IDEAS home Printed from https://ideas.repec.org/a/taf/transp/v39y2016i2p180-194.html
   My bibliography  Save this article

Comparison of advanced imputation algorithms for detection of transportation mode and activity episode using GPS data

Author

Listed:
  • Tao Feng
  • Harry J.P. Timmermans

Abstract

Global Positioning System (GPS) technologies have been increasingly considered as an alternative to traditional travel survey methods to collect activity-travel data. Algorithms applied to extract activity-travel patterns vary from informal ad-hoc decision rules to advanced machine learning methods and have different accuracy. This paper systematically compares the relative performance of different algorithms for the detection of transportation modes and activity episodes. In particular, naive Bayesian, Bayesian network, logistic regression, multilayer perceptron, support vector machine, decision table, and C4.5 algorithms are selected and compared for the same data according to their overall error rates and hit ratios. Results show that the Bayesian network has a better performance than the other algorithms in terms of the percentage correctly identified instances and Kappa values for both the training data and test data, in the sense that the Bayesian network is relatively efficient and generalizable in the context of GPS data imputation.

Suggested Citation

  • Tao Feng & Harry J.P. Timmermans, 2016. "Comparison of advanced imputation algorithms for detection of transportation mode and activity episode using GPS data," Transportation Planning and Technology, Taylor & Francis Journals, vol. 39(2), pages 180-194, March.
    • Handle: RePEc:taf:transp:v:39:y:2016:i:2:p:180-194
    DOI: 10.1080/03081060.2015.1127540
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/03081060.2015.1127540
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/03081060.2015.1127540?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Du, Jianhe & Aultman-Hall, Lisa, 2007. "Increasing the accuracy of trip rate information from passive multi-day GPS travel datasets: Automatic trip end identification issues," Transportation Research Part A: Policy and Practice, Elsevier, vol. 41(3), pages 220-232, March.
    2. Eui-Hwan Chung & Amer Shalaby, 2005. "A Trip Reconstruction Tool for GPS-based Personal Travel Surveys," Transportation Planning and Technology, Taylor & Francis Journals, vol. 28(5), pages 381-401, August.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Broach, Joseph & Dill, Jennifer & McNeil, Nathan Winslow, 2019. "Travel mode imputation using GPS and accelerometer data from a multi-day travel survey," Journal of Transport Geography, Elsevier, vol. 78(C), pages 194-204.
    2. Yanjun Qin & Haiyong Luo & Fang Zhao & Zhongliang Zhao & Mengling Jiang, 2018. "A traffic pattern detection algorithm based on multimodal sensing," International Journal of Distributed Sensor Networks, , vol. 14(10), pages 15501477188, October.
    3. Hong, Ye & Stüdeli, Emanuel & Raubal, Martin, 2023. "Evaluating geospatial context information for travel mode detection," Journal of Transport Geography, Elsevier, vol. 113(C).
    4. Wu, Jishi & Feng, Tao & Jia, Peng & Li, Gen, 2024. "Spatial allocation of heavy commercial vehicles parking areas through geo-fencing," Journal of Transport Geography, Elsevier, vol. 117(C).
    5. Seo, Toru & Kusakabe, Takahiko & Gotoh, Hiroto & Asakura, Yasuo, 2019. "Interactive online machine learning approach for activity-travel survey," Transportation Research Part B: Methodological, Elsevier, vol. 123(C), pages 362-373.
    6. Thomas Feilhauer & Florian Braun & Katja Faller & David Hutter & Daniel Mathis & Johannes Neubauer & Jasmin Pogatschneg & Michelle Weber, 2021. "Mobility Choices—An Instrument for Precise Automatized Travel Behavior Detection & Analysis," Sustainability, MDPI, vol. 13(4), pages 1-23, February.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chen, Cynthia & Gong, Hongmian & Lawson, Catherine & Bialostozky, Evan, 2010. "Evaluating the feasibility of a passive travel survey collection in a complex urban environment: Lessons learned from the New York City case study," Transportation Research Part A: Policy and Practice, Elsevier, vol. 44(10), pages 830-840, December.
    2. Antonio Comi & Antonio Polimeni, 2022. "Estimating Path Choice Models through Floating Car Data," Forecasting, MDPI, vol. 4(2), pages 1-13, June.
