Håvard Gjøsæter A Method for Detecting Near-Misses from AIS Data Master’s thesis in Marine Technology Supervisor: Bjørn Egil Asbjørnslett June 2019 Master’s thesis Master’s NTNU Faculty of Engineering Faculty Department of Marine Technology Norwegian University of Science and Technology of Science University Norwegian Håvard Gjøsæter A Method for Detecting Near-Misses from AIS Data Master’s thesis in Marine Technology Supervisor: Bjørn Egil Asbjørnslett June 2019 Norwegian University of Science and Technology Faculty of Engineering Department of Marine Technology Preface This master thesis represents the end of my two year Master of Science degree with specialization in Marine Systems Design and Logistics at the Norwegian University of Science and Technology (NTNU). It has been written during the spring 2019 and corresponds to 30 ECTs. The thesis proposes a method to identify near-misses from AIS data. All the data used in this thesis has been provided by the Norwegian Coastal Administration. I would like to express my thanks to my supervisor prof. Bjørn Egil Asbjørnslett for the help trough this project. I would also thank Bjørnar Brende Smestad for his work within simplifying AIS data. Trondheim, 10.June.2019 Havard˚ Gjøsæter i Summary The consequences of maritime accidents can be enormous regarding loss of life, economical loss and environmental harm. Therefore, improvement of safety at sea has been a focus for many years, and various maritime risk assessments have been conducted. To calculate the safety level in maritime traffic it is important with correct historical accidents statistics of collisions between ship. To get an even more thoroughly analysis of the risk in marine areas near-misses should also be investigated in addition to real collision. Underreporting of near-misses is an issue, and a model to detect these has therefore been proposed using Automatic Identification System (AIS) data. The model uses the concept of ship domain to detect near-misses. This is an area around the ship that the navigators want to keep free from other vessels, and if a ship enter another ship’s domain it will be defined as a near-miss. The use of ship domain is a well established method to detect near-misses. However, the domains vary and some domains are only applicable for open waters while other for restricted fairways. In this thesis a new ship domain has been proposed for narrow straits. This is a small domain where the length of the ship combined with speed are the parameters which determine the size of the domain. The model was applied in Karmsund, a narrow strait with high traffic density located in the western part of Norway. The model was first tested with a AIS database from 2010 to 2015, but due to high time intervals between the messages none near-misses were detected. A new database was acquired with data from August 2017, and in this period 1337 near-misses were detected. It is concluded that most of the detected near-misses are only close encounters and can not be defined as a near-miss. The proposed ship domain is not applicable for Karmsund since the vessels are forced to sail closely to each other due to the narrow fairway, and for large vessels the size of the domain will cover the whole width of the strait. ii Sammendrag Konsekvensene av maritime ulykker kan være enorme med hensyn til tap av liv, økonomiske tap og miljøskader. Derfor har forbedring av sikkerheten til sjøs vært et fokus i mange ar,˚ og det har vært utført ulike maritime risikovurderinger. For a˚ beregne sikkerhetsnivaet˚ i den maritime trafikken er det viktig med riktig ulykkesstatistikk for kollisjoner mellom skip. For a˚ fa˚ en enda grundigere analyse av risikoen i de ulike marine omradene˚ bør nestenulykker undersøkes i tillegg til ekte kollisjoner. Underrapportering av nestenulykker er en utfordring, og en modell for a˚ oppdage disse har derfor blitt foreslatt˚ ved hjelp av data fra automatisk identifiseringssystem (AIS). Modellen bruker konseptet skipsdomene for a˚ oppdage nestenulykker. Dette er et omrade˚ rundt skipet som navigatørene ønsker at andre skip ikke kommer inn i. Hvis et skip kommer inn i dette omradet,˚ vil det bli definert som en nestenulykke. Bruken av skipsdomene er en veletablert metode for a˚ oppdage nestenulykker. Disse domenene varierer, og enkelte domener gjelder kun for apent˚ farvann mens andre for begrensede omrader.