关键词: 智能交通, 短时交通状态预测, 混沌理论, 多维时间序列, 粒子群优化算法

Abstract: Short-term traffic flow forecasting is not only a core element of intelligent transportation system but also plays an important role in traffic information service and traffic guidance. Short-term traffic flow forecasting is highlighted and has become reality in the light of the fact that the feasibility of data collection and travelers’ expectation of high travel quality. A short-term traffic flow forecasting model of multiple road cross-sections in a road network based on chaotic time series analysis is proposed in this paper considering the chaotic and relative character of traffic state. The parameters of the forecasting model are compared with single road cross-sections based on the chaotic time series analysis and can be obtained by particle swarm optimization algorithm. Meanwhile, the proposed forecasting models and methods of multiple road cross-sections in a road network were tested by real-time traffic flow data from urban expressway and the results were satisfying.

Key words: intelligent transportation system, short-term forecast, chaotic theory, multidimensional time series, particle swarm optimization