The Traffic Flow Prediction Method Using the Incremental Learning-Based CNN-LTSM Model: The Solution of Mobile Application

Abstract

With the acceleration of urbanization and the increase in the number of motor vehicles, more and more social problems such as traffic congestion have emerged. Accordingly, efficient and accurate traffic flow prediction has become a research hot spot in the field of intelligent transportation. However, traditional machine learning algorithms cannot further optimize the model with the increase of the data scale, and the deep learning algorithms perform poorly in mobile application or real-time application; how to train and update deep learning models efficiently and accurately is still an urgent problem since they require huge computation resources and time costs. Therefore, an incremental learning-based CNN-LTSM model, IL-TFNet, is proposed for traffic flow prediction in this study. The lightweight convolution neural network-based model architecture is designed to process spatiotemporal and external environment features simultaneously to improve the prediction performance and prediction efficiency of the model. Especially, the K-means clustering algorithm is applied as an uncertainty feature to extract unknown traffic accident information. During the model training, instead of the traditional batch learning algorithm, the incremental learning algorithm is applied to reduce the cost of updating the model and satisfy the requirements of high real-time performance and low computational overhead in short-term traffic prediction. Furthermore, the idea of combining incremental learning with active learning is proposed to fine-tune the prediction model to improve prediction accuracy in special situations. Experiments have proved that compared with other traffic flow prediction models, the IL-TFNet model performs well in short-term traffic flow prediction.

Traffic Flow Prediction Using Big Data and Geographic Information Systems: A Survey of Data Sources, Frameworks, Challenges, and Opportunities

“…The results of this work will be used to examine transportation data for a major Colombian metropolis. For traffic flow prediction, the authors of [104] proposed a CNN-LTSM model trained using incremental learning (IL-TFNet). The forecasting performance and efficiency of the model have been optimized using a lightweight CNN-based model architecture.…”

“…[102] CNN RMSE: 49, Accuracy: 92.3% [103] BDA Will be used to examine transportation data for Colombian metropolis. [104] CNN, K-means clustering, and LTSM. High real-time performance, Low computational overhead.…”

Traffic Flow Prediction Research Based on an Interactive Dynamic Spatial–Temporal Graph Convolutional Probabilistic Sparse Attention Mechanism (IDG-PSAtt)

“…To capture spatial-temporal correlations, Graph WaveNet [3] adapts both temporal convolutional networks (TCNs) and adaptive graph convolution. SLCNN [4] adapts dynamic graph convolution and a 1D convolutional neural network (CNN) [5] for The ability to anticipate traffic flows using traditional machine learning techniques is severely constrained by their excessive reliance on feature engineering. Instead, deep learning-based approaches, which are frequently employed in traffic flow prediction applications, can efficiently and automatically extract data that describe traffic flow parameters.…”

“…To capture spatial-temporal correlations, Graph WaveNet [3] adapts both temporal convolutional networks (TCNs) and adaptive graph convolution. SLCNN [4] adapts dynamic graph convolution and a 1D convolutional neural network (CNN) [5] for exploring the spatial-temporal features between traffic flow nodes. STFGNN [6] utilizes multiple GCNs and 1D CNNs [7] to simultaneously extract spatial and temporal correlations.…”

Traffic Speed Prediction Method Based on Spatiotemporal Sampling and LSTM

“…In the past few decades, machine learning and deep learning rapid development have made Recurrent Neural Networks (RNNs) and their variations important tools in the field of traffic prediction [1]. Among them, Long Short-Term Memory (LSTM), as a special RNN architecture, has been widely used in sequence modeling and time series prediction tasks [2]. The LSTM network is known for its ability to capture long-term dependencies in time series data by utilizing memory units and gate mechanisms.…”