Dynamic Traffic Flow Prediction Based on Long-Short Term Memory Framework With Feature Organization

Traffic flow is an important piece of information for traffic management and control. In particular, the dynamic prediction of traffic flow provides the basis for efficient control measures. The existing studies focus on improving the prediction accuracy by integrating the long short-term memory (LSTM) into various complex frameworks without paying attention to the feature engineering, which has a significant impact on the performance of machine learning methods. In this article, we propose a dynamic traffic flow prediction approach based on the LSTM framework with different feature organizations: feature division modes and feature selection. The feature division modes consider the periodicity of traffic flow by intervals (e.g., 5 min) and periods (e.g., daily). The feature selection determines different types of features as inputs to the prediction model. The impact of different feature organization strategies on the prediction accuracy is investigated using field data collected by the Caltrans Performance Measurement System. Two types of LSTM frameworks, the fully connected LSTM and the sequence-to-sequence LSTM (seq2seq-LSTM), are used to evaluate the performance of the proposed prediction approach. The results show that the seq2seq-LSTM model with optimized feature organization can significantly improve the prediction performance.