CAJ | 학술논문

本文研究短时交通流预测。短时交通流预测是智能交通系统研究和实践的必要基础。本文提出和建立了一个短时交通流量预测模型，该模型利用一个基于规则的模糊系统，非线性地组合BP 神经网络模型和自适应卡尔曼滤波模型的交通流量预测结果，使得短时交通流量的预测结果更加准确可靠。该模型将传统方法和人工智能方法有机结合，一方面，利用人工神经网络强大的动态非线性映射能力，从而提高预测精度；另一方面，充分发挥卡尔曼滤波的静态线性稳定性，解决了单独使用BP神经网络进行预测时识别率不理想和可信度不高的问题。实验结果表明，本文提出的短时交通流预测模型具有较高的准确度和可靠度。
본문연구단시교통류예측。단시교통류예측시지능교통계통연구화실천적필요기출。본문제출화건립료일개단시교통류량예측모형，해모형이용일개기우규칙적모호계통，비선성지조합BP 신경망락모형화자괄응잡이만려파모형적교통류량예측결과，사득단시교통류량적예측결과경가준학가고。해모형장전통방법화인공지능방법유궤결합，일방면，이용인공신경망락강대적동태비선성영사능력，종이제고예측정도；령일방면，충분발휘잡이만려파적정태선성은정성，해결료단독사용BP신경망락진행예측시식별솔불이상화가신도불고적문제。실험결과표명，본문제출적단시교통류예측모형구유교고적준학도화가고도。
For the research and practice of modern intelligent transportation systems, short-term traffic flow prediction is an essential element. The main content of this paper is to establish a traffic prediction model for short-term traffic flow forecasting , using a rule-based fuzzy system, nonlinearly combine traffic flow forecasts resulting from an adaptive Kalman filter ( KF) and BP neural network model, which is referred as KBF model . Organic combination of traditional methods and artificial intelligence methods, on one hand, makes use of the powerful dynamic nonlinear mapping ability of artificial neural network, so as to improve the prediction accuracy;On the other hand, takes full advantages of the static linear sta-bility of the Kalman filter to solve the problem that the forecasts recognition rate is not satisfactory and the credibility is not high while using a BP neural network only. Verified by experiments, this model is useful for traffic flow forecasting with high accuracy and high reliability.