A data-driven system for cooperative-bus route planning based on generative adversarial network and metric learning

AbstractFaced with dynamic and increasingly diversified public transport requirements, bus operators are urged to propose operational innovations to sustain their competitiveness. In particular, ordinary bus operations are heavily constrained by well-established route options, and it is challenging to accommodate dynamic passenger flows effectively and with a good level of resource utilization performance. Inspired by the philosophy of sharing economy, many of the available transport resources on the road, such as minibuses and private vehicles, can offer opportunities for improvement if they can be effectively incorporated and exploited. In this regard, this paper proposes a metric learning-based prediction algorithm which can effectively capture the demand pattern and designs a route planning optimizer to help bus operators effectively deploy fixed routing and cooperative buses with traffic dynamics. Through extensive numerical studies, the performance of our proposed metric learning-based Generative Adversarial Network (GAN) prediction model outperforms existing ways. The effectiveness and robustness of the prediction-supported routing planner are well demonstrated for a real-time case. Further, managerial insights with regard to travel time, bus fleet size, and customer service levels are revealed by various sensitivity analysis.</jats:p

Incorporating Metric Learning and Adversarial Network for Seasonal Invariant Change Detection

Change detection by comparing two bitemporal images is one of the most fundamental challenges for dynamic monitoring of the Earth surface. In this article, we propose a metric learning-based generative adversarial network (GAN) (MeGAN) to automatically explore seasonal invariant features for pseudochange suppressing and real change detection. To achieve this purpose, a seasonal invariant term is introduced to maximally suppress pseudochanges, whereas the MeGAN explores the transition patterns between adjacent images in a self-learning fashion. Different from the previous works on bitemporal imagery change detection, the proposed MeGAN have the following contributions: 1) it automatically explores change patterns from the complex bitemporal background without human intervention and 2) it aims to maximally exclude pseudochanges from the seasonal transition term and map out real changes efficiently. To our best knowledge, this is the first time we incorporate the seasonal transition term and GAN for change detection between bitemporal images. At last, to demonstrate the robustness of the proposed method, we included two data sets which are the Google Earth data and the Landsat data, for bitemporal change detection and evaluation. The experimental results indicated that the proposed method is able to perform change detection with precision can be as high as 81% and 88% for the Google Earth and Landsat data set, respectively