Journal ArticleDOI
A Short-term Traffic Speed Prediction Model Based on LSTM Networks
Yu-Ling Hsueh,Yu-Ren Yang +1 more
TLDR
This work uses a long short-term memory (LSTM) network to analyze sequential sensor data to predict the car speed of the next time interval on the freeway and demonstrates that the proposed method for traffic speed prediction has achieved high accuracy.Abstract:
To successfully deploy an intelligent transportation system, it is essential to construct an effective method of traffic speed prediction. Recently, due to the advancements in sensor technology, traffic data have experienced explosive growth. It is therefore a challenge to construct an efficient model with highly accurate predictions. To improve the accuracy and the efficiency of short-term traffic predictions, we propose a prediction model based on deep learning approaches. We use a long short-term memory (LSTM) network to analyze sequential sensor data to predict the car speed of the next time interval on the freeway. Unlike the traditional model that only considers the changes in traffic speed which is used to derive the temporal and spatial features from the prediction road section, we mainly consider the features of the number of the most representative car types and the traffic speed variation of the front road segment that is ahead of the prediction road segment in addition to the number of cars, the road occupancy, and the traffic speed latency to successfully learn and capture the hidden patterns from the sensor data so as to improve the prediction accuracy. To the best of our knowledge, very few investigations have been conducted to consider the correlation between car speed and car type for a prediction model. Moreover, our extensive experiments demonstrate that the proposed method for traffic speed prediction has achieved high accuracy.read more
Citations
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Journal ArticleDOI
Prediction of Geological Parameters during Tunneling by Time Series Analysis on In Situ Data.
TL;DR: Wang et al. as mentioned in this paper proposed a method for predicting the geological parameters in advance based on TBM real-time state monitoring data, and the R2 of the prediction results for five geological parameters are all higher than 0.98.
Journal ArticleDOI
Spatial-temporal upsampling graph convolutional network for daily long-term traffic speed prediction
TL;DR: Wang et al. as discussed by the authors proposed a spatial-temporal upsampling graph convolutional network (STUGCN) for daily long-term traffic speed prediction, which not only preserves the local spatialtemporal correlations, but also has the ability to learn global temporal correlations.
Posted ContentDOI
Inverting the Fundamental Diagram and Forecasting Boundary Conditions: How Machine Learning Can Improve Macroscopic Models for Traffic Flow
TL;DR: In this article , a machine learning model based on an LSTM recursive neural network is proposed to estimate the number of vehicles passing under a sensor in the next 30 min, which is then used to improve the accuracy of an LWR-based first-order multi-class model.
Book ChapterDOI
Short-Term Traffic Speed Prediction Using Hybrid LSTM-SVR Model
Nguyen Duc Khang Quach,Zhao Ren,Khiem Vinh Tran,Viet Hung Vu,Yong-Sik Chun,Thanh-Tam Nguyen,Jun Jo +6 more
Proceedings ArticleDOI
Aircraft Trajectory Prediction Model Based on Improved GRU Structure
TL;DR: In this paper , the Elastic-BiGRU trajectory prediction model is proposed, which combines the Smooth filtering method, the Elastic Network fitting method and the GRU structure, and the prediction accuracy of aircraft trajectory is further improved.
References
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Journal ArticleDOI
Long short-term memory
TL;DR: A novel, efficient, gradient based method called long short-term memory (LSTM) is introduced, which can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units.
Posted Content
Empirical evaluation of gated recurrent neural networks on sequence modeling
TL;DR: These advanced recurrent units that implement a gating mechanism, such as a long short-term memory (LSTM) unit and a recently proposed gated recurrent unit (GRU), are found to be comparable to LSTM.
Posted Content
On the difficulty of training Recurrent Neural Networks
TL;DR: This paper proposes a gradient norm clipping strategy to deal with exploding gradients and a soft constraint for the vanishing gradients problem and validates empirically the hypothesis and proposed solutions.
Proceedings Article
On the difficulty of training recurrent neural networks
TL;DR: In this article, a gradient norm clipping strategy is proposed to deal with the vanishing and exploding gradient problems in recurrent neural networks. But the proposed solution is limited to the case of RNNs.
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.