Document Open Access Logo

National-Scale Spatiotemporal Variation in Driver Navigation Behaviour and Route Choice (Short Paper)

Authors Elliot Karikari , Manon Prédhumeau , Peter Baudains , Ed Manley



PDF
Thumbnail PDF

File

LIPIcs.GIScience.2023.45.pdf
  • Filesize: 1.52 MB
  • 6 pages

Document Identifiers

Author Details

Elliot Karikari
  • Leeds Institute for Data Analytics, University of Leeds, UK
Manon Prédhumeau
  • School of Geography, University of Leeds, UK
Peter Baudains
  • ESRC Consumer Data Research Centre, University of Leeds, UK
Ed Manley
  • School of Geography, University of Leeds, UK

Acknowledgements

The data for this research have been provided by the Consumer Data Research Centre, an ESRC Data Investment, under project ID CDRC 376, ES/L011840/1; ES/L011891/1.

Cite AsGet BibTex

Elliot Karikari, Manon Prédhumeau, Peter Baudains, and Ed Manley. National-Scale Spatiotemporal Variation in Driver Navigation Behaviour and Route Choice (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 45:1-45:6, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2023)
https://doi.org/10.4230/LIPIcs.GIScience.2023.45

Abstract

Understanding human behaviour is an integral task in GIScience, facilitated by increasingly large and descriptive datasets on human activity. Large-scale trajectory data have been particularly useful in measuring behaviours in different contexts, and understanding the relationship between the built environment and people. Yet, to date, most of these studies have focused on urban or regional scale analyses, with less exploration of behavioural variation at larger spatial scales. Human navigation behaviour is inherently linked to variation in spatial structure, and a study of national variations could help to better understand this variability. In this paper, we analyse GPS data from over 1 million journeys by 50,000 connected cars across the UK. Some key statistics relating to route choice are computed, and their variations are explored over time and space. A k-mean clustering of the trips identifies different types of trips and shows that their distribution varies by time of day and across the country. The insights gained from the data highlight spatio-temporal variations in road navigation, which should be considered in transportation modelling and planning.

Subject Classification

ACM Subject Classification
  • Applied computing → Transportation
Keywords
  • Connected Car
  • Geospatial big Data
  • Navigation Behaviour
  • Cluster Analysis

Metrics

  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    0
    PDF Downloads

References

  1. C Bongiorno, Y Zhou, M Kryven, D Theurel, A Rizzo, P Santi, J Tenenbaum, and C Ratti. Vector-based pedestrian navigation in cities. Nat. Comput. Sci, 1(10):678-685, 2021. Google Scholar
  2. R Coppola and M Morisio. Connected car: technologies, issues, future trends. ACM Comput. Surv., 49(3):1-36, 2016. Google Scholar
  3. E Karikari, M Prédhumeau, P Baudains, and E Manley. Analysing connected car data to understand vehicular route choice. In 31st Annual Geographical Information Science Research UK Conference (GISRUK), Glasgow, Scotland, 2023. Google Scholar
  4. L Li, S Wang, and F-Y Wang. An analysis of taxi driver’s route choice behavior using the trace records. IEEE Trans. Comput. Soc. Syst., 5(2):576-582, 2018. Google Scholar
  5. EJ Manley, JD Addison, and T Cheng. Shortest path or anchor-based route choice: a large-scale empirical analysis of minicab routing in London. J. Transp. Geogr., 43:123-139, 2015. Google Scholar
  6. L Pappalardo, S Rinzivillo, Z Qu, D Pedreschi, and F Giannotti. Understanding the patterns of car travel. Eur. Phys. J. Spec. Top., 215:61-73, 2013. Google Scholar
  7. L Pappalardo, F Simini, G Barlacchi, and R Pellungrini. scikit-mobility: A Python library for the analysis, generation and risk assessment of mobility data. J. Stat. Softw., 2022. Google Scholar
  8. B Yin and F Leurent. What are the multimodal patterns of individual mobility at the day level in the Paris region? A two-stage data-driven approach based on the 2018 Household Travel Survey. Transportation, pages 1-30, 2022. Google Scholar
Questions / Remarks / Feedback
X

Feedback for Dagstuhl Publishing


Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail