短期交通量變化：混沌特性之探索

SHORT-TERM TRAFFIC FLOW DYNAMICS: A CHAOTIC INVESTIGATION

本研究利用里亞普諾夫指數（Lyapunov exponents）混沌檢驗法及細胞自動機（cellular automaton；CA）分析技術檢驗市區道路短期交通量時間序列之變化是否屬於隨機現象或具有混沌現象，並選取台北市數個路段每五分鐘、十分鐘、十五分鐘交通量進行實證。結果顯示短期交通量時間序列具有明顯的非線性相關性跡象，且混沌時間序列可以成功解釋此種非線性結構。在預測準確度方面，實證結果亦顯示混沌時間序列模型優於隨機時間序列ARIMA模型。

In this study, Lyapunov exponents and cellular automaton (CA) are applied to examine whether the short-term traffic flows on urban streets display stochastic or chaotic phenomena. Empirical tests were conducted out of five, ten and fifteen minutes' traffic flow rates that are observed from some selected roadways in Taipei. We found strong evidence of nonlinear dependence in the short-term traffic flow dynamics and that chaotic time series model can successfully explain such nonlinear structures. In comparison with the conventional integrated autoregressive-moving average model (ARIMA), we also found that the chaos time series model obviously outperforms on the prediction accuracy of such short-term traffic flow dynamics.