10,305 research outputs found
Predicting Urban Dispersal Events: A Two-Stage Framework through Deep Survival Analysis on Mobility Data
Urban dispersal events are processes where an unusually large number of
people leave the same area in a short period. Early prediction of dispersal
events is important in mitigating congestion and safety risks and making better
dispatching decisions for taxi and ride-sharing fleets. Existing work mostly
focuses on predicting taxi demand in the near future by learning patterns from
historical data. However, they fail in case of abnormality because dispersal
events with abnormally high demand are non-repetitive and violate common
assumptions such as smoothness in demand change over time. Instead, in this
paper we argue that dispersal events follow a complex pattern of trips and
other related features in the past, which can be used to predict such events.
Therefore, we formulate the dispersal event prediction problem as a survival
analysis problem. We propose a two-stage framework (DILSA), where a deep
learning model combined with survival analysis is developed to predict the
probability of a dispersal event and its demand volume. We conduct extensive
case studies and experiments on the NYC Yellow taxi dataset from 2014-2016.
Results show that DILSA can predict events in the next 5 hours with F1-score of
0.7 and with average time error of 18 minutes. It is orders of magnitude better
than the state-ofthe-art deep learning approaches for taxi demand prediction.Comment: To appear in AAAI-19 proceedings. The reason for the replacement was
the misspelled author name in the meta-data field. Author name was corrected
from "Ynahua Li" to "Yanhua Li". The author list in the paper was correct and
remained unchange
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
Explaining Aviation Safety Incidents Using Deep Temporal Multiple Instance Learning
Although aviation accidents are rare, safety incidents occur more frequently
and require a careful analysis to detect and mitigate risks in a timely manner.
Analyzing safety incidents using operational data and producing event-based
explanations is invaluable to airline companies as well as to governing
organizations such as the Federal Aviation Administration (FAA) in the United
States. However, this task is challenging because of the complexity involved in
mining multi-dimensional heterogeneous time series data, the lack of
time-step-wise annotation of events in a flight, and the lack of scalable tools
to perform analysis over a large number of events. In this work, we propose a
precursor mining algorithm that identifies events in the multidimensional time
series that are correlated with the safety incident. Precursors are valuable to
systems health and safety monitoring and in explaining and forecasting safety
incidents. Current methods suffer from poor scalability to high dimensional
time series data and are inefficient in capturing temporal behavior. We propose
an approach by combining multiple-instance learning (MIL) and deep recurrent
neural networks (DRNN) to take advantage of MIL's ability to learn using weakly
supervised data and DRNN's ability to model temporal behavior. We describe the
algorithm, the data, the intuition behind taking a MIL approach, and a
comparative analysis of the proposed algorithm with baseline models. We also
discuss the application to a real-world aviation safety problem using data from
a commercial airline company and discuss the model's abilities and
shortcomings, with some final remarks about possible deployment directions
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