A universal opportunity model for human mobility.

Area of Science:

• * Computational Social Science
• * Human Mobility Modeling
• * Spatial Interaction Models

Background:

• * Predicting human mobility is crucial for transportation science, spatial economics, and sociology.
• * Classical models like the gravity and intervening opportunity (IO) models have limitations in describing behavior across scales.
• * Existing IO class models accurately predict mobility at specific scales but lack universality.

Purpose of the Study:

• * To develop a universal opportunity model for human mobility prediction.
• * To incorporate human behavioral tendencies (exploratory and cautious) into a new framework.
• * To enhance the understanding of destination selection mechanisms at diverse spatiotemporal scales.

Main Methods:

• * Developed a novel universal opportunity model integrating exploratory and cautious behavioral tendencies.
• * Established a new framework within the intervening opportunity (IO) class models.
• * Validated the model using diverse human mobility datasets.

Main Results:

• * The proposed universal opportunity model demonstrates superior predictive accuracy compared to previous IO class models.
• * The model successfully covers classical models like the radiation and opportunity priority selection models.
• * Empirical results confirm the model's ability to predict human mobility across various scales.

Conclusions:

• * The universal opportunity model provides a more comprehensive approach to predicting human mobility.
• * It offers enhanced insights into the underlying mechanisms of individual destination selection behavior.
• * This model advances the field of human mobility prediction by offering a scalable and versatile framework.