Adaptive Information Visualization for Maritime Traffic Stream Sensor Data with Parallel Context Acquisition and Machine Learning
Abstract
:1. Introduction
2. Background and Related Work
2.1. Maritime Monitoring Service
2.2. Collision Risk Evaluation
2.3. Information Overload
3. Adaptive Information Visualization Method for Maritime Traffic Stream Data
3.1. Architecture of Adaptive Information Visualization System
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3.2. Parallel and Distributed Evaluation of Collision Risk and Estimating Pilot Embarkation
3.3. Contextual Information Extraction
3.4. Machine Learning-Based Knowledge Extraction for Information Overload Handling
<AIS trajectory data> Ship name: VINUS *****, Position: (34°38′55″N , 127°55′02″E) , Course: 330°, Speed: 13 knots, rate of turn: 0.2°/min , call sign: DS***, time: 2017/06/01 10:15:00 <Port information management database> Ship type : tanker, Ship size : 250m, draught : 13 m, last pier code: MBN-01, last pier departure time: 2016/05/21 10:05:00, next pier code: MBN-01, next pier estimation arrival time: 2017/06/01 12:00:00, event category: entrance <Pilot information management database> Pilot station : No.1 pilot station, onboard time: 2016/06/01 10:00:00, pilot ladder: portside 3m, pilot name: SK, pilot disembarkation: MBN-01 <Displayed Items> Ship status: position, ship name, course, speed, ship type, call sign Destination: next pier Collision: collision index, DCPA, TCPA Pilot information: pilot name Regulation violation: regulation violation information
<Contextual data> Ship Information: large size tanker, Navigational Status: inbound, Pilot Embarkation: pilot onboard, Violation State: over speed, Collision Risk Index: 0.6 <Displayed Items> Ship status: position, ship name, course, speed, ship type, call sign Destination: next pier Collision: collision index, DCPA, TCPA Pilot information: pilot name Regulation violation: regulation violation information
4. Experiments
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
- Ortega-Corral, C. End-to-End Message Exchange in a Deployable Marine Environment Hierarchical Wireless Sensor Network. Int. J. Distrib. Sens. Netw. 2014, 10. [Google Scholar] [CrossRef]
- Park, D.W.; Park, S.H. Syntactic-level integration and display of multiple domains’ S-100-based data for e-navigation. Clust. Comput. 2017, 20, 727–730. [Google Scholar] [CrossRef]
- Wang, K.; Liang, M.; Li, Y.; Liu, J.; Liu, R.W. Maritime Traffic Data Visualization: A Brief Review. In Proceedings of the 2019 IEEE 4th International Conference on Big Data Analytics, Suzhou, China, 15–18 March 2019. [Google Scholar]
- International Maritime Organization (IMO). Guidelines for Vessel Traffic Services; International Maritime Organization (IMO): London, UK, 1997. [Google Scholar]
- Kim, K.I.; Lee, K.M. Ship Encounter Risk Evaluation for Coastal Areas with Holistic Maritime Traffic Data Analysis. Adv. Sci. Lett. 2017, 23, 9565–9569. [Google Scholar] [CrossRef]
- LocalizaTodo Home Page. Available online: https://www.localizatodo.com/html5/ (accessed on 8 November 2019).
- Weintrit, A. The Electronic Chart Display and Information System (ECDIS): An Operational Handbook; CRC Press: Boca Raton, FL, USA, 2009. [Google Scholar]
- Kim, K.I.; Lee, K.M. Context-aware information provisioning for vessel traffic service using rule-based and deep learning techniques. Int. J. Fuzzy Log. Intell. Syst. 2018, 18, 13–19. [Google Scholar] [CrossRef]
- Jeong, J.S.; Park, G.K.; Kim, K.I. Risk Assessment Model of Maritime Traffic in Time-Variant CPA Environments in Waterway. J. Adv. Comput. Intell. Intell. Inform. 2012, 16, 866–873. [Google Scholar] [CrossRef]
- Sang, L.Z.; Yan, X.P.; Mao, Z.A.; Wang, W.J. CPA Calculation Method based on AIS Position Prediction. J. Navig. 2016, 69, 1409–1426. [Google Scholar] [CrossRef]
- Mou, J.M.; van der Tak, C.; Ligteringen, H. Study on collision avoidance in busy waterways by using AIS data. Ocean Eng. 2010, 37, 483–490. [Google Scholar] [CrossRef]
- Kim, K.I.; Jeong, J.S.; Lee, B.G. Study on the Analysis of Near-Miss Ship Collisions Using Logistic Regression. J. Adv. Comput. Intell. Intell. Inform. 2017, 21, 467–473. [Google Scholar] [CrossRef]
- Hasegawa, K. Automatic Collision Avoidance System for Ship using Fuzzy Control. Kansai Soc. Nav. Arch. J. 1987, 1, 234–258. [Google Scholar]
- Hammer, A.; Hara, K. Knowledge Acquisition for Collision Avoidance Maneuver by Ship Handling Simulator; MARSIM & ICSA 90: Tokyo, Japan, 1990. [Google Scholar]
- Maes, P. Agents that reduce work and information overload. Read. Hum.-Comput. Interact. 1995, 811–821. [Google Scholar] [CrossRef]
- Eppler, M.J.; Mengis, J. The Concept of Information Overload: A Review of Literature from Organization Science, Accounting, Marketing, MIS, and Related Disciplines. Inf. Soc. 2014, 20, 325–344. [Google Scholar] [CrossRef]
