Understanding Dynamic Spatio-Temporal Contexts in Long Short-Term Memory for Road Traffic Speed Prediction

• URL: http://arxiv.org/abs/2112.02409v2
• Date: Sat, 17 Jun 2023 01:25:52 GMT
• Title: Understanding Dynamic Spatio-Temporal Contexts in Long Short-Term Memory for Road Traffic Speed Prediction
• Authors: Won Kyung Lee, Deuk Sin Kwon, So Young Sohn
• Abstract summary: We propose a dynamically localised long short-term memory (LSTM) model that involves both spatial and temporal dependence between roads. The LSTM model can deal with sequential data with long dependency as well as complex non-linear features. Empirical results indicated superior prediction performances of the proposed model compared to two different baseline methods.
• Score: 11.92436948211501
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reliable traffic flow prediction is crucial to creating intelligent transportation systems. Many big-data-based prediction approaches have been developed but they do not reflect complicated dynamic interactions between roads considering time and location. In this study, we propose a dynamically localised long short-term memory (LSTM) model that involves both spatial and temporal dependence between roads. To do so, we use a localised dynamic spatial weight matrix along with its dynamic variation. Moreover, the LSTM model can deal with sequential data with long dependency as well as complex non-linear features. Empirical results indicated superior prediction performances of the proposed model compared to two different baseline methods.

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