Predicting commuter flows in spatial networks using a radiation model based on temporal ranges.
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.
Physics Department and the Interdisciplinary Center for Network Science and Applications, University of Notre Dame, Notre Dame, Indiana 46556, USA.
Yihui
Y
Ren
Faculty of Physics, Babes-Bolyai University, RO-400084 Cluj-Napoca, Romania.
Mária
M
Ercsey-Ravasz
School of Traffic and Transportation Engineering, Central South University, 22 Shaoshan South Road, Changsha, Hunan 410075, China.
Pu
P
Wang
Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, USA.
Marta C
MC
González
Physics Department and the Interdisciplinary Center for Network Science and Applications, University of Notre Dame, Notre Dame, Indiana 46556, USA.
Zoltán
Z
Toroczkai