Journal of Transportation Research

Journal of Transportation Research

Investigation of behavioral policies to reduce the emissions of light vehicles: case study of district 13 of Tehran city

Document Type : Original Article

Authors
Mohammad Erfan Doraki 1
Akram Avami 2
Zahra Barzegar 3
Bahram Malek Mohammadi 4
1 M.Sc., Grad., Department of Energy Engineering, Sharif University of Technology, Tehran, Iran.
2 Associate Professor, Department of Energy Engineering, Sharif University of Technology, Tehran, Iran.
3 Assistant Professor, Environmental Studies, Tehran Urban Research and Planning Center, Tehran, Iran.
4 Associate Professor, Faculty of Environment, University of Tehran, Tehran, Iran.
10.22034/tri.2025.496932.3309
Abstract
This study employs an agent-based modeling approach using the MATSim tool to examine various behavioral scenarios and their effects on traffic flow and pollutant emissions in District 13 of Tehran. First, an artificial population of this district was developed while preserving key demographic characteristics. Subsequently, different behavioral scenarios were simulated through agent-based modeling, and the corresponding emissions were calculated. The results indicate that teleworking significantly reduces vehicle traffic and pollutants such as CO₂, NOx, SO₂, and PM₂.5, making it the most effective strategy for improving air quality and mitigating congestion. In contrast, flexible working hours exerted a limited impact on reducing air pollution—primarily due to additional trips generated—and in some cases even led to increased emissions, including a 9% rise in CO₂. Furthermore, expanding bicycle infrastructure decreased reliance on private cars, cutting CO₂ emissions by up to 26% and raising the modal share of bicycle use to 51.6%. Overall, this research demonstrates that combining travel demand management policies with sustainable infrastructure—such as cycling networks and clean public transportation—can significantly curtail pollution and traffic congestion in densely populated urban areas.
Keywords
Agent-Based Modeling
Behavioral Scenarios
Pollution
PM
Traffic
Subjects
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