Moving Objects Analytics:
The Case of Maritime
Analytics
Dimitris Zissis

Moving Objects
Analytics→Maritime
Analytics• Trajectory Data Mining (theory &practice)• Data Sources• trajectory pre-processing• Indexing and retrieval• trajectory pattern mining

Computing with spatial data
• A spatial trajectory is a trace generated by a moving object in geographical space, usually represented by aseries of chronologically ordered points, e. g. 𝑝1→𝑝2→⋯→𝑝𝑛, where each point consists of a geospatial
coordinate set and a timestamp such as 𝑝=(𝑥,𝑦,𝑡).

x,yIDtPOS_ID,MMSI,STATUS,STATION,SPEED,LON,LAT,COURSE,HEADING,TIMESTAMP,SHIP_ID
1094062019,667002119,0,1466,91,"27,41997","36,84816",208,511,16/09/14 12:54,763967
1094076566,667002119,0,1466,91,"27,41625","36,84376",218,511,16/09/14 12:56,763967
1094091446,667002119,0,1468,93,"27,41197","36,83942",218,511,16/09/14 12:58,763967
1094108793,667002119,0,1466,93,"27,40693","36,83435",219,511,16/09/14 13:01,763967
1094132136,667002119,0,1466,93,"27,4004","36,82856",221,511,16/09/14 13:04,763967
1094147871,667002119,0,1466,95,"27,39584","36,82366",212,511,16/09/14 13:06,763967
1094162364,667002119,0,1466,95,"27,39234","36,81873",208,511,16/09/14 13:08,763967
1094176311,667002119,0,1466,94,"27,38876","36,81381",211,511,16/09/14 13:11,763967
1094193923,667002119,0,1466,93,"27,38432","36,80786",209,511,16/09/14 13:13,763967
1094211580,667002119,0,1466,94,"27,38013","36,80176",208,511,16/09/14 13:16,763967

System Model for LBSs
• The locations of tracked movingobjects are reported to thelocation server.
• The LBS applications submit
queries to the server to retrieve
moving object data for analysis
or other application needs.

Big (spatial) DataΠοικιλία

Well…the data is BIG• Volume• 70GB per day• 50 bytes per message• Speed• 1 message per minute per vessel• >0.5 Billion messages per day• >200,000 streams• Variety• Multiple receiving sources• Data fusion from various different sourcesTrajectory Data Mining/ Moving Objects
Analytics

Yu Zheng. Trajectory Data Mining: An Overview. ACM Transactions on Intelligent Systems and Technology. 2015, vol. 6, issue 3.

DataSources

Maritime Big Data
• While in the past surveillance data was scarce, today there is amultitude of tracking systems both on board the vessel but also“above”• Collaborative Systems• Automatic Identification System (AIS)• Long-Range Identification and Tracking (LRIT)• Vessel Monitoring Systems (VMS)• Non collaborative systems• Cameras• Synthetic Aperture Radar (SAR)Automatic Identification System• The AIS is a collaborative, self-reporting system that allows marinevessels to broadcast their information to nearby vessels and onground base stations.
• It uses digital radio signals to exchange real time information between
vessels and shore based stations on dedicated VHF frequencies.
• Collision detection• Although mandatory for large commercial vessels to carry device, it is notmandatory to use it.
• Not a replacement of radar as it cannot detect land masses, navigation
beacons and vessels not equipped with AISIt is impossible to capture a lossless trajectory• To capture though the accurate and complete trajectory of a moving object, is almost impossible in realconditions, due to the inherent limitations of data acquisition and storage mechanisms.
• Thus it can be captured as a time stamped series of location points denoted as p0(x0,y0,t0), p0(x1,y1,t1), …,pn(xn,yn,tn), where xi,yi represents geographic coordinates of the moving object at time ti and N is the total
number of elements in the series. To generate the trajectory, a sensor needs to acquire its coordinates x,y attime t.

σ 𝑡𝑟𝑎𝑗1 = 𝑝0 𝑝1 , 𝑝1 𝑝2 , 𝑝2 𝑝3 , 𝑝3 𝑝4 , 𝑝4 𝑝5 , 𝑝5 𝑝6 , 𝑝6 𝑝7 , 𝑝7 𝑝8
σ 𝑡𝑟𝑎𝑗2 = 𝑝0 𝑝1 , 𝑝1 𝑝3 , 𝑝3 𝑝4 , 𝑝4 𝑝7 , 𝑝7 𝑝8
σ 𝑡𝑟𝑎𝑗1 != σ 𝑡𝑟𝑎𝑗2

08/06/2022

Inception Report Lot 2- Vessel density

17Non uniform temporal and spatial data
• AIS information is classed as either static or dynamic. Statis isbroadcast when data has been amended or upon request or bydefault every 6 minutes.

19Non uniform temporal and spatial data

Strait of Gibraltar. Ships (there are many) show up as bright dots.

ship targets seen next to the track through the sea ice.

