In this work a data pipe-line to manage and extract patterns from time-series is described. The patterns found with a combination of Conditional Restricted Boltzmann Machine (CRBM) and k-Means algorithms are then validated using a visualization tool. The motivation of finding these patterns is to leverage future emission model

Berral García, Josep Lluís

Buchaca, David

Gutiérrez, Alberto

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Discovering Ship Navigation Patterns towardsEnvironmental Impact ModelingAlberto Gutie´rrez, David Buchaca, Josep Lluı´s Berralfirstname.lastname@bsc.esBarcelona Supercomputing Center, Data Centric Computing departmentIndex Terms—Pattern Mining, Machine Learning, Neural Networks, Ships, Environmental modeling.In this work a data pipe-line to manage and extract patternsfrom time-series is described. The patterns found with acombination of Conditional Restricted Boltzmann Machine(CRBM) and k-Means algorithms are then validated using avisualization tool. The motivation of finding these patterns isto leverage future emission model.I. INTRODUCTIONAccording to the European Community Shipowners Associ-ations (ECSA) in 2015, the maritime traffic has become a keycomponent for European economy [1], being sea transportationmore fuel-efficient than other modes of transport (e.g. trucksand trains). Also, according to a recent report by the Inter-national Maritime Organization (IMO), it is expected that thisform of transport will continue increasing in the future dueto globalization and the increase of global-scale trade [2], butat the same time, it is considered an important contributorto primary atmospheric emissions in coastal areas [3] andsubsequently to European coastal air quality degradation [4],especially in the North Sea and the Mediterranean basin.Maritime traffic is also responsible for about 2.5% of globalgreenhouse gas (GHG) emissions, which are predicted toincrease between 50% and 250% by 2050 [2].Automatic Identification System (AIS) is the Global Po-sitioning System (GPS) based tracking system used for col-lision avoidance in maritime transport, as a supplement tomarine radars. AIS provides information such as a uniqueidentification for each transport (MMSI identifier), the positionas latitude and longitude (GPS positioning), the course andspeed (from the on-board gyrocompass). Such information isused by maritime authorities to track and monitor vessel move-ments and transmitted through standardized VHF transceivers,mainly to prevent collisions amongst ships.Discovering which patterns ships perform according to thedata provided will help to give explanation to: 1) air pollutantconcentrations in coastal zones and cities; and 2) degradationof sea life, by detecting unusual or even criminal activitiesfrom fishing fleets working in special sea-life protection zones.AIS data can be considered a time-series, as each input up-dates the vessel status in time. There are several approaches formining patterns for time series, from stream mining methodsfor learning on time-changing data [5], to series-aware neuralnetwork methods like Recurrent NNs and Hidden MarkovModels [6]. Here we are focusing on a simplistic pipelineconsisting in CRBMs to deal with time dimensions [7], and aclassical clustering method like k-Means [8]. The reasons forchoosing CRBMs is because our analytics goal passes to deter-mine patterns through dimensionality reduction attempting tosimplify clustering and pattern mining processes, and CRBMshave the ability to encode multidimensional input data and itshistory into a dimension and time aware k-length code, easierfor feeding simplistic clustering techniques.A. DatasetThe currently used dataset has been provided by the SpanishPorts Authority (Puertos del Estado), from their vessel mon-itoring database collecting the AIS signals from all registeredships navigating national waters. The dataset used for currentexperiments is a slice of data concerning the coastal area ofBarcelona, including a week of maritime traffic. It is composedby more than 1.5 million entries and indicating 19 features,including the vessel identification, the position in longitudeand latitude degrees, speed over ground, facing position, andother vessel properties like vessel category.The data is cleaned and processed in order to ensure thatthe data is of enough quality for analyzing it. Several featuresare then generated from the original for using them in theanalysis, e.g. rotation attribute generated from the GPS traces.B. MethodologyThe methodology here presented implements a data pipelineconsisting in the preparation of data, then passing the datathrough a CRBM for data encoding and reduction of dimen-sions, then clustering / classifying it through a classical methodlike k-Means. Following subsections explains each step of thechain, also depicted on Figure 1.The CRBM is trained with sample series of data, structuredas explained before. The CRBM is not aware of time by itself,but is our history input data what provides such notion. Thisallows training it through data batches and without forcing dataorder, as the notion of order is already present on each newinstance. Best practices in modeling and prediction require tosplit training data with validation and testing data, to preventthe auto-verification of the model, so for this reason weperformed this training process with a subset of the availabletime-series.4th