An Agent-Based Simulation of Residential Location Choice of Tenants in Tehran, Iran

TL;DR

This paper introduces a novel agent-based simulation model for residential location choice in Tehran, accurately predicting tenant residences by considering individual preferences and competition among agents.

Contribution

It develops a disaggregated agent-based model using a constrained NSGA-II algorithm to simulate tenant decision-making in residential location choice.

Findings

Accurately simulates 59.3% of tenant residences at the traffic zone level.

Validates the model by comparing simulated and actual tenant locations.

Demonstrates the effectiveness of agent-based modeling in land use and transportation planning.

Abstract

Residential location choice modeling is one of the substantial components of land use and transportation models. While numerous aggregated mathematical and statistical approaches have been developed to model the residence choice behavior of households, disaggregated approaches such as the agent-based modeling have shown interesting capabilities. In this article, a novel agent-based approach is developed to simulate the residential location choice of tenants in Tehran, the capital of Iran. Tenants are considered as agents who select their desired residential alternatives according to their characteristics and preferences for various criteria such as the rent, accessibility to different services and facilities, environmental pollution, and distance from their workplace and former residence. The choice set of agents is limited to their desired residential alternatives by applying a constrained NSGA-II algorithm. Then, agents compete with each other to select their final residence among their alternatives. Results of the proposed approach are validated by comparing simulated and actual residences of a sample of tenants. Results show that the proposed approach is able to accurately simulate the residence of 59.3% of tenants at the traffic analysis zone level.