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Trajectory Data Analyses for Pedestrian Space-time Activity Study
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Visit probability and accessibility within space-time prism of activity program.

Jing Lyu1, Feixiong Liao1, Soora Rasouli1

  • 1Urban Planning and Transportation Group, Eindhoven University of Technology, Eindhoven, The Netherlands.

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Summary

This study introduces probabilistic space-time prisms (STPs) to better measure accessibility, moving beyond binary assumptions. The new model accounts for individual travel differences and provides a more comprehensive evaluation of reaching opportunities.

Keywords:
Accessibilitymobility trajectoriesmulti-state supernetworkspace–time prismvisit probability

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Area of Science:

  • Transportation Geography
  • Urban Planning
  • Human Mobility Studies

Background:

  • Traditional space-time prism (STP) models often use binary accessibility, assuming equal access within the prism and no access outside.
  • Existing probabilistic STP models primarily focus on trip-level analysis, limiting their application in comprehensive accessibility measurement.
  • Individual heterogeneity in travel behavior and activity participation is not adequately addressed in current STP frameworks.

Purpose of the Study:

  • To propose a novel model framework for constructing and estimating probabilistic space-time prisms (STPs) for daily activity programs.
  • To develop a space-time accessibility measurement that incorporates visit probabilities derived from probabilistic STPs.
  • To account for individual heterogeneities in travel and activity participation using latent class models.

Main Methods:

  • Developed a model framework based on multi-state supernetworks to construct probabilistic STPs.
  • Employed latent class models for estimating probabilistic STPs, capturing individual differences in travel and activity participation.
  • Proposed a new space-time accessibility measure integrating visit probabilities from the probabilistic STPs.

Main Results:

  • The proposed model framework effectively constructs and estimates probabilistic STPs, capturing nuanced accessibility within daily activity programs.
  • Latent class models successfully accounted for individual heterogeneities in travel and activity participation.
  • The visit probability model accurately represented probabilistic STP interiors, validated with real-world mobility data.

Conclusions:

  • The probabilistic STP framework offers a more realistic representation of accessibility compared to binary models.
  • The proposed space-time accessibility measurement provides a comprehensive evaluation of opportunity access, considering activity chains and individual behavior.
  • This approach enhances our understanding of how individuals navigate space and time to access opportunities in complex urban environments.