Traffic Flow Prediction through a Hybrid CLSTM Model with Multifeature Fusion

Abstract

Accurate traffic volume prediction is crucial for intelligent transportation systems to control traffic conditions and improve travel efficiency. Traditional traffic volume prediction models focus on the similarity of passenger volume patterns in historical data. However, they ignore the continuous and periodic features of traffic volume data and the deviation in traffic volume caused by external factors such as holidays and weather. This paper proposes a multifeature fusion convolutional long-short-term memory (CLSTM) model. The model is based on a convolutional neural network (CNN) and a long-short-term memory (LSTM) neural network. The CLSTM model considers time continuity as a short-term feature, daily periodicity as a long-term feature, spatial correlation between roads as a spatial feature, and environmental factors as external features. The CNN model is applied to represent the temporal and spatial features as a two-dimensional spatial-temporal matrix, and two sets of high-level features are proposed. The fully connected neural network model is used to fuse the predictions from the feature matrix and LSTM neural networks. The effectiveness of feature extraction, model design, and model sensitivity are tested using the London M25 motorway as the research object. The results illustrate that the CLSTM model enhances both prediction accuracy and model adaptability, achieving a balance between prediction efficiency and accuracy.