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Urban Expressway Travel Time Prediction Method Based on Fuzzy Adaptive Kalman Filter |
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PP: 625-630 |
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Author(s) |
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Yanguo Huang,
Lunhui Xu,
Qiang Luo,
Xianyan Kuang,
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Abstract |
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| According to the poor adaptive ability of traditional filter algorithm in the estimation for traffic state and travel time with
Kalman filter, an improved fuzzy adaptive Kalman filtering method was proposed. The new interest of observation noise was defined,
and the fuzzy logic was used to adjust the importance weights of system noise and observation noise through on-line monitoring the
interest changes, which changed the trust and utilization degree of the model for the observation information, and this made the filter
eventually tend to be stable. To guarantee the real-time performance of system, a direct input - output fuzzy membership function
matching method was put forward to take the place of fuzzy reasoning. The method was tested on the urban expressway in Guangzhou
by using real-time detection data, and the result show that the traffic state estimation model had better tracking ability than conventional
Kalman filter, and results of travel time prediction show that there was a slight difference between the prediction value and that of actual
observation in free traffic flow state, and the relative error was under 15% in traffic congested state. The precision and applicability of
this method were acceptable, and it can be used to provide a basis for travel time of urban expressway in traffic control and guidance
system. |
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