Search

Short-term traffic flow prediction is one of the most important issues in the field of intelligent transport system (ITS). Because of the uncertainty and nonlinearity, short-term traffic flow prediction is a challenging task. In order to improve the accuracy of short-time traffic flow prediction, a hybrid model (SSA-KELM) is proposed based on singular spectrum analysis (SSA) and kernel extreme learning machine (KELM). SSA is used to filter out the noise of traffic flow time series. Then, the filtered traffic flow data is used to train KELM model, the optimal input form of the proposed model is determined by phase space reconstruction, and parameters of the model are optimized by gravitational search algorithm (GSA). Finally, case validation is carried out using the measured data of an expressway in Xiamen, China. And the SSA-KELM model is compared with several well-known prediction models, including support vector machine, extreme learning machine, and single KLEM model. The experimental results demonstrate that performance of the proposed model is superior to that of the comparison models. Apart from accuracy improvement, the proposed model is more robust., Competing Interests: The authors have declared that no competing interests exist.

The paper adopts the improved ViBe algorithm, and initializes the background sample according to the original algorithm. Using the idea of image binarization, the current frame image is subjected to the foreground detection of the traditional ViBe, and then the second judgment is made according to the number of times the target is judged, and the original is changed. The fixed background factor in the algorithm background update strategy increases the background update probability during shadow elimination. Experiments show that the method can eliminate shadows in time and improve the accuracy of motion vehicle detection in different scenarios. At the same time, the paper designs a multi-lane traffic flow monitoring system based on improved ViBe algorithm combined with IoT ZigBee technology. The flow monitoring accuracy of the algorithm is verified. [ABSTRACT FROM AUTHOR]