An LSTM-Based Method with Attention Mechanism for Travel Time Prediction

Sensors
Xiangdong RanChuang Lin

Abstract

Traffic prediction is based on modeling the complex non-linear spatiotemporal traffic dynamics in road network. In recent years, Long Short-Term Memory has been applied to traffic prediction, achieving better performance. The existing Long Short-Term Memory methods for traffic prediction have two drawbacks: they do not use the departure time through the links for traffic prediction, and the way of modeling long-term dependence in time series is not direct in terms of traffic prediction. Attention mechanism is implemented by constructing a neural network according to its task and has recently demonstrated success in a wide range of tasks. In this paper, we propose an Long Short-Term Memory-based method with attention mechanism for travel time prediction. We present the proposed model in a tree structure. The proposed model substitutes a tree structure with attention mechanism for the unfold way of standard Long Short-Term Memory to construct the depth of Long Short-Term Memory and modeling long-term dependence. The attention mechanism is over the output layer of each Long Short-Term Memory unit. The departure time is used as the aspect of the attention mechanism and the attention mechanism integrates departure time into the prop...Continue Reading

References

Oct 23, 1997·Neural Computation·S Hochreiter, J Schmidhuber
Jan 1, 1994·IEEE Transactions on Neural Networks·Y BengioP Frasconi

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Citations

Oct 16, 2020·Scientific Reports·Manuel Campos-TabernerMaría Amparo Gilabert
Jun 16, 2021·Environmental Science and Pollution Research International·Yurong YangMin Gao

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Software Mentioned

ARIMA
AdaGrad
CUDA

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