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.

For intelligent transportation systems (ITSs), reliable and accurate real-time traffic flow prediction is an important step and a necessary prerequisite for alleviating traffic congestion and improving highway operation efficiency. In this paper, we propose an improved hybrid predicting model including two steps: decomposition and prediction to predict highway traffic flow. First, we adopted the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method to adaptively decompose the original nonlinear, nonstationary, and complex highway traffic flow data. Then, we used the improved weighted permutation entropy (IWPE) to obtain new reconstructed components. In the prediction step, we used the gray wolf optimizer (GWO) algorithm to optimize the least-squares support vector machine (LSSVM) prediction model established for each reconstruction component and integrate the prediction results of each subsequence to obtain the final prediction result. We experimentally validated the effectiveness of the proposed approach. The research results reveal that the proposed model is useful for predicting traffic flow and its changing trends and also allowing transportation officials to make more effective traffic decisions.

Globally, traffic accidents on the highway network contribute significantly to a high fatality rate, drawing considerable attention from health institutions. The efficiency of transportation plays a vital role in mitigating the severe consequences of these incidents. This study delves into the issues of emergency vehicles experiencing delays despite having priority. Therefore, we construct mixed-integer linear programming with semi-soft time windows (MIPSSTW) model for optimizing emergency vehicle routing in highway incidents. We analyze the time-varying and complex traffic situations and respectively propose corresponding estimation approaches for the travel time of road segments, intersections on the urban road network, and ramp-weave sections on the highway network. Furthermore, we developed a modified cuckoo search(MCS) algorithm to solve this combinatorial problem. Optimization strategies of Lévy flight and dynamic inertial weight strategy are introduced to strengthen the exploration capability and the diversity of solution space of the CS algorithm. Computational experiments based on the Chinese emergency medical system data are designed to validate the efficacy and effectiveness of the MIPSSTW model and MCS algorithm. The results show that our works succeed in searching for high-quality solutions for emergency vehicle routing problems and enhance the efficacy of strategic decision-making processes in the realm of incident management and emergency response systems.