Research press release


Nature Communications

Physical sciences: Human travellers split between returners and explorers



今回、Albert-Laszlo Barabasiたちは、3カ月間にわたる67,000人の通話記録の匿名化データセットを解析し、1カ月間にイタリア中部を移動した約46,000台の自動車のGPS追跡データと比較した。そして、追跡対象者ひとりひとりについて、最も訪問回数の多い地点を調べることで得られた反復的移動先への総移動距離を比較した。その結果、「探検者」と呼ばれるサブグループの移動行動を反復的移動から推定できなかった反面、「帰還者」のパターンは十分に解明できた。既存の移動モデルでは、こうした差異を説明できなかったため、Barabasiたちは、移動パターンのシミュレーションによって得られた知見を再現する新しいモデルを開発したといえる。


The analysis of extensive datasets on human mobility patterns reveals the existence of two distinct classes of individual travel behaviour, returners and explorers, reports a study published in Nature Communications. This observation allows the creation of a new model of mobility which may help contain the spread of epidemics.

Variability in human movement is accompanied by a relatively high degree of statistical predictability of global patterns at the population level. The increasing availability of tracking datasets has now made it possible to develop and verify quantitative mobility models.

Albert-Laszlo Barabasi and colleagues analysed an anonymized dataset composed of 67,000 individuals’ call records over a three-month period, and compared it with the GPS traces of roughly 46,000 vehicles travelling through central Italy over a month. For each tracked person, they compared the overall distance travelled to their recurrent mobility, obtained by looking at their most visited locations. The researchers found that the mobility behaviour of a subset of individuals, the explorers, could not be approximated by their recurrent trips, as opposed to the well-defined patterns of the returners. Since existing mobility models could not account for this difference, the research team developed a new model to reproduce their findings through simulated travel patterns.

Explorers, by virtue of their non-recurring mobility, were found to influence the potential spread of epidemics. For example, simulated global mobility networks for the population of Tuscany show that, as the proportion of explorers increases, the chances of a widespread epidemic also increase. This suggests that improved understanding and modelling of human mobility have the potential to help predict the future spread of disease.

doi: 10.1038/ncomms9166

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