Search

Ridesplitting is a convenient and budget-friendly for-hire transportation service to arrange one-time shared rides on-the-fly. One crucial component for a ridesplitting system is the effective and efficient rider allocation method to match drivers to riders. Due to the uncertainty of ride requests, the difficulty in locating new riders is one of the problems in rider allocations. In this paper, a dynamic ridesplitting method based on the potential pick-up probability named DRPP is proposed. Given drivers and riders, DRPP aims to allocate the riders to maximize the drivers’ potential pick-up probability, subject to the riders’ time constraints and drivers’ capacity constraint. In DRPP, a grid network is first constructed to predict each grid’s pick-up probability and the traveling time between grids from historical GPS trajectories. To allocate multiple riders, an iterated local search method called ILSAS is proposed to find the solution with overall maximized potential pick-up probability for the drivers. Moreover, we propose the data structure TKdS-tree to improve the rider allocation efficiency. DRPP is evaluated on two real trajectory datasets. The experiment shows that DRPP performed better than other methods in service rate, share rate, and rider waiting time. [ABSTRACT FROM AUTHOR]

In existing literature, the distribution of pixel observations is analyzed with models designed for the video foreground detection task. However, it is possible that the background and foreground share similar observations, causing false detections. We propose a novel foreground detection method called Deep Variation Transformation Network (DVTN), focusing on analyzing the pixel variations instead of distributions. In particular, pixel variations are represented by a sequence of pixel observations, and DVTN is trained to transform the pixel variations into a new space, where the observations can be classified easily. Following this, the output of DVTN is utilized by a linear classifier to label pixels as foreground or background. As a result of the global analysis and the strong learning ability of DVTN, the proposed approach adaptively learns a good transformation from pixel variations to probabilities of labels to improve performance. Comprehensive experiments on several benchmark datasets demonstrate the superiority of our DVTN approach compared to both state-of-the-art deep learning and traditional methods, especially in scenes lacking texture and color information. Code is available at https://github.com/Zhangjunyin/DVTN. [ABSTRACT FROM AUTHOR]