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Title	Deep Spatio-Temporal Dependent Convolutional Lstm Network For Traffic Flow Prediction
ID_Doc	18083
Authors	Tang J.; Zhu R.; Wu F.; He X.; Huang J.; Zhou X.; Sun Y.
Year	2025
Published	Scientific Reports, 15, 1
DOI	http://dx.doi.org/10.1038/s41598-025-95711-6
Abstract	With the rapid development of economy, the concept of intelligent transportation system (ITS) and smart city has been mentioned. The most important part of building them is whether they can accurately predict traffic flow. An accurate traffic flow forecast can help manage traffic, plan travel paths in advance, and rationally allocate public resources such as shared bicycles. The biggest difficulty in this task is how to solve the problem of spatial imbalance and the problem of temporal imbalance. In this paper, we propose a deep learning algorithm STDConvLSTM. Firstly, for spatial features, most scholars use convolutional neural networks (with fixed kernel size) to capture. However, this does not solve the problem of spatial imbalance, i.e. each region has a different size of correlated regions (e.g., the busy area has a wider range of correlated regions). In this paper, we design a space-dependent attention mechanism, which assigns a convolutional neural network with a different kernel size to each region through attention weights. Secondly, for time features, most scholars use time series prediction models, such as recurrent neural networks and their variants. However, in the actual forecasting process, the importance of historical data in different time steps is not the same. In this paper, we design a time-dependent attention mechanism that assigns different weights to historical data to solve the time imbalance. In the end, we ran experiments on two real-world data sets and achieve good performance. © The Author(s) 2025.
Author Keywords	Convolutional LSTM; Space-dependent attention; Time-dependent attention; Traffic flow prediction

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