A universal opportunity model for human mobility

Abstract

Predicting human mobility between locations has practical applications in transportation science, spatial economics, sociology and many other fields. For more than 100 years, many human mobility prediction models have been proposed, among which the gravity model analogous to Newton's law of gravitation is widely used. Another classical model is the intervening opportunity (IO) model, which indicates that an individual selecting a destination is related to both the destination's opportunities and the intervening opportunities between the origin and the destination. The IO model established from the perspective of individual selection behavior has recently triggered the establishment of many new IO class models. Although these IO class models can achieve accurate prediction at specific spatiotemporal scales, an IO class model that can describe an individual's destination selection behavior at different spatiotemporal scales is still lacking. Here, we develop a universal opportunity model that considers two human behavioral tendencies: one is the exploratory tendency, and the other is the cautious tendency. Our model establishes a new framework in IO class models and covers the classical radiation model and opportunity priority selection model. Furthermore, we use various mobility data to demonstrate our model's predictive ability. The results show that our model can better predict human mobility than previous IO class models. Moreover, this model can help us better understand the underlying mechanism of the individual's destination selection behavior in different types of human mobility.

Universal scaling laws of collective human flow patterns in urban regions

“…Macroscopically, in the field of human migration, the so called gravity law has been widely applied [21][22][23] . Besides the gravity model, the intervening opportunity class model, where the flow amount is proportional to the opportunity of the destination and inversely proportional to the intervention opportunity between the origin and the destination, has been widely studied [24][25][26][27][28] . Also, probabilistic human mobility prediction are widely performed for congestion and advertisement optimization [29][30][31][32] .…”

Kernel-based formulation of intervening opportunities for spatial interaction modelling

“…4 a, c. In that case, the result indicated that the underlying mechanism of the decision-making process is not the same as the commuting flow. Migrants may tend to place high importance on the destination opportunity benefit rather than the distance to the destination as mentioned in a previous study 44 .…”

“…Although several parameter-free and universal models for human mobility are proposed 30 , 38 , 43 , 46 , we used the OPS model for comparison to the newly proposed model, because the model has the derivation and high predictability regardless of spatial scale such as inter-city and intra-city scales 43 , 44 .…”

Predictive limitations of spatial interaction models: a non-Gaussian analysis

“…The Sørensen-Dice coefficient is often used to compare models against real data 17 , 18 , 21 – 26 and is sometimes referred to as the ‘common part of commuters’ in this context. This is defined as for model values and flow data from site i to site j .…”

“…However, there are problems with the underlying statistical basis for many of the most popular approaches. The Sørensen-Dice coefficient is often used to compare models against real data 17,18,[21][22][23][24][25][26] and is sometimes referred to as the 'common part of commuters' in this context. This is defined as DSC = ij min( F ij , F ij )/ ij F ij for model values F ij and flow data F ij from site i to site j.…”