Accurate traffic flow data is crucial for traffic control and management in an intelligent transportation system (ITS), and thus traffic flow prediction research attracts significant attention in the transportation community. Previous studies have suggested that raw traffic flow data may be contaminated by noises caused by unexpected reasons (e.g., loop detector damage, roadway maintenance, etc.), which may degrade traffic flow prediction accuracy. To address this issue, we proposed an ensemble framework via ensemble empirical mode decomposition (EEMD) and artificial neural network (ANN) to predict traffic flow under different time intervals ahead. More specifically, the proposed framework firstly employed the EEMD model to suppress the noises in the raw traffic data, which were then processed to predict traffic flow at time steps under different time scales (i.e., 1, 2, and 10 min). We verified our model performance on three loop detectors’ data, which were supported by the Department of Transportation, Minnesota. The research findings can help traffic participants collect more accurate traffic flow data and thus benefits transportation practitioners by helping them to make more reasonable traffic decisions.
The status of the Pantograph and Catenary is the guarantee for the safe operation of the railway. However, the traditional Pantograph and Catenary status judgment efficiency is not satisfactory, and it is impossible to timely repair the catenary, which may lead to the greater economic loss. In this paper, a new GA-ADNN-based (genetic algorithm-Adadelta deep neural network-based) optimization method for the prediction model for catenary comprehensive pantograph and catenary monitor (CPCM) status is proposed. According to the status values of the CPCM parameters such as height, stagger, hard point, contact force, and height difference within span, the status value of the pillars in the catenary has been calculated by the analytic hierarchy process, and then the prediction model for predicting catenary CPCM status has been established and then optimized by genetic algorithm to avoid prediction model falling into local optimum. Finally, the CPCM test parameters of each pillar of the catenary in the actual example are input and the CPCM status value of the corresponding pillar is predicted. With the smallest prediction error found, the genetic algorithm is used for optimization, the optimal learning rate of the prediction model is 0.0559, and the optimal number of the hidden layer of the CPCM status prediction model is determined to be 14. The experimental results show the feasibility of GA-ADNN-based prediction model for predicting the catenary CPCM status, and that compared with the support vector machine and traditional artificial neural network prediction methods, the GA-ADNN-based prediction model has higher prediction precision and better generalization ability.
The location selection of logistics distribution centers is a crucial issue in the modern urban logistics system. In order to achieve a more reasonable solution, an effective optimization algorithm is indispensable. In this paper, a new hybrid optimization algorithm named cuckoo search-differential evolution (CSDE) is proposed for logistics distribution center location problem. Differential evolution (DE) is incorporated into cuckoo search (CS) to improve the local searching ability of the algorithm. The CSDE evolves with a coevolutionary mechanism, which combines the Lévy flight of CS with the mutation operation of DE to generate solutions. In addition, the mutation operation of DE is modified dynamically. The mutation operation of DE varies under different searching stages. The proposed CSDE algorithm is tested on 10 benchmarking functions and applied in solving a logistics distribution center location problem. The performance of the CSDE is compared with several metaheuristic algorithms via the best solution, mean solution, and convergence speed. Experimental results show that CSDE performs better than or equal to CS, ICS, and some other metaheuristic algorithms, which reveals that the proposed CSDE is an effective and competitive algorithm for solving the logistics distribution center location problem.
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