@article{8476163,
title = {BusBeat: Early Event Detection with Real-Time Bus GPS Trajectories},
author = {Shunsuke Aoki and Kaoru Sezaki and Nicholas Jing Yuan and Xing Xie},
doi = {10.1109/TBDATA.2018.2872532},
issn = {2372-2096},
year = {2018},
date = {2018-09-28},
journal = {IEEE Transactions on Big Data},
pages = {1-1},
abstract = {Large-scale events attracting many participants might have a strong negative impact on productivity, mobility, comfort, and safety. Within the few years, serious accidents led by congestion have occurred multiple times, especially during sports events, religious ceremonies, festivals, and so on.To alleviate these serious accidents, predicting the occurrence of a large-scale event is much significant. In fact, when we know an event occurrence in advance, some of those who are not interested in the event might change their plans and/or might take a detour to avoid to get involved in a heavy congestion. In this paper, we present an early event detection technique named BusBeat. BusBeat uses GPS trajectory data collected from periodic-cars, that are vehicles periodically traveling on a pre-scheduled route with a pre-determined departure time, such as a transit bus, shuttle, garbage truck, or municipal patrol car. In addition, BusBeat uses Time-dependent Congestion Network to detect geo-spatial events. BusBeat achieves early event detection without incurring any privacy invasion, by using the continuous trajectories of periodic-cars, that provide the real-time traffic flow and speed. We evaluate our BusBeat using over 7,000-bus data collected in Beijing for 5 months and compare with the check-in data collected from a social network service.},
keywords = {Event detection, GPS trajectory, Location knowledge, Smart city, Spatial data mining, Urban computing},
pubstate = {published},
tppubtype = {article}
}