Current Science

Open Access Open Access  Restricted Access Subscription Access

Navigational Safety and Traffic Pattern Analysis Using AIS Data on the Western Coast of India


Affiliations
1 Department of Ocean Engineering and Naval Architecture, Indian Institute of Technology Kharagpur, Kharagpur 721 302, India
 

Ship-borne automatic identification system (AIS) is required to be installed onboard ships as per the regulations of International Maritime Organization. The AIS data for Indian coast for three months was analysed to determine marine traffic safety, navigation pattern, etc. The entire western Indian coast was divided into 22 legs. The analysis utilized probit regression model to calculate quantitative collision risk values for identifying the risky zones in Indian coast. The methodology uses an estimate of the distance of closest point of approach and time for closest point of approach between any set of vessels in a zone to find the collision risk index.

Keywords

Automatic Identification System, Collision Risk Value, DCPA and TCPA, Probit Regression Model.
User
Notifications
Font Size

  • Hetherington, C., Flin, R. and Mearns, K., Safety in shipping: the human element. J. Safety Res., 2006, 37, 401–411.

  • Fujii, Y., Yamanouchi, H., Tanaka, K., Yamada, K., Okuyama, Y. and Hirano, S., The behavior of ships in limited waters. Electron. Navigation Res. Inst. Pap., 1978, 19, 1–14.

  • Godwin, E. A., Statistical study of ship domain. J. Navigation, 1975, 19, 1–14.

  • Kearon, J., Computer programs for collision avoidance and traffic keeping. In Conference on Mathematical Aspects on Marine Traffic, Academic Press, London, 1977.

  • Xiao, F., Lighter, H., Gulijk, C. V. and Ale, B., Comparison study on AIS data of ship traffic behavior. Ocean Eng., 2015, 95, 84–93.

  • Pedersen, P. T., Collision and grounding mechanics. Danish Soc. Naval Arch. Mar. Eng., 1995, 125, 57.

  • Chin, H. C. and Debnath, A. K., Modelling perceived collision risk in port water navigation. Safety Sci., 2009, 47(10), 1410–1416.

  • Yao, C. and Liu, Z., Distribution diagram of ship tracks based on radar observation in marine traffic survey. J. Navigation, 2010, 63(1), 129–136.

  • Solas, I. M. O., International Convention for the Safety of Life at Sea, International Maritime Organization, London, 2003.

  • Beattie, J. H., Encounter rates in marine traffic separation schemes. J. Navigation, 1973, 24(3), 325–340.

  • Celletti, M. D., Traffic models for use in vessel traffic system. J. Navigation, 1978, 31(3), 104–116.

  • Tsou, M. C., Discovering knowledge from AIS database for application in VTS. J. Navigation, 2010, 63, 449–469.

  • Mandal, S., Nagarajan, V. and Sha, O. P., AIS data, its application and analysis. In Proceedings of International Conference on Computing in Mechanical Engineering, Kochi, India, 2015.

  • Hasegawa, K. and Yamazaki, M., Qualitative and quantitative analysis of congested marine traffic environment – an application using marine traffic simulation system. Int. J. Mar. Navigation Safety Sea Transport., 2003, 7(2), 179–184.

  • Indian Ports Association; http://www.ipa.nic.in (accessed on 12 April 2016).

  • Japanese Handbook of Ship Design, JASNAOE, Japan, 2012.


Abstract Views: 263

PDF Views: 80




  • Navigational Safety and Traffic Pattern Analysis Using AIS Data on the Western Coast of India

Abstract Views: 263  |  PDF Views: 80

Authors

Saurav Mandal
Department of Ocean Engineering and Naval Architecture, Indian Institute of Technology Kharagpur, Kharagpur 721 302, India
Vishwanath Nagarajan
Department of Ocean Engineering and Naval Architecture, Indian Institute of Technology Kharagpur, Kharagpur 721 302, India
Om Prakash Sha
Department of Ocean Engineering and Naval Architecture, Indian Institute of Technology Kharagpur, Kharagpur 721 302, India

Abstract


Ship-borne automatic identification system (AIS) is required to be installed onboard ships as per the regulations of International Maritime Organization. The AIS data for Indian coast for three months was analysed to determine marine traffic safety, navigation pattern, etc. The entire western Indian coast was divided into 22 legs. The analysis utilized probit regression model to calculate quantitative collision risk values for identifying the risky zones in Indian coast. The methodology uses an estimate of the distance of closest point of approach and time for closest point of approach between any set of vessels in a zone to find the collision risk index.

Keywords


Automatic Identification System, Collision Risk Value, DCPA and TCPA, Probit Regression Model.

References





DOI: https://doi.org/10.18520/cs%2Fv114%2Fi12%2F2473-2481