Queue Length Prediction Method of Short-linked Intersections Based on Long Short-Term Memory

To address traffic congestion and vehicle queue overflow at short-link intersections, it is essential to conduct a predictive analysis of vehicle queue length between these intersections. This helps in proactively implementing appropriate traffic management measures to prevent traffic overflow and congestion. In this paper, we propose a short-link intersection vehicle queue length prediction method based on Long Short-Term Memory (LSTM). First, historical traffic data from the morning peak hours over the past month is collected. After comparing multiple machine learning models, LSTM is chosen for traffic flow prediction. Subsequently, mathematical models are used to predict queue length at future time intervals. Through practical validation, we found that the proposed method for predicting queue length between short-link intersections in this paper is effective, with an average prediction error rate of 11.9%. It meets the requirements for predicting vehicle queue length between intersections and proactively controlling them, holding significant practical significance for preventing traffic flow and congestion events.