@article{ref1,
title="A framework for end-to-end deep learning-based anomaly detection in transportation networks",
journal="Transportation research interdisciplinary perspectives",
year="2020",
author="Davis, Neema and Raina, Gaurav and Jagannathan, Krishna",
volume="5",
number="",
pages="e100112-e100112",
abstract="We develop an end-to-end deep learning-based anomaly detection model for temporal data in transportation networks. The proposed EVT-LSTM model is derived from the popular LSTM (Long Short-Term Memory) network and adopts an objective function that is based on fundamental results from EVT (Extreme Value Theory). We compare the EVT-LSTM model with some established statistical, machine learning, and hybrid deep learning baselines. Experiments on seven diverse real-world data sets demonstrate the superior anomaly detection performance of our proposed model over the other models considered in the comparison study.<p /> <p>Language: en</p>",
language="en",
issn="2590-1982",
doi="10.1016/j.trip.2020.100112",
url="http://dx.doi.org/10.1016/j.trip.2020.100112"
}