NDLI: RouteMiner: Mining Ship Routes from a Massive Maritime Trajectories

Content Provider	IEEE Xplore Digital Library
Author	Yu-Ting Wen Chien-Hsiang Lai Po-Ruey Lei Wen-Chih Peng
Copyright Year	2014
Description	Author affiliation: Dept. of Comput. Sci., Nat. Chiao Tung Univ., Hsinchu, Taiwan (Yu-Ting Wen; Chien-Hsiang Lai; Wen-Chih Peng) \|\| Dept. of Electr. Eng., ROC Naval Acad., Zuoying, Taiwan (Po-Ruey Lei)
Abstract	Mining trajectory data has been attracting significant interest in the last years. By analyzing trajectory data, we are able to discover the movement behavior and location-aware knowledge, and then develop many interesting applications such as movement behavior discovery, location prediction, traffic analysis, and so on. However, trajectory data mining is a challenge task because of the trajectory data is available with uncertainty. Furthermore, discovering the valuable knowledge from maritime trajectory is made even more difficult due to the maritime area is a free moving space. Unlike the vehicles' movements are constrained by road networks, there is no such a sea route for ships to follow in maritime area. A ship's movement may not exactly repeat the same trajectory even the ship has the similar movement behavior with others. In this work, Route Miner system provides a framework of ship route mining for maritime traffic analysis. Given a set of ship trajectories in a maritime area, Route Miner explore the movement behavior from those massive trajectories in a free moving space. Then, ship routes are detected based on those behavioral pattern. Finally, the system generates a set of ships routes to provide operators a better understanding from ship trajectory data. We conduct the experiments on real maritime trajectories to show the effectiveness of proposed Route Miner. In the future, Route Miner is going to serve as the photo type for exploring the solutions of the challenges those related to anomaly detection and traffic management in the maritime domain.
Sponsorship	IEEE
Starting Page	353
Ending Page	356
File Size	1448501
Page Count	4
File Format	PDF
ISBN	9781479957057
DOI	10.1109/MDM.2014.52
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-14
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Trajectory Marine vehicles Vectors Data mining Roads Uncertainty Vehicles Ship Route Mining Maritime Trajectory Trajectory Data Mining
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Knowledge-based clustering of ship trajectories using density-based approach

Mining ship spatial trajectory patterns from AIS database for maritime surveillance

Ship movement anomaly detection using specialized distance measures

Constructing popular routes from uncertain trajectories

A Maritime Traffic Network Mining Method Based on Massive Trajectory Data

Semi-automatic extraction of ship lanes and movement corridors from AIS data

Learning lane change trajectories from on-road driving data

Route Discovery from Mining Uncertain Trajectories

Mining Trajectories for Spatio-temporal Analytics

RouteMiner: Mining Ship Routes from a Massive Maritime Trajectories

Similar Documents

Knowledge-based clustering of ship trajectories using density-based approach

Mining ship spatial trajectory patterns from AIS database for maritime surveillance

Ship movement anomaly detection using specialized distance measures

Constructing popular routes from uncertain trajectories

A Maritime Traffic Network Mining Method Based on Massive Trajectory Data

Semi-automatic extraction of ship lanes and movement corridors from AIS data

Learning lane change trajectories from on-road driving data

Route Discovery from Mining Uncertain Trajectories

Mining Trajectories for Spatio-temporal Analytics

RouteMiner: Mining Ship Routes from a Massive Maritime Trajectories