Destination choice game: A spatial interaction theory on human mobility.

Area of Science:

• Computational Social Science
• Mobility Modeling
• Network Science

Background:

• Accurate prediction of human mobility is vital for diverse applications like urban planning and disease mitigation.
• Existing models (e.g., gravity, radiation) often overlook the impact of congestion and crowding on individual travel decisions.
• Understanding the microscopic mechanisms of mobility decisions is key to improving predictive accuracy.

Purpose of the Study:

• To introduce a novel microscopic mechanism for predicting human mobility fluxes.
• To incorporate the effects of destination crowding into mobility models.
• To evaluate the performance of the proposed model against state-of-the-art methods across various spatial scales.

Main Methods:

• Development of the destination choice game (DCG) model, a microscopic agent-based approach.
• Incorporation of spatial interactions and crowding effects into individual decision-making processes.
• Comparative analysis of DCG predictions against established mobility models using real-world data.

Main Results:

• The destination choice game (DCG) model demonstrates superior accuracy in predicting human mobility fluxes.
• The model's effectiveness spans multiple scales, from intracity trips to intercity travel and internal migration.
• The study reveals the gravity model as a special case of the DCG, specifically when destination crowding is ignored.

Conclusions:

• The destination choice game (DCG) offers a more realistic and accurate framework for human mobility prediction.
• Accounting for crowding effects is essential for understanding and modeling individual movement patterns.
• This research advances the field of mobility modeling by integrating microscopic behavioral insights.