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Identifying road user classes based on repeated trip behaviour using Bluetooth data

Author

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  • Crawford, F.
  • Watling, D.P.
  • Connors, R.D.

Abstract

Analysing the repeated trip behaviour of travellers, including trip frequency and intrapersonal variability, can provide insights into traveller needs, flexibility and knowledge of the network, as well as inputs for models including learning and/or behaviour change. Data from emerging data sources provide new opportunities to examine repeated trip making on the road network. Point-to-point sensor data, for example from Bluetooth detectors, is collected using fixed detectors installed next to roads which can record unique identifiers of passing vehicles or travellers which can then be matched across space and time. Such data is used in this research to segment road users based on their repeated trip making behaviour, as has been done in public transportation research using smart card data to understand different categories of users. Rather than deciding on traveller segmentation based on a priori assumptions, the method provides a data driven approach to cluster together travellers who have similar trip regularity and variability between days. Measures which account for the strengths and weaknesses of point-to-point sensor data are presented for (a) spatial variability, using Sequence Alignment, and (b) time of day variability, using Model Based Clustering. The proposed method is also applied to one year of data from 23 fixed Bluetooth detectors in a town in northwest England. The data consists of almost 7.5 million trips made by over 300,000 travellers. Applying the proposed methods allows three traveller user classes to be identified: infrequent, frequent, and very frequent. Interestingly, the spatial and time of day variability characteristics of each user class are distinct and are not linearly correlated with trip frequency. The frequent travellers are observed 1–5 times per week on average and make up 57% of the trips recorded during the year. Focusing on these frequent travellers, it is shown that these can be further separated into those with high spatial and time of day variability and those with low spatial and time of day variability. Understanding the distribution of travellers and trips across these user classes, as well as the repeated trip characteristics of each user class, can inform further data collection and the development of policies targeting the needs of specific travellers.

Suggested Citation

  • Crawford, F. & Watling, D.P. & Connors, R.D., 2018. "Identifying road user classes based on repeated trip behaviour using Bluetooth data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 113(C), pages 55-74.
    • Handle: RePEc:eee:transa:v:113:y:2018:i:c:p:55-74
    DOI: 10.1016/j.tra.2018.03.027
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    1. Kay Axhausen & Andrea Zimmermann & Stefan Schönfelder & Guido Rindsfüser & Thomas Haupt, 2002. "Observing the rhythms of daily life: A six-week travel diary," Transportation, Springer, vol. 29(2), pages 95-124, May.
    2. Minnen, Joeri & Glorieux, Ignace & van Tienoven, Theun Pieter, 2015. "Transportation habits: Evidence from time diary data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 76(C), pages 25-37.
    3. Crawford, F. & Watling, D.P. & Connors, R.D., 2017. "A statistical method for estimating predictable differences between daily traffic flow profiles," Transportation Research Part B: Methodological, Elsevier, vol. 95(C), pages 196-213.
    4. Ron Buliung & Matthew Roorda & Tarmo Remmel, 2008. "Exploring spatial variety in patterns of activity-travel behaviour: initial results from the Toronto Travel-Activity Panel Survey (TTAPS)," Transportation, Springer, vol. 35(6), pages 697-722, November.
    5. Elisabetta Cherchi & Cinzia Cirillo, 2014. "Understanding variability, habit and the effect of long period activity plan in modal choices: a day to day, week to week analysis on panel data," Transportation, Springer, vol. 41(6), pages 1245-1262, November.
    6. Andrew Abbott & Angela Tsay, 2000. "Sequence Analysis and Optimal Matching Methods in Sociology," Sociological Methods & Research, , vol. 29(1), pages 3-33, August.
    7. Kitamura, Ryuichi & Yamamoto, Toshiyuki & Susilo, Yusak O. & Axhausen, Kay W., 2006. "How routine is a routine? An analysis of the day-to-day variability in prism vertex location," Transportation Research Part A: Policy and Practice, Elsevier, vol. 40(3), pages 259-279, March.
    8. Paul D. McNicholas, 2016. "Model-Based Clustering," Journal of Classification, Springer;The Classification Society, vol. 33(3), pages 331-373, October.
    9. Heinen, Eva & Chatterjee, Kiron, 2015. "The same mode again? An exploration of mode choice variability in Great Britain using the National Travel Survey," Transportation Research Part A: Policy and Practice, Elsevier, vol. 78(C), pages 266-282.
    10. Robert Schlich & Kay Axhausen, 2003. "Habitual travel behaviour: Evidence from a six-week travel diary," Transportation, Springer, vol. 30(1), pages 13-36, February.
    11. Cherchi, Elisabetta & Cirillo, Cinzia & Ortúzar, Juan de Dios, 2017. "Modelling correlation patterns in mode choice models estimated on multiday travel data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 96(C), pages 146-153.
    12. Gabadinho, Alexis & Ritschard, Gilbert & Müller, Nicolas S & Studer, Matthias, 2011. "Analyzing and Visualizing State Sequences in R with TraMineR," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 40(i04).
    13. Liu, Ronghui & Van Vliet, Dirck & Watling, David, 2006. "Microsimulation models incorporating both demand and supply dynamics," Transportation Research Part A: Policy and Practice, Elsevier, vol. 40(2), pages 125-150, February.
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    Cited by:

    1. F. Crawford & D. P. Watling & R. D. Connors, 2023. "Analysing Spatial Intrapersonal Variability of Road Users Using Point-to-Point Sensor Data," Networks and Spatial Economics, Springer, vol. 23(2), pages 373-406, June.
    2. Zahnow, Renee & Abewickrema, Wanuji, 2023. "Examining regularity in vehicular traffic through Bluetooth scanner data: Is the daily commuter the regular road user?," Journal of Transport Geography, Elsevier, vol. 109(C).
    3. Crawford, Fiona, 2020. "Segmenting travellers based on day-to-day variability in work-related travel behaviour," Journal of Transport Geography, Elsevier, vol. 86(C).
    4. Michał Zawodny & Maciej Kruszyna, 2022. "Proposals for Using the Advanced Tools of Communication between Autonomous Vehicles and Infrastructure in Selected Cases," Energies, MDPI, vol. 15(18), pages 1-15, September.
    5. Chu, Chih-Peng & Chou, Yu-Hsin, 2021. "Using cellular data to analyze the tourists' trajectories for tourism destination attributes: A case study in Hualien, Taiwan," Journal of Transport Geography, Elsevier, vol. 96(C).

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