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Implicit GPS-based bicycle route choice model using clustering methods and a LSTM network

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  • Lucas Magnana
  • Herve Rivano
  • Nicolas Chiabaut

Abstract

Biking is gaining in popularity all around the world as a healthy and environmentally friendly mode of transportation. Urban policies tend to encourage citizens to use bicycles. This can be done by creating new cycling infrastructures, the renovation of old ones or the deployment of bike-sharing systems (BSS). These policies having a cost, understanding and predicting the behavior of cyclists has become a necessity in order to optimize them. Classical methods analyzing cyclists’ route choices use external factors and generated choice sets of paths along with a logit model to create a discrete route choice model. Nevertheless, few studies focus on the predictive capacity that this type of model can offer. In this paper, we developed a prediction-centered bicycle route choice model. Our model is created without using external factors or choice sets of paths as in the more classical methods. The idea of our method is to use deep and machine learning algorithms on GPS tracks. These algorithms learn representations from the data which replace explicit factors. To build the model, we clustered the GPS tracks using DBSCAN. The clusters allow to identify the cyclists’ preferred road segments and are used to create paths using them. A method weighting the road graph weights is developed to create paths passing through the preferred road segments of a given cluster. A LSTM is finally trained in order to retrieve a cluster from a shortest path between an origin/destination pair. Tracks created by our model are more similar to the original GPS tracks than the shortest paths or tracks generated by a prominent path computation service.

Suggested Citation

  • Lucas Magnana & Herve Rivano & Nicolas Chiabaut, 2022. "Implicit GPS-based bicycle route choice model using clustering methods and a LSTM network," PLOS ONE, Public Library of Science, vol. 17(3), pages 1-20, March.
  • Handle: RePEc:plo:pone00:0264196
    DOI: 10.1371/journal.pone.0264196
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    References listed on IDEAS

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    1. Xiaolu Zhou, 2015. "Understanding Spatiotemporal Patterns of Biking Behavior by Analyzing Massive Bike Sharing Data in Chicago," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-20, October.
    2. Pablo Vera-Villarroel & Daniela Contreras & Sebastián Lillo & Christian Beyle & Ariel Segovia & Natalia Rojo & Sandra Moreno & Francisco Oyarzo, 2016. "Perception of Safety and Liking Associated to the Colour Intervention of Bike Lanes: Contribution from the Behavioural Sciences to Urban Design and Wellbeing," PLOS ONE, Public Library of Science, vol. 11(8), pages 1-15, August.
    3. Zhang, Yongping & Mi, Zhifu, 2018. "Environmental benefits of bike sharing: A big data-based analysis," Applied Energy, Elsevier, vol. 220(C), pages 296-301.
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    1. Łukawska, Mirosława & Paulsen, Mads & Rasmussen, Thomas Kjær & Jensen, Anders Fjendbo & Nielsen, Otto Anker, 2023. "A joint bicycle route choice model for various cycling frequencies and trip distances based on a large crowdsourced GPS dataset," Transportation Research Part A: Policy and Practice, Elsevier, vol. 176(C).

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