摘要
对随机性较强的公路交通事故,提出了采用灰色残差模型来减小预测误差,模型用多个指数函数的线性叠加来分段描述事故预测值,克服了灰色模型只能描述单调变化的过程,仅适用于预测具有较强指数变化规律事故序列的缺点。对影响灰色残差建模的修正对象、数据选取、数据预处理和修正方法等多种因素作了详细分析和划分,并归纳出4种实用的灰色残差模型。实例分析结果表明:与灰色模型相比,各种灰色残差模型的预测误差降低了70%～80%,其中采用残差直接建模、一次还原的灰色残差模型,复杂程度低,预测误差小于5%。
Grey residual error model （GREM） was proposed to improve the prediction accuracy of highway traffic accidents with high randomicity. GREM was used to depict the accident prediction values piecewise with the linear superposition of multiple exponential functions, and it overcomed the shortcomings of grey model（GM）. GM was only able to describe the monotonous change process and only could be used to estimate the sequences consistent with obvious exponential law. The manifold factors affecting GREM were analyzed and concluded in detail, including amending objects, data selection, data pretreatment and amending methods. Then, 4 practical GREMs were summed up. Analysis result indicates that in comparison with GM, all GREMs＇ predicting errors reduce by 70%- 80%, and the one modeled by residual error directly and restored only one time has low complexity and its predicting error is less than 5%.
出处
《交通运输工程学报》
EI
CSCD
北大核心
2009年第5期88-93,共6页
Journal of Traffic and Transportation Engineering
基金
重庆市教委科技计划项目(KJ090619)
重庆市九龙坡区科技计划项目(2007Q76)
关键词
交通安全
事故预测
残差
灰色残差模型
traffic safety
accident foreeast
residual error
gray residual error model