    3. Ron Dalumpines & Darren M. Scott, 2017. "Making mode detection transferable: extracting activity and travel episodes from GPS data using the multinomial logit model and Python," Transportation Planning and Technology, Taylor & Francis Journals, vol. 40(5), pages 523-539, July.
    4. Gingerich, Kevin & Maoh, Hanna & Anderson, William, 2016. "Expansion of a GPS Truck Trip Sample to Remove Bias and Obtain Representative Flows for Ontario," 57th Transportation Research Forum (51st CTRF) Joint Conference, Toronto, Ontario, May 1-4, 2016 319310, Transportation Research Forum.
    5. Mofeng Yang & Yixuan Pan & Aref Darzi & Sepehr Ghader & Chenfeng Xiong & Lei Zhang, 2022. "A data-driven travel mode share estimation framework based on mobile device location data," Transportation, Springer, vol. 49(5), pages 1339-1383, October.
    6. Nadine Rieser-Schüssler & Kay W. Axhausen, 2014. "Self-tracing and reporting: state of the art in the capture of revealed behaviour," Chapters, in: Stephane Hess & Andrew Daly (ed.), Handbook of Choice Modelling, chapter 6, pages 131-151, Edward Elgar Publishing.
    7. Menghini, G. & Carrasco, N. & Schüssler, N. & Axhausen, K.W., 2010. "Route choice of cyclists in Zurich," Transportation Research Part A: Policy and Practice, Elsevier, vol. 44(9), pages 754-765, November.
    8. Muhammad Shafique & Eiji Hato, 2015. "Use of acceleration data for transportation mode prediction," Transportation, Springer, vol. 42(1), pages 163-188, January.
    9. Moiseeva, Anastasia & Timmermans, Harry, 2010. "Imputing relevant information from multi-day GPS tracers for retail planning and management using data fusion and context-sensitive learning," Journal of Retailing and Consumer Services, Elsevier, vol. 17(3), pages 189-199.
    10. Papinski, Dominik & Scott, Darren M., 2011. "A GIS-based toolkit for route choice analysis," Journal of Transport Geography, Elsevier, vol. 19(3), pages 434-442.
    11. Morency, Catherine & Trépanier, Martin & Agard, Bruno, 2007. "Measuring transit use variability with smart-card data," Transport Policy, Elsevier, vol. 14(3), pages 193-203, May.
    12. Yun Xiang & Chengcheng Xu & Weijie Yu & Shuyi Wang & Xuedong Hua & Wei Wang, 2019. "Investigating Dominant Trip Distance for Intercity Passenger Transport Mode Using Large-Scale Location-Based Service Data," Sustainability, MDPI, vol. 11(19), pages 1-17, September.
    13. Rajat Verma & Eunhan Ka & Satish V. Ukkusuri, 2024. "Long-term forecasts of statewide travel demand patterns using large-scale mobile phone GPS data: A case study of Indiana," Papers 2404.13211, arXiv.org.
    14. Yijing Lu & Lei Zhang, 2015. "Imputing trip purposes for long-distance travel," Transportation, Springer, vol. 42(4), pages 581-595, July.
    15. Reinau, Kristian Hegner & Harder, Henrik & Weber, Michael, 2015. "The SMS–GPS-Trip method: A new method for collecting trip information in travel behavior research," Telecommunications Policy, Elsevier, vol. 39(3), pages 363-373.
    16. Laranjeiro, Patrícia F. & Merchán, Daniel & Godoy, Leonardo A. & Giannotti, Mariana & Yoshizaki, Hugo T.Y. & Winkenbach, Matthias & Cunha, Claudio B., 2019. "Using GPS data to explore speed patterns and temporal fluctuations in urban logistics: The case of São Paulo, Brazil," Journal of Transport Geography, Elsevier, vol. 76(C), pages 114-129.
    17. Adrian C. Prelipcean & Gyözö Gidófalvi & Yusak O. Susilo, 2017. "Transportation mode detection – an in-depth review of applicability and reliability," Transport Reviews, Taylor & Francis Journals, vol. 37(4), pages 442-464, July.
    18. Ying Hui & Mengtao Ding & Kun Zheng & Dong Lou, 2017. "Observing Trip Chain Characteristics of Round-Trip Carsharing Users in China: A Case Study Based on GPS Data in Hangzhou City," Sustainability, MDPI, vol. 9(6), pages 1-15, June.
    19. Kemajou, Armel & Jaligot, Rémi & Bosch, Martí & Chenal, Jérôme, 2019. "Assessing motorcycle taxi activity in Cameroon using GPS devices," Journal of Transport Geography, Elsevier, vol. 79(C), pages 1-1.
    20. Feng, Xiaoyan & Sun, Huijun & Wu, Jianjun & Liu, Zhiyuan & Lv, Ying, 2020. "Trip chain based usage patterns analysis of the round-trip carsharing system: A case study in Beijing," Transportation Research Part A: Policy and Practice, Elsevier, vol. 140(C), pages 190-203.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:transp:v:39:y:2016:i:2:p:180-194. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/GTPT20 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.