[1] Huang Kun, Chen Senfa, Zhou Zhenguo, Qi Xia, et al. Research on a non-linear chaotic prediction modelfor urban traffic flow [J]. Journal of Southeast University (English Edition), 2003, 19 (4): 410-413. [doi:10.3969/j.issn.1003-7985.2003.04.022]
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Research on a non-linear chaotic prediction modelfor urban traffic flow(
)
城市交通流量的非线性混沌预测模型研究
)Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]
- Volumn:
- 19
- Issue:
- 2003 4
- Page:
- 410-413
- Research Field:
- Traffic and Transportation Engineering
- Publishing date:
- 2003-12-30
Info
- Title:
- Research on a non-linear chaotic prediction modelfor urban traffic flow
- 城市交通流量的非线性混沌预测模型研究
- Author(s):
- Huang Kun; Chen Senfa; Zhou Zhenguo; Qi Xia
- College of Economics and Management, Southeast University, Nanjing 210096, China
- Keywords:
- traffic flow; chaotic theory; phase reconstruction; non-linear; genetic algorithm; prediction model
- PACS:
- U491.14
- DOI:
- 10.3969/j.issn.1003-7985.2003.04.022
- Abstract:
- In order to solve serious urban transport problems, according to the proved chaotic characteristic of traffic flow, a non-linear chaotic model to analyze the time series of traffic flow is proposed. This model reconstructs the time series of traffic flow in the phase space firstly, and the correlative information in the traffic flow is extracted richly, on the basis of it, a predicted equation for the reconstructed information is established by using chaotic theory, and for the purpose of obtaining the optimal predicted results, recognition and optimization to the model parameters are done by using genetic algorithm. Practical prediction research of urban traffic flow shows that this model has famous predicted precision, and it can provide exact reference for urban traffic programming and control.
- 为了解决日益严重的城市交通问题, 本文根据交通流已被证明的混沌特性, 尝试采用非线性混沌模型来分析交通流时间序列.该模型首先将交通流时间序列在相空间中重构, 以充分提取交通流中的相关信息, 在此基础上, 应用混沌理论对重构信息构建了预测方程, 并运用遗传算法对模型参数进行了优化辨识, 以获得最佳的预测效果.实际的城市交通流量预测研究表明, 该模型具有较高的预测精度, 可以为城市交通规划和控制提供准确的参考.
References:
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Memo
- Memo:
- Biographies: Huang Kun(1973—), male, graduate; Chen Senfa(corresponding author), male, professor.