How Connected Are Major Canadian Cities? A Large-Scale Empirical Investigation

Street network design is an important yet extremely complex problem in urban planning as it is shaped and influenced by human travel patterns, city planning, and local cultures. Modern cites combine multiple modes of transportation; however, each mode may prefer a certain type of network structure (e.g., aligned grids favor public transit while cul de sacs favor personal vehicles). Hence, it is of critical importance to understand the network patterns of cities in general and street network connectivity in particular. Nevertheless, prior efforts concentrate on measuring street network structure from a holistic point of view using limited connectivity measures. Therefore, this study investigates network patterns using a wide range of metrics constructed upon graph theory, such as intersection density, street density, node degree, circuity, and street length. Large-scale geospatial data from 30 major Canadian cities were assimilated and used to quantify the connectedness of individual cities. Moreover, similarities and differences were explored between cities by utilizing a hierarchical clustering analysis. Street orientation was also analyzed to provide an in-depth understanding, which combines with the street connectivity together explain the matching degree between transportation system modes and the city street network. The findings of this study suggest that the method presented herein is effective in measuring network patterns of large cities, which in turn, will help in the understanding of the characteristics of urban street networks and guide the cities towards building a more sustainable road network.

Language

  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; Maps; References; Tables;
  • Pagination: 16p

Subject/Index Terms

Filing Info

  • Accession Number: 01763976
  • Record Type: Publication
  • Report/Paper Numbers: TRBAM-21-02650
  • Files: TRIS, TRB, ATRI
  • Created Date: Dec 23 2020 11:16AM