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
Citations
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Distributed Fine-Grained Traffic Speed Prediction for Large-Scale Transportation Networks based on Automatic LSTM Customization and Sharing
TL;DR: DistPre as discussed by the authors is a distributed fine-grained traffic speed prediction scheme for large-scale transportation networks, which customizes a Long Short-Term Memory (LSTM) model with an appropriate hyperparameter configuration for a detector.
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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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Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
Kelvin Xu,Jimmy Ba,Ryan Kiros,Kyunghyun Cho,Aaron Courville,Ruslan Salakhutdinov,Richard S. Zemel,Yoshua Bengio +7 more
TL;DR: This paper proposed an attention-based model that automatically learns to describe the content of images by focusing on salient objects while generating corresponding words in the output sequence, which achieved state-of-the-art performance on three benchmark datasets: Flickr8k, Flickr30k and MS COCO.
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Recurrent Neural Network Regularization
TL;DR: This paper shows how to correctly apply dropout to LSTMs, and shows that it substantially reduces overfitting on a variety of tasks.
Journal ArticleDOI
Traffic Flow Prediction With Big Data: A Deep Learning Approach
TL;DR: A novel deep-learning-based traffic flow prediction method is proposed, which considers the spatial and temporal correlations inherently and is applied for the first time that a deep architecture model is applied using autoencoders as building blocks to represent traffic flow features for prediction.
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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.