Fuzzy Inference System for Determining Collision Risk of Ship in Madura Strait Using Automatic Identification System

Authors: Emmy Pratiwi, Ketut B. Artana, A. A. B. Dinariyana

Abstract:

Madura Strait is considered as one of the busiest shipping channels in Indonesia. High vessel traffic density in Madura Strait gives serious threat due to navigational safety in this area, i.e. ship collision. This study is necessary as an attempt to enhance the safety of marine traffic. Fuzzy inference system (FIS) is proposed to calculate risk collision of ships. Collision risk is evaluated based on ship domain, Distance to Closest Point of Approach (DCPA), and Time to Closest Point of Approach (TCPA). Data were collected by utilizing Automatic Identification System (AIS). This study considers several ships’ domain models to give the characteristic of marine traffic in the waterways. Each encounter in the ship domain is analyzed to obtain the level of collision risk. Risk level of ships, as the result in this study, can be used as guidance to avoid the accident, providing brief description about safety traffic in Madura Strait and improving the navigational safety in the area.

Keywords: Automatic identification system, collision risk, DCPA, fuzzy inference system, TCPA.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1129053

References:

[1] F. Zhu and L. Liu, "Research on a method for analysis of ship traffic density in harbor water area based on GIS," in IEEE Conference Anthology, 2013, pp. 1-4.
[2] M. P. Indonesia, "Rekapitulasi data kecelakaan kapal," Mahkamah Pelayaran Kementerian Perhubungan Indonesia, Jakarta2009.
[3] S. Chen, R. Ahmad, B.-G. Lee, and D. Kim, "Composition ship collision risk based on fuzzy theory," Journal of Central South University, vol. 21, pp. 4296-4302, 2014.
[4] I. Y. Gong, "The Development of a Supporting System for Safe Navigation on Basis of Risk," 3rd Research Report (Development of a Core Technology for a Risk Reduction), 2002.
[5] N. Akten, "Shipping accidents: a serious threat for marine environment," Journal of Black Sea/Mediterranean Environment, vol. 12, 2006.
[6] Q. Xu and N. Wang, "A Survey on Ship Collision Risk Evaluation," PROMET-Traffic&Transportation, vol. 26, pp. 475-486, 2014.
[7] P. P. I. III, "Data Jumlah Kapal Berlayar di Alur Pelayaran Barat Surabaya," PT Pelabuhan Indonesia III (Persero) Surabaya 2013.
[8] Y. Fujii and K. Tanaka, "Traffic capacity," Journal of Navigation, vol. 24, pp. 543-552, 1971.
[9] E. M. Goodwin, "A statistical study of ship domains," Journal of Navigation, vol. 28, pp. 328-344, 1975.
[10] T. Coldwell, "Marine traffic behaviour in restricted waters," Journal of Navigation, vol. 36, pp. 430-444, 1983.
[11] S. J. Chang, D. T. Hsiao, and W. C. Wang, "AIS-based delineation and interpretation of ship domain models," in OCEANS 2014 - TAIPEI, 2014, pp. 1-6.
[12] Z. Pietrzykowski, "Ship's Fuzzy Domain–a Criterion for Navigational Safety in Narrow Fairways," Journal of Navigation, vol. 61, pp. 499-514, 2008.
[13] A. C. Bukhari, I. Tusseyeva, B.-G. lee, and Y.-G. Kim, "An intelligent real-time multi-vessel collision risk assessment system from VTS view point based on fuzzy inference system," Expert Systems with Applications, vol. 40, pp. 1220-1230, 2013.
[14] K. B. Artana, D. DP, and T. P. Masroeri, "Combining AIS data and fuzzy clustering to measure danger score of ships," Journal of maritime researches, vol. 1, pp. 33-41, 2011.
[15] T. Pitana, E. Kobayashi, S. Koshimura, K. Onoda, and Rusmanto, "25 Evacuation Assessment of a Large Passenger Vessel due to Tsunami Attack," Journal of the Japan Society of Naval Architects and Ocean Engineers, pp. 205-217, 2008/12 2008.
[16] T. Pitana, A. Dinariyana, K. B. Artana, M. B. Zaman, and P. Hilman, "Development of hazard navigation map by using AIS data," Journal of maritime researches, vol. 1, pp. 43-52, 2011.
[17] C.-M. Su, K.-Y. Chang, and C.-Y. Cheng, "Fuzzy decision on optimal collision avoidance measures for ships in vessel traffic service," Journal of Marine Science and Technology, vol. 20, pp. 38-48, 2012.
[18] A. Paralikas and A. Lygeros, "A multi-criteria and fuzzy logic based methodology for the relative ranking of the fire hazard of chemical substances and installations," Process Safety and Environmental Protection, vol. 83, pp. 122-134, 2005.
[19] L. A. Zadeh, "Fuzzy sets," Information and control, vol. 8, pp. 338-353, 1965.