Predicting commuter flows in spatial networks using a radiation model based on temporal ranges

• Published in: Nature communications Vol. 5; no. 1; p. 5347
• Main Authors: Ren, Yihui, Ercsey-Ravasz, Mária, Wang, Pu, González, Marta C., Toroczkai, Zoltán
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 06-11-2014; Nature Publishing Group
• Subjects: 639/766/259; Humanities and Social Sciences; multidisciplinary; Science; Science (multidisciplinary)
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/ncomms6347

Understanding network flows such as commuter traffic in large transportation networks is an ongoing challenge due to the complex nature of the transportation infrastructure and human mobility. Here we show a first-principles based method for traffic prediction using a cost-based generalization of the radiation model for human mobility, coupled with a cost-minimizing algorithm for efficient distribution of the mobility fluxes through the network. Using US census and highway traffic data, we show that traffic can efficiently and accurately be computed from a range-limited, network betweenness type calculation. The model based on travel time costs captures the log-normal distribution of the traffic and attains a high Pearson correlation coefficient (0.75) when compared with real traffic. Because of its principled nature, this method can inform many applications related to human mobility driven flows in spatial networks, ranging from transportation, through urban planning to mitigation of the effects of catastrophic events.
Mathematical tools for understanding network flows with a social component are much less developed than for, say, electric circuits. Ren
et al
. introduce a method by generalizing the radiation model to flows in spatial networks, which they apply to predict commuter flows in a highway network.