No abstract

The coordination between bus rapid transit (BRT) and feeder bus service is helpful in improving the operational efficiency and service level of urban public transport system. Therefore, a coordinated operation model of BRT and bus is intended to develop in this paper. The total costs are formulated and optimized by genetic algorithm. Moreover, the skip-stop BRT operation is considered when building the coordinated operation model. A case of the existing bus network in Beijing is studied, the proposed coordinated operation model of BRT and bus is applied, and the optimized headway and costs are obtained. The results show that the coordinated operation model could effectively decrease the total costs of the transit system and the transfer time of passengers. The results also suggest that the coordination between the skip-stop BRT and bus during peak hour is more effective than non-coordination operation.

As an information carrier with rich semantics, image plays an increasingly important role in real-time monitoring of logistics management. Abnormal objects are typically closely related to the specific region. Detecting abnormal objects in the specific region is conducive to improving the accuracy of detection and analysis, thereby improving the level of logistics management. Motivated by these observations, we design the method called abnormal object detection in a specific region based on Mask R-convolutional neural network: Abnormal Object Detection in Specific Region. In this method, the initial instance segmentation model is obtained by the traditional Mask R-convolutional neural network method, then the region overlap of the specific region is calculated and the overlapping ratio of each instance is determined, and these two parts of information are fused to predict the exceptional object. Finally, the abnormal object is restored and detected in the original image. Experimental results demonstrate that our proposed Abnormal Object Detection in Specific Region can effectively identify abnormal objects in a specific region and significantly outperforms the state-of-the-art methods.

Effectively integrating the feeder bus lines along the main BRT line can advance the operation efficiency and service level of the public transit system. In order to obtain the planning method of the feeder bus network of BRT system, based on the passenger OD data of five peak values, a route generation optimization model is developed to minimize the number of feeder bus routes and to maximize the passenger intensity by introducing the conception of station importance. Then, the simulation anneal genetic algorithm is formulated to solve the proposed planning model. Finally, the proposed model and algorithm are used in the case of the BRT line 2 along Beijing Chaoyang Road. The results show that the simulation anneal genetic algorithm can calculate effectively the optimization model and the bus network optimization model based on the passenger OD of five peak values can be applied in the practice.