@article{ref1,
title="Modeling real-time human mobility based on mobile phone and transportation data fusion",
journal="Transportation research part C: emerging technologies",
year="2018",
author="Huang, Zhiren and Ling, Ximan and Wang, Pu and Zhang, Fan and Mao, Yingping and Lin, Tao and Wang, Fei-Yue",
volume="96",
number="",
pages="251-269",
abstract="Even though a variety of human mobility models have been recently developed, models that can capture real-time human mobility of urban populations in a sustainable and economical manner are still lacking. Here, we propose a novel human mobility model that combines the advantages of mobile phone signaling data (i.e., comprehensive penetration in a population) and urban transportation data (i.e., continuous collection and high accuracy). Using the proposed human mobility model, travel demands during each 1-h time window were estimated for the city of Shenzhen, China. Significantly, the estimated travel demands not only preserved the distribution of travel demands, but also captured real-time bursts of mobility fluxes during large crowding events. Finally, based on the proposed human mobility model, a predictive model is deployed to predict crowd gatherings that usually cause severe traffic jams.  Keywords: Road transportation <p /> <p>Language: en</p>",
language="en",
issn="0968-090X",
doi="10.1016/j.trc.2018.09.016",
url="http://dx.doi.org/10.1016/j.trc.2018.09.016"
}