一种改进的用于城市主干道行驶时间短时预测的自适应指数平滑(IAES)模型(英文)

• 作 者: 李志鹏 虞鸿 刘允才 刘富强
• 机 构: Key Laboratory of Embedded System and Service Computing, Ministry of Education, Tongji University
• 出 处: 自动化学报
• 发表日期: 2008年11期
• 关键词: Travel time short-term forecasting license plate matching (LPM) exponential smoo
• 基金项目: Supported by National High Technology Research and Develop-ment Program of China (863 Program) (2007AA11Z221);Interna-tional Cooperation Project of Shanghai (08210707500);NaturalScience Foundation o

Short-term forecasting of travel time is essential for the success of intelligent transportation system. In this paper, we review the state-of-art of short-term traffic forecasting models and outline their basic ideas, related works, advantages and disadvantages of each model. An improved adaptive exponential smoothing (IAES) model is also proposed to overcome the drawbacks of the previous adaptive exponential smoothing model. Then, comparing experiments are carried out under normal traffic condition and abnormal traffic condition to evaluate the performance of four main branches of forecasting models on direct travel time data obtained by license plate matching (LPM). The results of experiments show each model seems to have its own strength and weakness. The forecasting performance of IASE is superior to other models in shorter forecasting horizon (one and two step forecasting) and the IASE is capable of dealing with all kind of traffic conditions.