netherlands, 2014 (in Dutch).
Google Scholar
12. Aguilera V., Allio S., Benezech V., Combes F., and Milion C. Using Cell Phone Data to Measure Quality of Service and Passenger Flows of Paris Transit System. Transportation Research Part C: Emerging Technologies, Vol. 43, No. 2, 2014, pp. 198–211. https://doi.org/10.1016/j.trc.2013.11.007.
Google Scholar
13. Calabrese F., Diao M., Di Lorenzo G., Ferreira J. Jr., and Ratti C. Understanding Individual Mobility Patterns from Urban Sensing Data: A Mobile Phone Trace Example. Transportation Research Part C: Emerging Technologies, Vol. 26, 2013, pp. 301–313. https://doi.org/10.1016/j.trc.2012.09.009.
Google Scholar
14. Iqbal M. S., Choudhury C. F., Wang P., and Gonzalez M. C. Development of Origin-Destination Matrices Using Mobile Phone Call Data. Transportation Research Part C: Emerging Technologies, Vol. 40, 2014, pp. 63–74. https://doi.org/10.1016/j.trc.2014.01.002.
Google Scholar
15. Kusakabe T., and Asakura Y. Behavioral Data Mining of Transit Smart Card Data: A Data Fusion Approach. Transportation Research Part C: Emerging Technologies, Vol. 46, 2014, pp. 179–191. https://doi.org/10.1016/j.trc.2014.05.012.
Google Scholar
16. Elfrink M., Courtz M., Metz S., Ebben M., and Weppner J. OV-Potentie Opsporen Door Datafusie. Nationaal Verkeerskundecongres, 2015 (in Dutch).
Google Scholar
17. Holleczek T., Yu L., Lee J. K., Senn O., Ratti C., and Jaillet P. Detecting Weak Public Transport Connections from Cellphone and Public Transport Data. In Proceedings of the 2014 International Conference on Big Data Science and Computing (p. 9). Association for Computing Machinery, 2014.
Crossref
Google Scholar
18. Duff-Riddell W. R., and Bester C. J. Network Modeling Approach to Transit Network Design. Journal of Urban Planning and Development, Vol. 131, No. 2, 2005, pp. 87–97. https://doi.org/10.1061/(ASCE)0733-9488(2005)131:2(87).
Google Scholar
19. de Regt K. L., How Do Spatial and Temporal Patterns of Public Transport Relate to the Overall Travel Demand? A Data Fusion Method for Smart Card Data and GSM Data. MS thesis. Delft University of Technology, 2016.
Google Scholar
20. ViewDAT. ViewDAT. http://view.dat.nl/viewdat/. Accessed October 2015.
Google Scholar
21. Alexander L., Jiang S., Murga M., and Gonzalez M. C. Origin–Destination trips by Purpose and Time of Day, Inferred from Mobile Phone Data. Transportation Research Part C: Emerging Technologies, Vol. 58, 2015, pp. 240–250. https://doi.org/10.1016/j.trc.2015.02.018.
Google Scholar
22. Liu L., Hou A., Biderman A., Ratti C., and Chen J. Understanding Individual and Collective Mobility Patterns from Smart Card Records: A Case Study in Shenzhen. In Proceedings of the 12th International IEEE Conference on Intelligent Transportation Systems, St. Louis, Mo., October 3–7, 2009, pp. 1–6. https://doi.org/10.1109/ITSC.2009.5309662.
Google Scholar
23. Nishiuchi H., King J., and Todoroki T. Spatial-Temporal Daily Frequent Trip Pattern of Public Transport Passengers Using Smart Card Data. International Journal of Intelligent Transportation Systems Research, Vol. 11, No. 1, 2013, pp. 1–10. https://doi.org/10.1007/s13177-012-0051-7.
Google Scholar
24. RET. Over RET, http://corporate.ret.nl (In Dutch). Accessed July 2016.
Google Scholar

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Published In

Volume 2652, Issue 1
Pages: 50 - 58
Article first published online: January 1, 2017
Issue published: January 2017

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© 2017 National Academy of Sciences.
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Authors

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Karin de Regt
Transport, Infrastructure, and Logistics, Faculty of Civil Engineering and Geosciences, Delft University of Technology, P.O. Box 5048, 2600 GA Delft, Netherlands
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Oded Cats
Transport and Planning, Faculty of Civil Engineering and Geosciences, Delft University of Technology, P.O. Box 5048, 2600 GA Delft, Netherlands
[email protected]
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Niels Van Oort
Transport and Planning, Faculty of Civil Engineering and Geosciences, Delft University of Technology, P.O. Box 5048, 2600 GA Delft, Netherlands
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Hans van Lint
Transport and Planning, Faculty of Civil Engineering and Geosciences, Delft University of Technology, P.O. Box 5048, 2600 GA Delft, Netherlands
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