research-article

Polestar: An Intelligent, Efficient and National-Wide Public Transportation Routing Engine

Authors:

Hui XiongAuthors Info & Claims

KDD '20: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

Pages 2321 - 2329

https://doi.org/10.1145/3394486.3403281

Published: 20 August 2020 Publication History

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.

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Cited By

Han JZhang WLiu HTao TTan NXiong H(2024)BigST: Linear Complexity Spatio-Temporal Graph Neural Network for Traffic Forecasting on Large-Scale Road NetworksProceedings of the VLDB Endowment10.14778/3641204.364121717:5(1081-1090)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.14778/3641204.3641217
Li WCai MGao MWen DQin LWang W(2023)Expanding Reverse Nearest NeighborsProceedings of the VLDB Endowment10.14778/3636218.363622017:4(630-642)Online publication date: 1-Dec-2023
https://dl.acm.org/doi/10.14778/3636218.3636220
Zhang QZhu HWang PChen EXiong H(2023)Hierarchical Wi-Fi Trajectory Embedding for Indoor User Mobility Pattern AnalysisProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35962377:2(1-21)Online publication date: 12-Jun-2023
https://dl.acm.org/doi/10.1145/3596237
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Index Terms

Polestar: An Intelligent, Efficient and National-Wide Public Transportation Routing Engine
1. Information systems
  1. Information systems applications
    1. Spatial-temporal systems

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cover image ACM Conferences

KDD '20: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

August 2020

3664 pages

ISBN:9781450379984

DOI:10.1145/3394486

General Chairs:
Rajesh Gupta
UC San Diego, USA
,
Yan Liu
USC, USA
,
Program Chairs:
Mohak Shah
LG Electronics, USA
,
Suju Rajan
Linkedin, USA
,
Publications Chairs:
Jiliang Tang
Michigan State, USA
,
B. Aditya Prakash
Georgia Tech, USA

Copyright © 2020 ACM.

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Published: 20 August 2020

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July 6 - 10, 2020

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Cited By

Han JZhang WLiu HTao TTan NXiong H(2024)BigST: Linear Complexity Spatio-Temporal Graph Neural Network for Traffic Forecasting on Large-Scale Road NetworksProceedings of the VLDB Endowment10.14778/3641204.364121717:5(1081-1090)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.14778/3641204.3641217
Li WCai MGao MWen DQin LWang W(2023)Expanding Reverse Nearest NeighborsProceedings of the VLDB Endowment10.14778/3636218.363622017:4(630-642)Online publication date: 1-Dec-2023
https://dl.acm.org/doi/10.14778/3636218.3636220
Zhang QZhu HWang PChen EXiong H(2023)Hierarchical Wi-Fi Trajectory Embedding for Indoor User Mobility Pattern AnalysisProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35962377:2(1-21)Online publication date: 12-Jun-2023
https://dl.acm.org/doi/10.1145/3596237
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Han JLiu HLiu SChen XTan NChai HXiong HSingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)iETA: A Robust and Scalable Incremental Learning Framework for Time-of-Arrival EstimationProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599842(4100-4111)Online publication date: 6-Aug-2023
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Ding LZhao DWang ZWang GTan CFan LMa H(2023)Learning to Help Emergency Vehicles Arrive Faster: A Cooperative Vehicle-Road Scheduling ApproachIEEE Transactions on Mobile Computing10.1109/TMC.2022.318834422:10(5949-5962)Online publication date: 1-Oct-2023
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Chen ZXiao XGong YFang JMa NChai HCao ZZhang ARangwala H(2022)Interpreting Trajectories from Multiple Views: A Hierarchical Self-Attention Network for Estimating the Time of ArrivalProceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3534678.3539051(2771-2779)Online publication date: 14-Aug-2022
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