CAJ | 학술논문

路段行程时间超前预报是动态交通诱导方案制定的基础，应用短时交通预测的方法可以获得将来某个时刻的路段行程时间数据，但已有研究成果，还存在适应性不强，计算量大，基础数据需求多等不足。应用谱分析及Karhunen-Loeve变换对随机序列的分解与重构功能，通过挖掘路段历史行程时间序列与当前检测行程时间序列的相似性特征进行序列重构，实现对后一时段路段行程时间的预测，结果显示，该方法具有良好的预测精度。
로단행정시간초전예보시동태교통유도방안제정적기출，응용단시교통예측적방법가이획득장래모개시각적로단행정시간수거，단이유연구성과，환존재괄응성불강，계산량대，기출수거수구다등불족。응용보분석급Karhunen-Loeve변환대수궤서렬적분해여중구공능，통과알굴로단역사행정시간서렬여당전검측행정시간서렬적상사성특정진행서렬중구，실현대후일시단로단행정시간적예측，결과현시，해방법구유량호적예측정도。
Road travel time prediction is a crucial foundation for dynamic traffic guidance and the short traffic fore?casting method can be applied to get near future travel time of the road. But the previously developed models have some deficiency, such as bad adaptability, large amount of calculation needing and history data requirement. This study is to develop a model that can estimate the road travel time by using spectral analysis and Karhunen-Loeve transformation. Karhunen-Loeve transformation can decompose and restructure random sequences, utilize the sim?ilarity of current detection series and history series, and then realize estimation of the following travel time series by means of reconstruction. The case study suggests that the proposed method has a good performance.