A Hybrid Deep Learning Model for Short-Term Traffic Flow Pre-Diction Considering Spatiotemporal Features

Abstract

Traffic flow prediction is one of the basic, key problems with developing an intelligent transportation system since accurate and timely traffic flow prediction can provide information support and decision support for traffic control and guidance. However, due to the complex characteristics of traffic information, it is still a challenging task. This paper proposes a novel hybrid deep learning model for short-term traffic flow prediction by considering the inherent features of traffic data. The proposed model consists of three components: the recent, daily and weekly components. The recent component is integrated with an improved graph convolutional network (GCN) and bi-directional LSTM (Bi-LSTM). It is designed to capture spatiotemporal features. The remaining two components are built by multi-layer Bi-LSTM. They are developed to extract the periodic features. The proposed model focus on the important information by using an attention mechanism. We tested the performance of our model with a real-world traffic dataset and the experimental results indicate that our model has better prediction performance than those developed previously.

Prediction of Course Grades in Computer Science Higher Education Program via a Combination of Loss Functions in LSTM Model

“…See Table 2 for a comprehensive overview of these approaches. The mentioned table demonstrates LSTM's efficacy in predicting student performance by surpassing baseline models, such as SVM and MLP, with superior accuracy [30]. LSTM's adeptness in handling sequential data and retaining long-term information enhances its adaptability to the complexities of educational data, resulting in enhanced predictions [29].…”

“…These methods excel in capturing complex time-dependent relationships, as highlighted in Table 1. Academic performance forecasting is commonly achieved through grade prediction using standard machine learning (e.g., MLP, SVM) and time series (e.g., LSTM, GRU) methods [30], [33]. Notably, LSTM consistently outperforms other models in terms of precision, Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) due to its effective capture of complex temporal dependencies in educational data [1].…”

EVHF-GCN: An Emergency Vehicle Priority Scheduling Model Based on Heterogeneous Feature Fusion With Graph Convolutional Networks

“…•Bi-LSTM [32]: This study introduces a novel hybrid deep learning framework that incorporates GCN to capture the spatiotemporal dependency and periodicity of traffic data. The proposed model partitions the time series into recent, daily cycle, and weekly cycle components.…”

Towards a new intelligent traffic system based on deep learning and data integration

“…Before adding it as input to the cell through the self-recurrent connection, the forget gate scales the internal state of the cell [18], [19]. To simulate the internal calculating process of the LSTM, Zhou et al described four phases in [20]. With, 𝑋 𝑡 is the input value of the LSTM cell at time 𝑡, 𝐶 𝑡 is the state value memory cell at time 𝑡, ℎ 𝑡 is the output value at time 𝑡, σ denotes the 𝑠𝑖𝑔𝑚𝑜𝑖𝑑 activation function and 𝑡𝑎𝑛ℎ means the 𝑡𝑎𝑛ℎ activation function.…”