Explanatory prediction of traffic congestion propagation mode: A self-attention based approach

Qingchao Liu, Tao Liu, Yingfeng Cai, Xiaoxia Xiong, Haobin Jiang, Hai Wang and Ziniu Hu

Physica A: Statistical Mechanics and its Applications, 2021, vol. 573, issue C

Abstract: Short-term traffic flow forecasting, an important component of intelligent transportation systems (ITS), is a challenging research direction as forecasting itself is affected by a series of complex factors. As more and more attention is paid to the data itself, deep learning methods have attained mainstream popularity for accomplishing traffic flow prediction tasks. In recent years, the attention mechanism has been widely used in various fields thanks to its excellent result interpretation ability and its capability to improve the performance of neural network models. In terms of time series data prediction, LSTM has demonstrated its powerful time feature extraction capability. Because of its ability to efficiently and quickly extract spatial–temporal features, CNN is often used in combination with LSTM and attention mechanisms to obtain accurate traffic flow prediction forecast results. In this paper, we propose a short-term traffic flow prediction model based on self-attention, and test the performance of the model experimentally with real data. The model can achieve the best prediction results compared with other classical models. In addition, the temporal and spatial features extracted by the model have certain physical characteristics making results easier to interpret.

Keywords: Short-term traffic flow prediction; Traffic congestion propagation; Self-attention model; CNN; LSTM (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:573:y:2021:i:c:s0378437121002120

DOI: 10.1016/j.physa.2021.125940

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