基于自适应灰色区间模型的短时交通流不确定预测  

Uncertainty prediction of short-term traffic flow based on adaptive grey interval model

作　　者：黎豪 LI Hao(Civil Engineering College,Shaanxi Polytechnic Institute,Xianyang 712000,China)

机构地区：[1]陕西工业职业技术学院土木工程学院,陕西咸阳712000

出　　处：《甘肃科学学报》2024年第6期114-120,共7页Journal of Gansu Sciences

基　　金：陕西省教育厅科学研究计划项目(23JK0312)。

摘　　要：为了更好地捕捉交通流随机波动的不确定性,提升短时交通流预测的准确性,进而为交通管理提供科学依据,提出一种自适应灰色区间模型,用于短时交通流的不确定预测。该自适应灰色区间模型由自适应灰色模型、粒子群算法和残差模型组成。首先,构建自适应灰色模型,预测短时交通流的均值,采用粒子群优化算法来实时获取自适应灰色模型的最优参数;然后,通过比较均值预测结果与真实值,得到残差序列,并对得到的残差序列进行绝对值处理,采用残差模型对经过绝对值处理的残差序列进行处理;最后,将均值预测结果与残差结果相结合,生成预测区间,实现对短时交通流的不确定量化。利用美国明尼苏达州高速公路采集的交通流数据对所提模型的性能进行评价,选择预测区间覆盖概率、预测区间宽度和综合指数作为不确定预测性能评价指标,并与灰色包络模型(GEPM)、灰色区间预测模型(GIPM)和线性灰色区间模型(LGIM)进行比较。结果表明,本文模型能够生成可行的交通流预测区间,通过比较不确定预测性能评价指标,表明本文模型有更好的预测精度,可以为智能交通系统提供决策支持。In order to better capture the uncertainty of random fluctuations in traffic flow and improve the accuracy of short-term traffic flow prediction,an adaptive grey interval model is proposed in this paper for uncertain prediction of short-term traffic flow.The adaptive grey interval model consists of an adaptive grey model,a particle swarm optimization algorithm and a residual model.First,an adaptive grey model is constructed to predict the mean of short-term traffic flow.The particle swarm optimization algorithm is used to obtain the optimal parameters of the adaptive grey model in real time.Then,the residual sequence is obtained by comparing the mean prediction result with the true value,the obtained residual sequence is processed by absolute value,and the residual model is used to process the residual sequence processed by absolute value.Finally,the mean prediction result is combined with the residual result to generate a prediction interval to realize the uncertainty quantification of short-term traffic flow.The performance of the proposed model is evaluated using traffic flow data collected from highways in Minnesota,USA.The prediction interval coverage probability,prediction interval width and comprehensive index are selected as uncertainty prediction performance evaluation indicators,and compared with the grey envelope model(GEPM),grey interval prediction model(GIPM) and linear grey interval model(LGIM).The results show that the proposed model can generate feasible traffic flow prediction intervals.By comparing the uncertain prediction performance evaluation indicators,it is shown that the proposed model has better prediction accuracy and can provide decision support for intelligent transportation systems.

关 键 词：智能交通 短时交通流预测 不确定预测 自适应灰色区间模型 粒子群优化 

分 类 号：U491.1[交通运输工程—交通运输规划与管理]