CAJ | 학술논문

现代交通系统结构复杂，涉及的数据类型和数量众多，模糊性、随机性和不确定性等因素的存在增加了数据分析过程中定性与定量综合集成的难度。本文对城市交通流预测进行了研究，根据云模型和自组织神经网络的特点，构建了云-自组织神经网络交通流预测模型。该预测模型运用云模型处理数据的模糊性和随机性问题的优势，提高了自组织神经网络预测中学习样本数据的可靠性。通过对某城区的实际数据进行对比测算，改进的预测模型比单纯使用自组织神经网络预测模型决定系数更高。结果表明，本文提出的模型在交通流预测中提高了准确率，降低了预测泛化误差。
현대교통계통결구복잡，섭급적수거류형화수량음다，모호성、수궤성화불학정성등인소적존재증가료수거분석과정중정성여정량종합집성적난도。본문대성시교통류예측진행료연구，근거운모형화자조직신경망락적특점，구건료운-자조직신경망락교통류예측모형。해예측모형운용운모형처리수거적모호성화수궤성문제적우세，제고료자조직신경망락예측중학습양본수거적가고성。통과대모성구적실제수거진행대비측산，개진적예측모형비단순사용자조직신경망락예측모형결정계수경고。결과표명，본문제출적모형재교통류예측중제고료준학솔，강저료예측범화오차。
Modern transportation systems have complex structure, and the existence of fuzzy, stochastic and uncertainty factors increase the difficulty of huge data involved in qualitative and quantitative integrated analysis. This paper developed the cloud neural network self-organization of traffic flow forecasting model based on the characteristics of cloud model and self-organizing neural network. Using cloud model fuzziness and randomness advantages, the paper proposed the prediction model that can improve the reliability of self-organizing neural network prediction learning sample data to process data problems. Through comparing two models to a city traffic flow forecasting with actual data, the paper found that the forecasting model has high-er coefficient of determination than the only using of self-organizing neural network. The results show that the model proposed in the traffic flow forecasting can improve accuracy and reduce generalization error.