BSC Severo Ochoa Doctoral Symposium34k  MeansCRBMSelected centroid...Time th(t)v(t)v(t 1)WDA... nvnv(t n)Fig. 1. Schema of the data pipeline, given a sequence at a particular timet (and its n predecessor values) the hidden activations of the CRBM arecomputed. Then the hidden activations are fed to a k-Means in order to clusterthe sequence at time t.Fig. 2. Example of the visualizing tool for traces and categorization. In thiscase patterns are distinguished using colors over the traces of the ship.Once CRBM and k-Means models are trained, new datacan be fed to the pipe-line, encoding and classifying eachinput into a status. At this time, similitude between patternsare checked visually using a tool for ship trace visualization,created on behalf this and future analyses in our center, as canbe seen in Figure 2.The visualization tool also allows the superposition of tracesfor different ships, allowing us to detect geographical regionswhere clustering labels cluster, indicating where behaviors arecaused by geographical causes.II. CONCLUSIONDetecting and discovering patterns in maritime traffic is animportant topic for modeling air quality in coastal urban zonesand sea-life. Maritime emissions, combined with urban emis-sions (industry and road traffic), are responsible of pollutionin such areas.In this paper we presented a methodology for characterizingmaritime traffic, understanding it as time series, and usingan ensemble of CRBMs and clustering techniques like k-Means to reduce dimensionality of data while consideringtime, then clustering it into common patterns of traffic. Suchmethodology implies pre-processing data, knowing that AISprovides error-prone data. Such datasets can be cleaned usingstandard techniques, also aggregated features can be derivedfrom the most reliable ones, i.e. GPS traces.CRBMs have proven to be useful for reducing such di-mensionality, as most time series contained more than 3000observations, even after pre-processing and reducing the timescale from seconds to minutes. When tuning the CRBM hyper-parameters, we observed that it is not required to introduce alarge history window (< 20 minutes) or a high number ofhidden units (∼ 20) to achieve good results. Also k-Meansappeared as a simple but effective approach to cluster thereduced outputs of CRBMs, comparable to real ship statuses.By using the presented methodology, we observed identifi-able patterns for real use cases, like vessel discrimination andoperation modes. Such patterns can be used to complementor correct missing or erroneous data from AIS, trace shipbehaviors and recognize their activity, and define geographicalregions with common operation modes and behaviors. We alsoprovided a tool for data and patterns visualization, availableto the general public.ACKNOWLEDGMENTWe would like to thanks to Marc Guevara and Albert Soretfrom Earth Sciences department for providing us with the usecase, related information and their help on this project andSpanish Ports Authority (Puertos del Estado) for providingthe data for this study. This project has received funding fromthe European Research Council (ERC) under the EuropeanUnion Horizon 2020 research and innovation programme(grant agreement No 639595). This work is partially sup-ported by the Ministry of Economy of Spain under contractsTIN2012-34557, 2014SGR1051, and Severo Ochoa Center ofExcellence SEV-2015-0493-16-5.This paper has been submitted to a Data Mining conferenceand is under review process.REFERENCES[1] E. C. S. A. (ECSA), “The economic value of the eu shipping industry.update,” February 2015.[2] T. S. et al., “Third imo greenhouse gas study,” 2014.[3] F. D. Natale and C. Carotenuto, “Particulate matter inmarine diesel engines exhausts: Emissions and control strategies,”Transportation Research Part D: Transport and Environment,vol. 40, pp. 166 – 191, 2015. [Online]. Available:http://www.sciencedirect.com/science/article/pii/S1361920915001169[4] M. Viana, P. Hammingh, A. Colette, X. Querol, B. Degraeuwe,I. de Vlieger, and J. van Aardenne, “Impact of maritimetransport emissions on coastal air quality in europe,” AtmosphericEnvironment, vol. 90, pp. 96 – 105, 2014. [Online]. Available:http://www.sciencedirect.com/science/article/pii/S1352231014002313[5] A. Bifet and R. Gavalda´, “Learning from time-changing data withadaptive windowing,” in In SIAM International Conference on DataMining, 2007.[6] Z. Ghahramani, “Hidden markov models.” River Edge, NJ, USA: WorldScientific Publishing Co., Inc., 2002, ch. An Introduction to HiddenMarkov Models and Bayesian Networks, pp. 9–42. [Online]. Available:http://dl.acm.org/citation.cfm?id=505741.505743[7] V. Mnih, H. Larochelle, and G. E. Hinton, “Conditional restrictedboltzmann machines for structured output prediction.” in UAI, F. G.Cozman and A. Pfeffer, Eds. AUAI Press, 2011, pp. 514–522. [Online].Available: http://dblp.uni-trier.de/db/conf/uai/uai2011.html#MnihLH11[8] P. K. Agarwal and C. M. Procopiuc, “Exact and approximation algorithmsfor clustering,” in Proceedings of the Ninth Annual ACM-SIAMSymposium on Discrete Algorithms, ser. SODA ’98. Philadelphia, PA,USA: Society for Industrial and Applied Mathematics, 1998, pp. 658–667.[Online]. Available: http://dl.acm.org/citation.cfm?id=314613.3150404th BSC Severo Ochoa Doctoral Symposium35

Discovering ship navigation patterns towards environmental impact modeling

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