˚ I denne oppgaven er det foreslatt˚ et nytt skipsdomene for smale farvann. Dette er et lite domene der lengden pa˚ skipet kombinert med hastigheten er parameterne som bestemmer størrelsen pa˚ domenet. Modellen ble brukt i Karmsund, et smalt sund med høy trafikktetthet som er lokalisert i Vest- Norge. Modellen ble først testet med en AIS-database fra 2010 til 2015, men pa˚ grunn av høye tidsintervaller mellom meldingene ble det ikke oppdaget noen nestenulykker. En ny database ble anskaffet med data fra august 2017, og i denne perioden ble 1337 nestenulykker oppdaget. Det konkluderes med at de fleste av de oppdagede nestenulykkene kun er passeringer mellom to skip der avstanden har vært kort, og de kan derfor ikke defineres som en nestenulykke. Det foreslatte˚ skipsdomenet er ikke anvendelig a˚ bruke i Karmsund siden fartøyene er tvunget til a˚ seile tett pa˚ hverandre pa˚ grunn av den smale farleden, og for store fartøyer vil størrelsen pa˚ domenet dekke hele bredden av sundet. iii Contents Preface . .i Summary . ii Sammendrag . iii List of Figures . vii List of Tables . viii 1 Introduction 1 1.1 Background . .1 1.2 Objective . .3 1.3 Structure of the Thesis . .3 2 Theory 4 2.1 Risk Assessment in Maritime traffic . .4 2.2 Previous work within AIS . .6 2.3 Near-Miss Detection . .8 3 Method 15 3.1 Near-Miss Detection Algorithm . 15 3.2 AIS Data . 16 3.2.1 Introduction to AIS . 17 3.2.2 Data Quality . 20 3.2.3 Data Handling . 20 3.3 COLREGs Definitions of Encounters . 21 3.4 Ship Domain . 24 iv CONTENTS CONTENTS 3.5 Finding True Center . 28 4 Case Study 31 4.1 Data . 33 4.2 Results From the Case Study . 39 4.3 Case Study With New Database . 39 4.3.1 Detected Near-misses . 40 4.3.2 Examples of Detected Near-misses . 43 5 Discussion 46 5.1 AIS Data . 46 5.2 The Method for Detecting Near-misses . 47 6 Conclusion 49 6.1 Recommendations for Further Work . 50 Bibliography . 51 A Appendix 55 A.1 Acronyms . 55 A.2 Plotting Domains . 57 A.3 Preparing the Database . 61 A.4 Preparing the New Database . 62 A.5 Detection of Near-misses . 64 A.6 Distribution of Overtaking-, Head-on- and Crossing Encounters . 77 v List of Figures 2.1 Collision scenarios: Head-on(1), Overtaking (2) and crossing collision(3) . .5 2.2 Illustration of crossing waterways (Pedersen et al., 1995) . .6 2.3 Traffic density plot for the Baltic Sea (COWI, 2012) . .7 2.4 Fujii’s ship domain. (Goerlandt et al., 2012) . .9 2.5 Different domain-based safety criteria (Szlapczynski and Szlapczynska, 2017) . 10 2.6 Domain violation . 11 2.7 Visualization of trajectories from high resolution AIS data . 12 2.8 A. Quadrangle domain. B. Elliptical domain. (Wang, 2010) . 14 3.1 Algorithm to reveal near-misses . 16 3.2 Division of encounter sections . 23 3.3 The proposed domain is shaped like an ellipse, and Equation 3.6 defines the four radii. 26 3.4 QSD for different speeds (a) and sizes (b) compared to Fujii’s. In figure a) the domain is a function of speed with a constant length of 150 m, while in b) the domain is a function of length with a speed of 12 knots. 27 3.5 The distance between the ship and boundary, Lα, where α is the relative bearing . 28 3.6 Geometries to find the ship’s true center (H) where F, K, L and J are representing the location of the AIS transponder (Nordkvist, 2018). 30 4.1 Ferry line Mortavika-Arsvagen˚ . 32 4.2 Overview of Karmsund from Havbase.no . 33 4.3 AIS plot of registered messages. The red star represent the location of a real collision 35 vi LIST OF FIGURES LIST OF FIGURES 4.4 Distribution of vessel types in the database . 37 4.5 Interpolation of two arbitrarily vessels’ trajectories . 38 4.6 Density plot for the identified near-misses in August 2017. 41 4.7 The figures show the distribution of near-misses for overtaking, head-on and cross- ing encounters. The red dots are the encounters used for further analysis . 42 4.8 Example of overtaking encounter . 44 4.9 Example of head-on (a) and crossing (b) encounters . 45 vii List of Tables 3.1 IMO’s requirements for AIS transponders . 18 3.2 Information in Message type 1 . 18 3.3 Information in Message type 5 . 19 3.4 Reporting intervalls for dynamic data . 19 3.5 Defining encounters . 22 4.1 AIS vessel type and main group codes . 36 4.2 Distribution of near-misses . 40 viii Chapter 1 Introduction 1.1 Background Marine transport is the most economical way for transporting goods, and it is crucial for trading in a global perspective. Ships are carrying large quantities, and therefore the potential consequences of a single accident can be enormous regarding financial loss, fatalities and environmental harm. The transportation by sea has grown over the years and the world fleet is getting larger. Due to increasing traffic and the environmental focus the last decades, the safety at sea has become a priority for maritime authorities. This focus has led to maritime risk assessments and implementing of risk reducing measures (RRM).