K. Bereta, R. Grasso and D. Zissis, "Vessel Detection using Image Processing and Neural Networks," IGARSS 2020 - 2020 IEEE
International Geoscience and Remote Sensing Symposium, 2020, pp. 2276-2279, doi: 10.1109/IGARSS39084.2020.9323883.

Coastal Cameras

Trajectory
Preprocessing

Stay Point Detection• Spatial points are not equally important in a trajectory.
• Some points denote locations where objects have stayed for a while• such as shopping malls and tourist attractions, or gas stations where a vehicle was refueled.

With such stay points, we can turn a trajectory from a series of time-stamped spatial points 𝑷 into
a sequence of meaningful places 𝑺,
𝑷=𝑝1→𝑝2→⋯→𝑝𝑛, ⇒ 𝑺=𝑠1Δ𝑡1→ 𝑠2Δ𝑡2→ ,…,Δ𝑡𝑛−1→ 𝑠𝑛,

Defining port boundaries

A distributed approach to estimating sea port operational regions from lots of AIS data
Millefiori, L.M., Zissis, D., Cazzanti, L. , IEEE Big Data, 2016

Scalable and distributed sea port
operational areas estimation from AIS data

A distributed approach to estimating sea port operational regions from lots of AIS data
Millefiori, L.M., Zissis, D., Cazzanti, L. , IEEE Big Data, 2016

SHANGAI PORT

>57 million AIS messages!
recorded during the month of March 2015

Scalable and distributed sea port operational areas estimationfrom AIS data
Millefiori, L.M., Zissis, D., Cazzanti, L. Arcieri, G, IEEE
International Conference on Data Mining, 2016

Compression• Given a trajectory that consists of a full series of time-stamped points, a batched compression algorithm aims
to generate an approximated trajectory by discarding some points with a negligible error from the originaltrajectory• A well-known algorithm, called Douglas-Peucker, is used to approximate the original trajectory.
• the idea of Douglas-Peucker is to replace the original trajectory by an approximate line segment, e.g. 𝑝1𝑝12 .
• If the replacement does not meet the specified error requirement (Perpendicular Euclidean Distance is used inthis example), it recursively partitions the original problem into two sub-problems by selecting the point
contributing the biggest error as the splitting point.

On-line Compression –Sliding Window• Fit the location points in a growing sliding window with a valid line segment and continue
to grow the sliding window until the approximation error exceeds some error bound.
1.
2.
3.
4.

First initialize the first location point of a trajectory as the anchor point pa and then starts to grow thesliding window
When a new location point pi is added to the sliding window, the line segment pa pi is used to fit all thelocation points within the sliding window.
As long as the distance errors against the line segment pa pi are smaller than the user-specified error
threshold, the sliding window continues to grow. Otherwise, the line segment pa pi-1 is included as partof the approximated trajectory and pi is set as the new anchor point.
The algorithm continues until all the location points in the original trajectory are visited.

Trajectory
Indexing /RetrievalTrajectory Data Management• Spatial Databases• Queries

Tr3

R

Tr2

• Range queries• KNN queries

q1p2

Tr1A) Range Query• Distance metrics

qt

Tr3
q2

Tr3

Tr2

Tr2

Tr1

Tr1

B) KNN Point QueryC) KNN Trajectory Query• The distance between a point 𝑞 and a trajectory• The Distance between two trajectories• The distance between two trajectory segments• Indexing structures• Retrieval algorithms

timeTime slot 0Time slot 1

Segment 3
Tr1
Segment 1

Segment 2xyA) 3DR-TreeB) A spatial index in each time slotC) A temporal index

Uncertainty

Uncertain Trajectories• As the location of a moving object is recorded at a certain time interval, the trajectory data we obtain isusually a sample of the object’s true movement. On one hand, the movement of an object between two
consecutive sampling points becomes unknown (or called uncertain). To this end, we expect to reduce theuncertainty of a trajectory.
• On the other hand, in some applications, to protect a user’s privacy that could be leaked from her trajectories,
we need to make a trajectory even more uncertain.

Interpolation or Gap fillingTraj. Pattern
MiningTrajectory pattern mining
Common Behaviour Detection: flocks, convoys, moving clusters, grouppatterns and swarms

https://dspace.lib.ntua.gr/xmlui/bitstream/handle/123456789/8613/vlachakia_trajectorymining.pdf?sequence=3&isAllowed=yTrajectory Clustering• Route detection and definition

Why is this useful though?

Routes as traffic corridors (1)• In the real world though, ships do not travel on these thin lines, as
traffic corridors have a variable width, volume, and distribution• identifying the specific characteristics of each corridor, andfundamentally the spatial variations of ship traffic dynamics.

46

Patterns of Life

D. Zissis, K. Chatzikokolakis, G. Spiliopoulos and M. Vodas, "A Distributed Spatial Method for Modeling
Maritime Routes," in IEEE Access, vol. 8, pp. 47556-47568, 2020, doi: 10.1109/ACCESS.2020.2979612.