- Lee, H.W.; Kim, N.R.; Lee, J.H. Deep Neural Network Self-Training Based on Unsupervised Learning and Dropout. Int. J. Fuzzy Log. Intell. Syst. 2017, 17, 1–9. [Google Scholar] [CrossRef]
- Chen, F.; Deng, P.; Wan, J.; Zhang, D.; Vasilakos, A.V.; Rong, X. Data mining for the internet of things: Literature review and challenges. Int. J. Distrib. Sens. Netw. 2015, 11. [Google Scholar] [CrossRef]
- Lee, K.M.; Lee, S.Y.; Lee, K.M.; Lee, S.H. Density and frequency-aware cluster identification for spatio-temporal sequence data. Wirel. Pers. Commun. 2017, 93, 47–65. [Google Scholar] [CrossRef]
- Kang, S.J.; Lee, S.Y.; Lee, K.M. Performance comparison of OpenMP, MPI, and MapReduce in practical problems. Adv. Multimed. 2015, 7, 1–9. [Google Scholar] [CrossRef]
- Cormen, T.H.; Leiserson, C.E.; Rivest, R.L.; Stein, C. Introduction to Algorithms; MIT Press: Cambridge, MA, USA, 2009. [Google Scholar]
- Huang, T.C.; Chu, K.C.; Lee, W.T.; Ho, Y.S. Adaptive Combiner for MapReduce on cloud computing. Clust. Comput. 2014, 17, 1231–1252. [Google Scholar] [CrossRef]
- Sun, Y.; Zhang, J.; Xiong, Y.; Zhu, G. Data security and privacy in cloud computing. Int. J. Distrib. Sens. Netw. 2014, 10. [Google Scholar] [CrossRef]
- Fox, A.; Eichelberger, C.; Hughes, J.; Lyon, S. Spatio-temporal indexing in non-relational distributed databases. In Proceedings of the 2013 IEEE International Conference on Big Data, Silicon Valley, CA, USA, 6–9 October 2013. [Google Scholar]
- Kim, K.I.; Lee, K.M. Deep learning-based caution area traffic prediction with automatic identification system sensor data. Sensors 2018, 18, 3172. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Praetorius, G.; Lützhöft, M. Decision Support for Vessel Traffic Service (VTS): User Needs for Dynamic Risk Management in the VTS. Work A J. Prev. Assess. Rehabil. 2012, 41, 4866–4872. [Google Scholar]
- Hadjieleftheriou, M.; Kollios, G.; Tsotras, V.J.; Gunopulos, D. Efficient indexing of spatiotemporal objects. In International Conference on Extending Database Technology; Springer: New York, NY, USA, 2012; pp. 251–268. [Google Scholar]
Category | Information Items to Be Displayed |
---|---|
Ship status | Position, ship name, course, speed, rate of turn (ROT), ship type, ship length, ship width, tonnage, draught, nationality, call sign, MMSI, contact number |
Destination | Last port, next port, next pier, last pier, estimated arrival time, cargo quantity, agent information |
Collision risk index | Collision index, distance, relative bearing, DCPA, TCPA, CPA |
Pilot Information | Pilot embarkation time, pilot disembarkation time, pilot station, assist tug, pilot name |
Regulation violation | Regulation violation information |
Information Category | Monitoring Group | Number of Information Items Displayed by VTS Operators | Number of Information Items Displayed by the Models (RDR) | |
---|---|---|---|---|
Rule-Based Information Provisioning Model [8] | Proposed Method | |||
Vessel Status | Inbound Vessel | 25,875 | 24,502 (▼5.3%) | 24,731 (▼4.4%) |
Outbound Vessel | 27,164 | 26,340 (▼3.0%) | 26,684 (▼1.8%) | |
Other Vessel | 18,635 | 10,414 (▼44.1%) | 13,667 (▼26.7%) | |
Destination | Inbound Vessel | 4934 | 3779 (▼23.4%) | 4648 (▼5.8%) |
Outbound Vessel | 4555 | 3631 (▼20.3%) | 4122 (▼9.5%) | |
Collision Risk Index | Inbound Vessel | 2524 | 2421 (▼4.1%) | 2402 (▼4.8%) |
Outbound Vessel | 2423 | 2345 (▼3.2%) | 2310 (▼4.7%) | |
Other Vessel | 5148 | 2341 (▼54.5%) | 3651 (▼29.1%) | |
Pilot Information | Pilot Boarding | 4876 | 2793 (▼42.7%) | 3187 (▼34.6%) |
Pilot Discharging | 2190 | 1898 (▼13.3%) | 1872 (▼14.5%) | |
Regulation Violations | Over speed | 1267 | 1208 (▼4.7%) | 1247 (▼1.6%) |
Violations of Navigation Rules | 954 | 655 (▼31.3%) | 926 (▼2.9%) | |
Total | 100,545 | 82,327 (▼18.1%) | 89,447 (▼11.0%) |
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Kim, K.-i.; Lee, K.M. Adaptive Information Visualization for Maritime Traffic Stream Sensor Data with Parallel Context Acquisition and Machine Learning. Sensors 2019, 19, 5273. https://doi.org/10.3390/s19235273
Kim K-i, Lee KM. Adaptive Information Visualization for Maritime Traffic Stream Sensor Data with Parallel Context Acquisition and Machine Learning. Sensors. 2019; 19(23):5273. https://doi.org/10.3390/s19235273
Chicago/Turabian StyleKim, Kwang-il, and Keon Myung Lee. 2019. "Adaptive Information Visualization for Maritime Traffic Stream Sensor Data with Parallel Context Acquisition and Machine Learning" Sensors 19, no. 23: 5273. https://doi.org/10.3390/s19235273
APA StyleKim, K.-i., & Lee, K. M. (2019). Adaptive Information Visualization for Maritime Traffic Stream Sensor Data with Parallel Context Acquisition and Machine Learning. Sensors, 19(23), 5273. https://doi.org/10.3390/s19235273