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GLUE: a Parameter-Tuning-Free Map Updating System

Published: 17 October 2015 Publication History

Abstract

Map data are widely used in mobile services, but most maps might not be complete. Updating the map automatically is an important problem because road networks are frequently changed with the development of the city. This paper studies the problem of recovering missing road segments via GPS trajectories, especially low sampled data. Our approach takes the GPS noise into consideration and proposes an effective self-adaptive algorithm. Besides, we propose theoretical models behind all the important parameters to enable self-adaptive parameter setting. To the best of our knowledge, this is the first work that addresses the parameter setting issue successfully to make sure our approach is free of parameter-tuning. In addition, we also propose a quantitative evaluation method for map updating problem. The result shows our algorithm has a much better performance than the existing approaches.

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Cited By

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  • (2024)DelvMap: Completing Residential Roads in Maps Based on Couriers’ Trajectories and Satellite ImageryIEEE Transactions on Geoscience and Remote Sensing10.1109/TGRS.2024.336583362(1-14)Online publication date: 2024
  • (2020)A Survey and Quantitative Study on Map Inference Algorithms from GPS TrajectoriesIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2020.2977034(1-1)Online publication date: 2020
  • (2020)Automatic Calibration of Road Intersection Topology using Trajectories2020 IEEE 36th International Conference on Data Engineering (ICDE)10.1109/ICDE48307.2020.00145(1633-1644)Online publication date: Apr-2020
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Published In

cover image ACM Conferences
CIKM '15: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management
October 2015
1998 pages
ISBN:9781450337946
DOI:10.1145/2806416
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 17 October 2015

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Author Tags

  1. map inference
  2. map updating
  3. trajectory mining

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  • Research-article

Funding Sources

  • Shanghai Science and Technology Development Funds
  • National Natural Science Foundation of China
  • Shanghai Natural Science Foundation

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CIKM'15
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CIKM '15 Paper Acceptance Rate 165 of 646 submissions, 26%;
Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

View all
  • (2024)DelvMap: Completing Residential Roads in Maps Based on Couriers’ Trajectories and Satellite ImageryIEEE Transactions on Geoscience and Remote Sensing10.1109/TGRS.2024.336583362(1-14)Online publication date: 2024
  • (2020)A Survey and Quantitative Study on Map Inference Algorithms from GPS TrajectoriesIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2020.2977034(1-1)Online publication date: 2020
  • (2020)Automatic Calibration of Road Intersection Topology using Trajectories2020 IEEE 36th International Conference on Data Engineering (ICDE)10.1109/ICDE48307.2020.00145(1633-1644)Online publication date: Apr-2020
  • (2019)Road Intersection Detection Based on Direction Ratio Statistics Analysis2019 20th IEEE International Conference on Mobile Data Management (MDM)10.1109/MDM.2019.00-46(288-297)Online publication date: Jun-2019
  • (2019)Trajectories know where map is wrong: an iterative framework for map-trajectory co-optimisationWorld Wide Web10.1007/s11280-019-00721-w23:1(47-73)Online publication date: 14-Aug-2019
  • (2018)Walkway discovery from large scale crowdsensingProceedings of the 17th ACM/IEEE International Conference on Information Processing in Sensor Networks10.1109/IPSN.2018.00009(13-24)Online publication date: 11-Apr-2018
  • (2018)Online clustering of streaming trajectoriesFrontiers of Computer Science: Selected Publications from Chinese Universities10.1007/s11704-017-6325-012:2(245-263)Online publication date: 1-Apr-2018
  • (2017)CLSTERSProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/31309811:3(1-28)Online publication date: 11-Sep-2017
  • (2017)COMPRESSACM Transactions on Database Systems10.1145/301545742:2(1-49)Online publication date: 10-May-2017
  • (2017)Feature Grouping-Based Outlier Detection Upon Streaming TrajectoriesIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2017.274461929:12(2696-2709)Online publication date: 1-Dec-2017
  • Show More Cited By

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