PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
March 22, 2026Urban Planning and Transport Research0 citationsOpen Access

Measuring Central Business District (CBD) walkability using intersection permeability and activity intensity

View Full Paper
HIHamid Iravani

Key Points

Key points are not available for this paper at this time.

Abstract

This paper develops a Central Business District (CBD) -specific walkability index to address limitations of standard metropolitan-wide indices that mischaracterize employment-dominant CBDs. The index is applied to CBDs in 50 U. S. state-representative cities using standardized 1-mile diameter CBD windows (land area excluding water). Network permeability is captured through densities of 4 + leg and 3-leg intersections, and activity intensity is represented by employment and population densities. To avoid penalizing legitimate job surpluses, a population-to-employment ratio adjustment is applied only when CBDs become unusually resident-heavy (T = 0. 35, Rₘax = 10). Criterion weights, derived using the Analytic Hierarchy Process (CR = 0. 013), are 0. 46 (4 + intersection density), 0. 14 (3-leg intersection density), 0. 30 (employment density), 0. 05 (population density), and 0. 05 (resident-heaviness score). Validation uses commute mode shares for workers 16+, including walk-only and walk-plus-transit trips. A regression model with all five components explains walk + transit share well (R² = 0. 752, n = 50). Rank agreement between the index and walk + transit shares is strong (Spearman ρ ≈ 0. 75), and mean walk + transit shares drop sharply across index tiers. The method offers a transparent CBD walkability benchmark, highlighting the importance of connected street networks and mixed-use intensity.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Hamid Iravani (2026) studied this question.

synapsesocial.com/papers/6a0b56a5db419d24cd5d0399https://doi.org/10.1080/21650020.2026.2648896
Ask AI
Helpful
Bookmark
Share
View Full Paper