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

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

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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


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.

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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)


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
  • Connected Car
  • Geospatial big Data
  • Navigation Behaviour
  • Cluster Analysis


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