  Public transportation plays a critical role in people's 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. 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 Public Transportation Graph (PTG) to model public
transportation system 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. Indeed, in early 2019, Polestar has been deployed
on Baidu Maps, one of the world's largest map services. 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.
