IDEAS home Printed from https://ideas.repec.org/a/kap/transp/v42y2015i1p163-188.html
   My bibliography  Save this article

Use of acceleration data for transportation mode prediction

Author

Listed:
  • Muhammad Shafique
  • Eiji Hato

Abstract

Most smartphones today are equipped with an accelerometer, in addition to other sensors. Any data recorded by the accelerometer can be successfully utilised to determine the mode of transportation in use, which will provide an alternative to conventional household travel surveys and make it possible to implement customer-oriented advertising programmes. In this study, a comparison is made between changes in pre-processing, selection methods for generating training data, and classifiers, using the accelerometer data collected from three cities in Japan. The classifiers used were support vector machines (SVM), adaptive boosting (AdaBoost), decision tree and random forests. The results of this exercise suggest that using a 125-point moving average during pre-processing and selecting training data proportionally for all modes will maximise prediction accuracy. Moreover, random forests outperformed all other classifiers by yielding an overall prediction accuracy of 99.8 % for all three cities. Copyright The Author(s) 2015

Suggested Citation

  • Muhammad Shafique & Eiji Hato, 2015. "Use of acceleration data for transportation mode prediction," Transportation, Springer, vol. 42(1), pages 163-188, January.
    • Handle: RePEc:kap:transp:v:42:y:2015:i:1:p:163-188
    DOI: 10.1007/s11116-014-9541-6
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s11116-014-9541-6
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s11116-014-9541-6?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. 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. 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. Zhenbo Lu & Zhen Long & Jingxin Xia & Chengchuan An, 2019. "A Random Forest Model for Travel Mode Identification Based on Mobile Phone Signaling Data," Sustainability, MDPI, vol. 11(21), pages 1-21, October.
    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. 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.
    4. Mercedes Ayuso & Montserrat Guillen & Jens Perch Nielsen, 2019. "Improving automobile insurance ratemaking using telematics: incorporating mileage and driver behaviour data," Transportation, Springer, vol. 46(3), pages 735-752, June.
    5. Montserrat Guillen & Ana M. Pérez-Marín & Mercedes Ayuso & Jens Perch Nielsen, 2018. "“Exposure to risk increases the excess of zero accident claims frequency in automobile insurance”," IREA Working Papers 201810, University of Barcelona, Research Institute of Applied Economics, revised May 2018.
    6. Montserrat Guillen & Jens Perch Nielsen & Mercedes Ayuso & Ana M. Pérez‐Marín, 2019. "The Use of Telematics Devices to Improve Automobile Insurance Rates," Risk Analysis, John Wiley & Sons, vol. 39(3), pages 662-672, March.
    7. Weijia (Vivian) Li & Kara M. Kockelman, 2022. "How does machine learning compare to conventional econometrics for transport data sets? A test of ML versus MLE," Growth and Change, Wiley Blackwell, vol. 53(1), pages 342-376, March.

    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. 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.
    2. Satomi Kimijima & Masahiko Nagai, 2017. "Human Mobility Analysis for Extracting Local Interactions under Rapid Socio-Economic Transformation in Dawei, Myanmar," Sustainability, MDPI, vol. 9(9), pages 1-14, September.
    3. Toşa, Cristian & Sato, Hitomi & Morikawa, Takayuki & Miwa, Tomio, 2018. "Commuting behavior in emerging urban areas: Findings of a revealed-preferences and stated-intentions survey in Cluj-Napoca, Romania," Journal of Transport Geography, Elsevier, vol. 68(C), pages 78-93.
    4. 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.
    5. Yijing Lu & Lei Zhang, 2015. "Imputing trip purposes for long-distance travel," Transportation, Springer, vol. 42(4), pages 581-595, July.
    6. 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.
    7. Roy, Avipsa & Fuller, Daniel & Nelson, Trisalyn & Kedron, Peter, 2022. "Assessing the role of geographic context in transportation mode detection from GPS data," Journal of Transport Geography, Elsevier, vol. 100(C).
    8. 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.
    9. Wenyun Tang & David Levinson, 2014. "An empirical study of the deviation between actual and shortest travel time paths," Working Papers 000125, University of Minnesota: Nexus Research Group.
    10. Bulu, Melih, 2014. "Upgrading a city via technology," Technological Forecasting and Social Change, Elsevier, vol. 89(C), pages 63-67.
    11. 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.
    12. 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.
    13. Bwambale, Andrew & Choudhury, Charisma F. & Hess, Stephane, 2019. "Modelling departure time choice using mobile phone data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 130(C), pages 424-439.
    14. Nour, Akram & Hellinga, Bruce & Casello, Jeffrey, 2016. "Classification of automobile and transit trips from Smartphone data: Enhancing accuracy using spatial statistics and GIS," Journal of Transport Geography, Elsevier, vol. 51(C), pages 36-44.
    15. Shen, Yue & Kwan, Mei-Po & Chai, Yanwei, 2013. "Investigating commuting flexibility with GPS data and 3D geovisualization: a case study of Beijing, China," Journal of Transport Geography, Elsevier, vol. 32(C), pages 1-11.
    16. Mariem Fekih & Tom Bellemans & Zbigniew Smoreda & Patrick Bonnel & Angelo Furno & Stéphane Galland, 2021. "A data-driven approach for origin–destination matrix construction from cellular network signalling data: a case study of Lyon region (France)," Transportation, Springer, vol. 48(4), pages 1671-1702, August.
    17. Iago C. Cavalcante & Rodolfo I. Meneguette & Renato H. Torres & Leandro Y. Mano & Vinícius P. Gonçalves & Jó Ueyama & Gustavo Pessin & Georges D. Amvame Nze & Geraldo P. Rocha Filho, 2022. "Federated System for Transport Mode Detection," Energies, MDPI, vol. 15(23), pages 1-17, December.
    18. Peter Widhalm & Yingxiang Yang & Michael Ulm & Shounak Athavale & Marta González, 2015. "Discovering urban activity patterns in cell phone data," Transportation, Springer, vol. 42(4), pages 597-623, July.
    19. Xiaofeng Lou & Changhai Peng, 2022. "Planning of a comprehensive transportation system in Ma’anshan based on mobile phone signaling data," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(7), pages 9380-9406, July.
    20. 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.

    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:kap:transp:v:42:y:2015:i:1:p:163-188. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.