Freeway Incident Detection Algorithms: Fuzzy-Neural-Based and Chaotic-Based Approaches
Lawrence W. Lan
|關鍵字:||事件偵測;模糊類神經網路;模糊推論;滾動式學習過程;混沌診斷;Incident detection;Fuzzy neural network;Fuzzy inference;Rolling-trained procedure;Chaotic diagnosis|
Safety and efficiency is the most important issue in transportation. To reduce traffic flow delay, damage to life and properties and social cost, and to increase efficiency and safety in transportation system, this research focuses on non-recurring congestion and develops the automatic incident detection algorithm. Existent automatic incident detection algorithms may encounter one or some of the following difficulties: the detection performance is subject to the settings of algorithm threshold, traffic flow condition (medium and heavy traffic flows normally have higher detection rate) and the distance between two adjacent detectors; Most detection algorithms are not transferable in that parameters and thresholds of an algorithm must be recalibrated and revalidated to be valid for different locations or times; Quantity and quality of traffic flow data is subject to detector types. In dealing with the uncertain contexts, both neural networks and fuzzy inference have been proven as powerful tools. The FNN approaches have the advantages of learning capability to avoid subjectively setting of the parameters and possessing high fault tolerance due to the distributed memory of parameters separately stored on each link of the network. To capture the change in traffic dynamics through network training, this study presumes that the rolling-trained procedure in FNN might be imperative in augmenting the incident detection performance. Thus, the present research attempts to develop a rolling-trained fuzzy neural network (RTFNN) approach for freeway incident detection. Its underlying logic is to establish a proper fuzzy neural network and then adaptively adjust the network parameters using the most up-to-date traffic data in response to the prevailing traffic conditions so as to improve the detection performance over the conventional FNN approach. In practice, the complexity of traffic dynamics is characterized with uncertain and nonlinear nature. The chaos abnormality diagnosis algorithm proposed in this paper attempts to use the change in chaotic traffic parameters, including largest Lyapunov exponent, capacity dimension, correlation dimension, relative Lz complexity, Kolmogorov entropy, delay time, and Hurst exponent to examine the existence of traffic incident. The RTFNN approach was found to have the highest potential, compared with FNN approach, to achieve a better incident detection performance. The Chaotic diagnosis approach performed the best detection rate, which covering low flow condition to heavy flow condition, but it also suffered the worst false alarm rate. However, all the tests results have indicated the feasibility of attaining the real-time automatic incident detection using the fuzzy-neural-based approaches and chaotic diagnosis approach. Furthermore, these approaches are promising and, in expectation, can be integrated into the hybrid incident detection algorithm with chaotic-based approach, which has the capability of identification, for initial diagnosis and with fuzzy-neural-based, which has the capability of classification, for confirmation of incidents in future exploration.
|Appears in Collections:||Thesis|