As an important supplement of conventional fixed-route public transit, flexible transit services draw more attention in low-demand areas recently. Demand-responsive connector is one of those flexible transit services and has already been operated as feeder transit in some cities. This article concentrates on the vehicle routing operation of the demandresponsive connector system with on-demand stations. A two-stage routing model is proposed to minimize the system cost, considering both the service provider and riders, and two solution algorithms are proposed and compared for the routing model. A simulation experiment based on a real-life case study in Nanjing city is conducted to demonstrate the applicability of the proposed vehicle routing model. The results demonstrate that our vehicle routing modeling and optimized algorithm can confidently handle the daily operation of demand-responsive connector services with on-demand stations.

Multiscale fluctuation dispersion entropy (MFDE) has been proposed to measure the dynamic features of complex signals recently. Compared with multiscale sample entropy (MSE) and multiscale fuzzy entropy (MFE), MFDE has higher calculation efficiency and better performance to extract fault features. However, when conducting multiscale analysis, as the scale factor increases, MFDE will become unstable. To solve this problem, refined composite multiscale fluctuation dispersion entropy (RCMFDE) is proposed and used to improve the stability of MFDE. And a new fault diagnosis method for hydraulic pumps using particle swarm optimization variational mode decomposition (PSO-VMD) and RCMFDE is proposed in this paper. Firstly, PSO-VMD is adopted to process the original vibration signals of hydraulic pumps, and the appropriate components are selected and reconstructed to get the denoised vibration signals. Then, RCMFDE is adopted to extract fault information. Finally, particle swarm optimization support vector machine (PSO-SVM) is adopted to distinguish different work states of hydraulic pumps. The experiments prove that the proposed method has higher fault recognition accuracy in comparison with MSE, MFE, and MFDE.

No abstract

No abstract

No abstract