Paper Info
Title
Polestar: An Intelligent, Efficient and National-Wide Public Transportation Routing Engine
Abstract
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 effectivenes 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.
Year
DOI
Venue
2020
10.1145/3394486.3403281
KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining Virtual Event CA USA July, 2020
DocType
ISSN
ISBN
Conference
KDD 2020 applied data science track
978-1-4503-7998-4
Citations 
PageRank 
References 
2
0.36
20
Authors
7
Name
Order
Citations
PageRank
hao liu141.78
Ying Li2111.23
Yanjie Fu360644.43
Huaibo Mei420.36
Jingbo Zhou59422.78
Xu Ma620.36
Hui Xiong74958290.62