Transactions on Machine Intelligence

Transactions on Machine Intelligence

Demographic and Efficiency Analysis of Street Transportation Network in Districts of Tehran Metropolitan

Document Type : Original Article

Authors
A. Ghahremanlou 1
M. Saffarzadeh 2
A. Naderan 3
H. Javanshir 4
1 PhD Candidate, Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
2 Professor, Department of Civil and Environmental Engineering, Tarbiat Modares University, Tehran, Iran
3 Assistant Professor, Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
4 PhD, Assistant Professor, Department of Industrial Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran
10.47176/TMI.2024.70
Abstract
The primary goal of transportation accessibility is to improve travel efficiency by minimizing the distance between trip origins and destinations, thereby enhancing user experience. While conventional performance assessments focus on speed and distance, this study integrates demographic and spatial analytics to evaluate Tehran’s mobility infrastructure. Using district-level population and census data from Tehran Municipality and the Statistics Center, four analytical steps were undertaken: mapping population density via ArcGIS 10.8, measuring real and straight-line distances with Google Earth Pro, and calculating accessibility using the route factor. The 2021 forecast reveals that approximately 67% of Tehran’s population resides in suburban districts, with District 4 alone accounting for 10.57%. Despite this concentration, peripheral districts remain sparsely populated compared to central ones. Accessibility assessment classified internal district connections into five categories: excellent (38%), moderate (32%), and poor (30%). Tehran’s average route factor of 1.52 indicates a moderate level of accessibility. These findings underscore a significant imbalance in population distribution and transport network efficiency. To address this, targeted investments in underperforming areas should be prioritized. This study demonstrates the value of combining demographic insights with spatial network analysis, delivering actionable intelligence for urban planners to optimize resource allocation and strengthen connectivity in Tehran’s evolving metropolitan landscape.
Keywords
Population Density
Suburban Districts
Accessibility
Street Network
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Transactions on Machine Intelligence
Volume 7, Issue 1
Winter 2024
Pages 70-81

  • Receive Date 05 December 2023
  • Revise Date 17 January 2024
  • Accept Date 30 March 2024