Predicting commuter flows in spatial networks using a radiation model based on temporal ranges
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Gonzalez_Predicting commuter.pdf
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Author(s)
Ren, Yihui
Ercsey-Ravasz, Maria
Wang, Pu
Gonzalez, Marta C.
Toroczkai, Zoltan
Date Issued
November 2014
Journal
Nature Communications
Publisher
Nature Publishing Group
Citation
Ren, Yihui, Maria Ercsey-Ravasz, Pu Wang, Marta C. Gonzalez, and Zoltan Toroczkai. “Predicting Commuter Flows in Spatial Networks Using a Radiation Model Based on Temporal Ranges.” Nat Comms 5 (November 6, 2014): 5347.
Version
Original manuscript
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1038/ncomms6347
