Polestar++: An Intelligent Routing Engine for National-Wide Public Transportation
Hao Liu, Ying Li, Yanjie Fu, Huaibo Mei, Hui Yun Xiong · IEEE Transactions on Knowledge and Data Engineering · 2022
Public transportation plays a critical role in peoples daily life. It has been proven that public transportation is more environmentally sustainable, efficient, and economical than any other forms of travel. However, due to the increasing expansion of transportation networks and more complex travel situations, people are having difficulties in efficiently finding the most preferred route from one place to another through public transportation systems for both intra-city and inter-city trips. To this end, in this paper, we present Polestar++, a data-driven engine for intelligent and efficient public transportation routing. Specifically, we first propose a novel hierarchical public transportation graph(HPTG) to model both intra-city and inter-city public transportation in terms of various travel costs, such as time or distance. Then, we introduce a general route search algorithm coupled with an efficient station binding method for efficient route candidate generation. After that, we propose a two-pass route candidate ranking module to capture user preferences under dynamic travel situations. Finally, experiments on two real-world data sets demonstrate the advantages of Polestar++ in terms of both efficiency and effectiveness. To date, Polestar++ is servicing over 330 cities, answers over a hundred millions of queries each day, and achieves substantial improvement of user click ratio.