Abstract Body

Large cities are in constant changes. These changes are becoming increasingly compulsory due to the dynamism and growing urban population. Such flows of people rely on sound and timely transport systems daily. Therefore, more paved paths must be developed while more private and public vehicles used. The result will be more traffic congestions, air pollution, carbon emissions, infrastructure, and energy consumptions. Sustainable solutions and strategies must be developed to reduce these burdens and make transportation more sustainable and effective.

This research has made a case for Tehran as the largest city in Iran in terms of population and size area. The city has grown fast due to mass rural-urban migrations, industrialization, and improper urban development over the past decades. Currently, the city has embedded 22 Municipality Districts across its vast fish-like shape area.

Based on a recent comprehensive annual report published by the Tehran Municipality, we determined the most influential parameters affecting daily commutes across all residential areas located in 22 Districts. A total of 15 variables were defined, such as ‘number of the population per area unit’ (Pop_dens). As an innovative data analysis approach, we employed SQL algorithms to evaluate relationships between these 15 variables and select those with lower-than-mean thresholds. We then crossed each of these variables chosen with daily commutes figures for each District. We could attain several SQL-based queries and determine the best District for offering low-carbon daily commutes. Among all Tehran Districts, District-22 could offer higher scores (6 out of 10) based on queries made for low-carbon daily commutes.

This research revealed a reliable data analysis approach to tackle transport inefficiency and reducing carbon emissions in the cities.