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Modeling Accessibility-Constrained Networks with Time-Weighted Graphs

Marc Walden, Jason Liu, Ryan Liu, Hamza Khan

TL;DR

The paper addresses campus accessibility for physically disabled users by modeling UCLA as a time-weighted network, where edges encode traversal time and accessibility constraints. It develops a production pipeline that fuses Strava/OpenStreetMap data with intermediate vertices to capture curvature, and compares a standard Dijkstra-based routing approach with a novel Least Resistance algorithm focused on point-to-point routing. Key findings show that disabled travel times are markedly higher than for non-disabled users, with centrality analysis highlighting bottleneck areas and potential targets for infrastructure improvements; the method scales from a prototype 21-node graph to a 1290-node campus network. The work offers a practical tool for planning accessible campus infrastructure and provides a foundation for applying the approach to other universities, while outlining future work on reliable elevation data and automatic identification of design changes.

Abstract

Accessibility for the physically disabled is a prevalent issue on university campuses, where stairs and steep slopes make navigating campus arduous. Our work proposes a pipeline to model a college campus as a network by combining Strava and other APIs with depth-first search to derive insights into wheelchair-accessible paths. We then develop a custom Least Resistance algorithm to compute optimal paths between selected nodes and benchmark it against Dijkstra's algorithm. We highlight crucial nodes on campus using centrality measures, demonstrating that wheelchair users are significantly constrained by the lack of mobility options and accommodations. Our pipeline is designed to support future expansion in scope and accuracy, while enabling the proposal of engineering solutions to improve campus accessibility for physically disabled individuals.

Modeling Accessibility-Constrained Networks with Time-Weighted Graphs

TL;DR

The paper addresses campus accessibility for physically disabled users by modeling UCLA as a time-weighted network, where edges encode traversal time and accessibility constraints. It develops a production pipeline that fuses Strava/OpenStreetMap data with intermediate vertices to capture curvature, and compares a standard Dijkstra-based routing approach with a novel Least Resistance algorithm focused on point-to-point routing. Key findings show that disabled travel times are markedly higher than for non-disabled users, with centrality analysis highlighting bottleneck areas and potential targets for infrastructure improvements; the method scales from a prototype 21-node graph to a 1290-node campus network. The work offers a practical tool for planning accessible campus infrastructure and provides a foundation for applying the approach to other universities, while outlining future work on reliable elevation data and automatic identification of design changes.

Abstract

Accessibility for the physically disabled is a prevalent issue on university campuses, where stairs and steep slopes make navigating campus arduous. Our work proposes a pipeline to model a college campus as a network by combining Strava and other APIs with depth-first search to derive insights into wheelchair-accessible paths. We then develop a custom Least Resistance algorithm to compute optimal paths between selected nodes and benchmark it against Dijkstra's algorithm. We highlight crucial nodes on campus using centrality measures, demonstrating that wheelchair users are significantly constrained by the lack of mobility options and accommodations. Our pipeline is designed to support future expansion in scope and accuracy, while enabling the proposal of engineering solutions to improve campus accessibility for physically disabled individuals.
Paper Structure (12 sections, 6 figures)

This paper contains 12 sections, 6 figures.

Table of Contents

  1. Introduction
  2. Background
  3. Data Collection
  4. Prototype Model.
  5. Production Model.
  6. Methods and Models
  7. Dijkstra's Algorithm.
  8. Least Resistance Algorithm.
  9. Elevation.
  10. Centralities.
  11. Results
  12. Conclusion

Figures (6)

  • Figure 1: Nodes and Edges for the Prototype Model
  • Figure 2: Time taken for physical traversal between all pairs of nodes with an edge between them for disabled and non-disabled individuals
  • Figure 3: Nodes, vertices, and edges after using Strava's API and Depth-First Search
  • Figure 4: Pseudocode for the Depth-First Search Algorithm
  • Figure 5: Boxplot for times taken to traverse between node pairs.
  • ...and 1 more figures