Open AccessProceedings Article
Deep Learning: A Generic Approach for Extreme Condition Traffic Forecasting.
Rose Yu,Yaguang Li,Cyrus Shahabi,Ugur Demiryurek,Yan Liu +4 more
- pp 777-785
TLDR
This work builds a deep neural network based on long short term memory (LSTM) units and applies Deep LSTM to forecast peak-hour traffic and manages to identify unique characteristics of the traffic data.Abstract:
Traffic forecasting is a vital part of intelligent transportation systems. It becomes particularly challenging due to short-term (e.g., accidents, constructions) and long-term (e.g., peak-hour, seasonal, weather) traffic patterns. While most of the previously proposed techniques focus on normal condition forecasting, a single framework for extreme condition traffic forecasting does not exist. To address this need, we propose to take a deep learning approach. We build a deep neural network based on long short term memory (LSTM) units. We apply Deep LSTM to forecast peak-hour traffic and manage to identify unique characteristics of the traffic data. We further improve the model for postaccident forecasting with Mixture Deep LSTM model. It jointly models the normal condition traffic and the pattern of accidents. We evaluate our model on a realworld large-scale traffic dataset in Los Angeles. When trained end-to-end with suitable regularization, our approach achieves 30%-50% improvement over baselines. We also demonstrate a novel technique to interpret the model with signal stimulation. We note interesting observations from the trained neural network.read more
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References
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Sequence to Sequence Learning with Neural Networks
TL;DR: This paper presents a general end-to-end approach to sequence learning that makes minimal assumptions on the sequence structure, and finds that reversing the order of the words in all source sentences improved the LSTM's performance markedly, because doing so introduced many short term dependencies between the source and the target sentence which made the optimization problem easier.
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Kelvin Xu,Jimmy Ba,Ryan Kiros,Kyunghyun Cho,Aaron Courville,Ruslan Salakhutdinov,Richard S. Zemel,Yoshua Bengio +7 more
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Recurrent Neural Network Regularization
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Practical recommendations for gradient-based training of deep architectures
TL;DR: Overall, this chapter describes elements of the practice used to successfully and efficiently train and debug large-scale and often deep multi-layer neural networks and closes with open questions about the training difficulties observed with deeper architectures.