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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A Fuzzy-based Convolutional LSTM Network Approach for Citywide Traffic Flow Prediction
TL;DR: In this article , a fuzzy-based convolutional LSTM neural network (FConvLSTM) method is proposed to improve the accuracy of citywide traffic flow prediction by taking data uncertainty into consideration.
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Effective Traffic Prediction with Self-Supervised Contrastive Learning
TL;DR: The authors utilized contrastive learning to construct an effective auxiliary task to learn feature representations of data in a self-supervised manner, which can be subsequently applied for downstream tasks, which is proven to be more robust against overfitting.
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Simpler is better: Multilevel Abstraction with Graph Convolutional Recurrent Neural Network Cells for Traffic Prediction
TL;DR: A new sequence-to-sequence architecture to extract the spatiotemporal correlation at multiple levels of abstraction using GNN-RNN cells with sparse architecture to decrease training time compared to more complex designs is proposed.
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Decoupled Traffic Spatial-Temporal Graph Neural Network for Traffic Flow Prediction
TL;DR: In this article , the authors proposed an innovative deep learning-based model named DTSTGNN, where the original traffic signal is decoupled into an instantaneous fusion signal and a long-term dependent signal, which are captured by two well-designed modules, the instantaneous fusion module and the longterm dependency module.
Book ChapterDOI
Balanced Cluster-Based Spatio-Temporal Approach for Traffic Prediction
TL;DR: In this paper , a balanced clustering approach using normalized variance is proposed to extract the spatial and temporal attributes of traffic parameters and a further combination of Graph Convolution Networks (GCN) and Gated Recurrent Unit (GRU) technique is applied to predict traffic flow with greater accuracy and less computationally expensive.
References
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