An Agent-Based Simulation of Residential Location Choice of Tenants in Tehran, Iran
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.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
An agent-based evaluation of impacts of transport developments on the modal shift in Tehran, Iran
Changes in travel modes used by people, particularly reduction of the private car use, is an important determinant of effectiveness of transportation plans. Because of dependencies between the choices of residential loca…
Decision MakingComputer vision-enriched discrete choice models, with an application to residential location choice
Visual imagery is indispensable to many multi-attribute decision situations. Examples of such decision situations in travel behaviour research include residential location choices, vehicle choices, tourist destination ch…
AttributeDiscrete Choice ModelsGraph neural networks for residential location choice: connection to classical logit models
Researchers have adopted deep learning for classical discrete choice analysis as it can capture complex feature relationships and achieve higher predictive performance. However, the existing deep learning approaches cann…
Graph Neural NetworkA utility-based spatial analysis of residential street-level conditions; A case study of Rotterdam
Residential location choices are traditionally modelled using factors related to accessibility and socioeconomic environments, neglecting the importance of local street-level conditions. Arguably, this neglect is due to …
Discrete Choice ModelsAutonomous Driving and Residential Location Preferences: Evidence from a Stated Choice Survey
The literature suggests that autonomous vehicles (AVs) may drastically change the user experience of private automobile travel by allowing users to engage in productive or relaxing activities while travelling. As a conse…
Autonomous DrivingAutonomous VehiclesDecision MakingSurvey