The invention discloses a traffic flow prediction and signal adjustment method based on machine learning. The method comprises the following steps: data acquisition: acquiring real-time traffic data; data processing: carrying out standardization processing on the traffic flow data and external data; machine learning model training: training the processed multi-modal data through a model; traffic flow prediction: predicting traffic flow change in a period of time in the future according to the current multi-modal data; and signal adjustment: adjusting the traffic signal of each intersection based on the predicted future traffic flow. According to the method, deep learning training is carried out on multi-modal data including traffic flow, weather, emergencies and the like, so that the model can more accurately capture the trend and rule of traffic flow change, and the prediction accuracy is remarkably improved. The system can flexibly adjust the signal period and the green light duration according to the real-time data, so that the traffic signal control has better adaptability, the instantaneous fluctuation of the traffic flow can be effectively dealt with, and the overall efficiency of the traffic system is improved.
本发明公开了一种基于机器学习的交通流量预测与信号调节方法,包括:数据采集:对实时交通数据进行采集;数据处理:对交通流量数据及外部数据进行标准化处理;机器学习模型训练:将处理后的多模态数据通过模型进行训练;交通流量预测:根据当前的多模态数据预测未来一段时间内的交通流量变化;信号调节:基于预测的未来交通流量,调整个路口的交通信号。本发明通过对多模态数据包括交通流量、天气、突发事件等进行深度学习训练,模型能够更精准地捕捉交通流量变化的趋势和规律,显著提高预测的准确性。系统能够根据实时数据灵活调整信号周期和绿灯时长,使交通信号控制具有更好的适应性,能有效应对交通流量的瞬时波动,提升交通系统的整体效率。
Traffic flow prediction and signal adjustment method based on machine learning
一种基于机器学习的交通流量预测与信号调节方法
25.02.2025
Patent
Elektronische Ressource
Chinesisch
IPC: | G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS |
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