Abstract
Reliable and accurate real-time traffic flow state identification is crucial for an intelligent transportation system (ITS). This identification is a prerequisite for alleviating traffic congestion and improving highway operation efficiency. In this paper, we propose an improved traffic flow state identification model that is based on selective ensemble learning (SEL). First, we adopted the fuzzy C-means (FCM) clustering method to divide the traffic flow data into three main kinds of traffic flow states and obtained the parameters that correspond to each kind of traffic flow state. Second, we applied the random subspace (RS) algorithm as the ensemble method and support vector machine (SVM) model as base learners to construct the RS-SVM ensemble model for traffic flow identification. Significantly, the discrete binary particle swarm optimization (BPSO) algorithm with global optimization search ability was employed to select the classifiers obtained by the random subspace training in the ensemble system. We experimentally validated the effectiveness of the proposed BPSO–RS-SVM-SEL approach. The research results reveal that compared with other classical traffic flow state identification methods, the proposed model has a higher maximum accuracy of 98.68%. It can be seen that our model improves the classification accuracy of traffic flow state identification and the difference in the ensemble system to a certain extent.
Highlights
With the continuous expansion of highway networks and the increase in the number of vehicles in cities and on highways, the traffic environment is deteriorating and traffic congestion is worsening
The model combines unsupervised learning with supervised learning to effectively improve the accuracy of classification and reduces the complexity in training
The fuzzy Cmeans (FCM) clustering method was employed to divide the original traffic flow data into three kinds of traffic flow states and obtained the parameters that correspond to each kind of traffic state
Summary
With the continuous expansion of highway networks and the increase in the number of vehicles in cities and on highways, the traffic environment is deteriorating and traffic congestion is worsening. Obtaining accurate and timely traffic state information is necessary for individual travelers and related managers. With the current explosion of traffic flow data, identifying traffic flow states with big data technology is crucial to ensure safe travel and develop a superefficient navigation design, which may help travelers make informed travel decisions and maximize the efficiency of the limited network space and time resources. A variety of effective traffic flow state identification methods have been developed. Wang et al built a model of general stochastic macroscopic traffic flow and proposed an approach to the real-time estimation of the complete traffic flow state on freeway segments based on the extended Kalman filter[6]. Billot et al combined online traffic state estimation within a
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