Where do we go from here?

We move between different places every day: go to work, travel to a city, or even migrate to another country. Although everyone has different ideas when choosing a destination, human mobility at the population level follows a law ...
Published in Social Sciences

We move between different places every day: go to work, travel to a city, or even migrate to another country. Although everyone has different ideas when choosing a destination, human mobility at the population level follows a law similar to Newton's law of gravitation, that is, the flow between two places is proportional to their population and decays as the power of their distance. The gravity model developed according to this law has been widely used to predict human mobility between places. However, the gravity model doesn't directly allow us to understand how people choose their destination. At least, as an individual human being, I, myself, don't use the product of populations of my current place and one of my potential destinations and certain negative power of the distance to the place to decide whether I travel to there or not. So, where do we go from here? This is a question I'm interested in.

Figure 1. Intercity travel in China. Dataset downloaded from here.

As early as 1940, Stouffer established the intervening opportunities (IO) model [1] to describe an individual's destination selection behavior. The IO model takes the total number of opportunities (often proportional to population) between the origin and the destination (named intervening opportunities) as a key factor in determining where to go. Although the IO model is not as well known as the gravity model, it provides a new direction for modeling human mobility. It has recently triggered the establishment of many new IO class models, including the radiation model [2], the population-weighted opportunities (PWO) model [3], the deliberate social tie (DST) model [4] and others. These models can achieve accurate predictions at specific spatiotemporal scales. For example, the radiation model can accurately predict commuting behavior, and the PWO model can accurately predict intracity [3] and intercity mobility [5]. However, the individual destination selection rules of these models are different. An IO class model that can simultaneously describe the individual's destination selection behavior at different spatiotemporal scales has been lacking.

Last year my Ph.D. student Er-Jian Liu and I started research on this issue. We believe that the agent representing all of the individuals has two behavioral tendencies when choosing a destination: one is the exploratory tendency, and the other is the cautious tendency. The more exploratory the agent, the more likely the distant potential destinations will be selected; the more cautious the agent, the more likely the near potential destinations will be chosen. We established a universal opportunity model with these two tendencies as parameters and collected a variety of human mobility datasets to validate its predictive ability. The results were exciting for both of us: our model can better predict human mobility than previous IO class models. Not only that, but we also found that the agent's destination selection behavioral tendencies are significantly different in different types of mobility, as shown in Figure 2.

Figure 2. Parameter combination with the highest model prediction accuracy for different types of mobility datasets.

From this triangle, we can see that both commuters and truck drivers are generally more cautious when selecting destinations. On the other hand, both migrants and job seekers usually are more exploratory. Intercity travelers' destination selection behavioral tendencies are somewhere between those of the commuters and migrants. For intracity travelers, both exploratory and cautious tendencies are weak. These results may help us better understand the underlying mechanism of the individual's destination selection behavior in different types of human mobility.

You can find the paper here.

[1] Stouffer, S. A. Intervening opportunities: A theory relating mobility and distance. Am. Sociol. Rev. 5, 845-867 (1940).
[2] Simini, F., González, M. C., Maritan, A. & Barabási, A. L. A universal model for mobility and migration patterns. Nature 484, 96-100 (2012).
[3] Yan, X.-Y., Zhao, C., Fan, Y., Di, Z.-R. & Wang, W.-X. Universal predictability of mobility patterns in cities. J. R. Soc. Interface 11, 20140834 (2014).
[4] Sim, A., Yaliraki, S. N., Barahona, M., & Stumpf, M. P. Great cities look small. J. R. Soc. Interface 12, 20150315 (2015). 
[5] Yan, X.-Y., Wang, W.-X., Gao, Z.-Y. & Lai, Y.-C. Universal model of individual and population mobility on diverse spatial scales. Nat. Commun. 8, 1639 (2017).

Please sign in or register for FREE

If you are a registered user on Research Communities by Springer Nature, please sign in

Subscribe to the Topic

Humanities and Social Sciences > Society

Related Collections

With collections, you can get published faster and increase your visibility.

Retinal imaging and diagnostics

This Collection invites works providing insight into using novel or existing retinal imaging technologies in clinical applications or presents new or adaptive forms of these techniques.

Publishing Model: Open Access

Deadline: Apr 30, 2024