Routes as traffic corridors (2)a convex polygon of 95% ofclustered locations

D. Zissis, K. Chatzikokolakis, G. Spiliopoulos and M. Vodas, "A Distributed Spatial Method for Modeling
Maritime Routes," in IEEE Access, vol. 8, pp. 47556-47568, 2020, doi: 10.1109/ACCESS.2020.2979612.

Global Tanker PoL (more than 100 trips perroute)

D. Zissis, K. Chatzikokolakis, G. Spiliopoulos and M. Vodas, "A Distributed Spatial Method for Modeling
49
Maritime Routes," in IEEE Access, vol. 8, pp. 47556-47568, 2020, doi: 10.1109/ACCESS.2020.2979612.

Tankers PoL in Mediterranean (with morethan 100 trips)

D. Zissis, K. Chatzikokolakis, G. Spiliopoulos and M. Vodas, "A Distributed Spatial Method for Modeling
50
Maritime Routes," in IEEE Access, vol. 8, pp. 47556-47568, 2020, doi: 10.1109/ACCESS.2020.2979612.

TrajectoryClassification/Anomaly
Detection

Detecting real world “anomalies”
Incident 1: The “CostaConcordia” grounding (13January 2012)

Costa Concordia

56

Detecting real world “anomalies”

Incident 1: The “CostaConcordia” grounding (13January 2012)

Costa Concordia57GraphMiningDucruet, C., Berli, J., Spiliopoulos, G., Zissis, D. (2021). Maritime Network Analysis: Connectivity and Spatial Distribution. In:
Artikis, A., Zissis, D. (eds) Guide to Maritime Informatics. Springer, Cham. https://doi.org/10.1007/978-3-030-61852-0_10

Ducruet, C., Berli, J., Spiliopoulos, G., Zissis, D. (2021). Maritime Network Analysis: Connectivity and Spatial Distribution. In:
Artikis, A., Zissis, D. (eds) Guide to Maritime Informatics. Springer, Cham. https://doi.org/10.1007/978-3-030-61852-0_10

Visualisation
VisualisationThe pandemic’s impact on global
maritime mobility
The work is collaboration with several collegues and friends from US and EU: Leonardo M.
Millefiori,Paolo Braca, Stefano Marano, Peter K. Willett, Giannis Spiliopoulos, Sandro Carniel.

One of the most disruptive crises ever...
•To prevent the outbreak of COVID-19,
many countries all around the world went
into full lockdown in the first half of 2020•

Unprecedented containment measures• Produced changes to all aspect ofsocial life
• Dramatically changed mobilitypatterns•

Shipping industry accounts alone formore than 80% of world trade• Reduced ship mobility could imply
reduced goods mobility on theglobal scale•

Need to assess qualitatively andquantitatively the impact of lockdowns
on global shipping mobility

https://www.dallasfed.org/research/indicators/tei/2020/tei2007

L. M. Millefiori, P. Braca, D. Zissis, G. Spiliopoulos, S.
Marano, P. K. Willett and S. Carniel, “COVID-19 impact on
global maritime mobility,” Nature Scientific Reports, 2021.

Data-driven approach to COVID-19 data•

Data-driven approach to COVID-19 data
• Global maritime mobility• Epidemiological curve (upper plots)• Risk vs Detection Delay• Forecast evolution•

Unprecedent data analysis of the maritime mobility• Historical Automatic Identification System(AIS) dataset• 55 TB stored in a big-data architecture• 1 trillion AIS messages
from more than 50,000 ships• Terrestrial network ofover 4,500 receiving stations
from 140 countries.
• The processing is based on a distributed
Apache Spark™ cluster of 40 virtual cores and
128 GB of RAM.

G. Soldi et al., "Quickest Detection and Forecast of
Pandemic Outbreaks: Analysis of COVID-19 Waves,"in IEEE Communications Magazine, 2021.

L. M. Millefiori, P. Braca, D.
Zissis, G. Spiliopoulos, S.
Marano, P. K. Willett and S.
Carniel, “COVID-19 impact onglobal maritime mobility,”
Nature Scientific Reports, 2021.

Braca, P., Gaglione, D., Marano, S. et al. Decision support for the quickest
detection of critical COVID-19 phases. Nature Scientific Reports, 2021.

Shanghai’s ego network andits 3-step neighborhood

Monthly Cumulative Navigated Miles (CNM)••

Unprecedent decrease of theglobal mobility – CNM – after thelockdowns in 2020, compare with• the previous years; and• the expected value in 2020
without pandemic
Range of variation mobility [%]• Container [−5.62, −13.77]• Dry bulk [+2.28, −3.32]• Wet bulk [−0.22, −9.27]• Passenger [−19.57,−42.77]

L. M. Millefiori, P. Braca, D. Zissis, G. Spiliopoulos, S. Marano, P. K. Willett
and S. Carniel, “COVID-19 impact on global maritime mobility,”
Nature Scientific Reports, 2021.

Substantial increase in idle ships••

Substantial increase in idle ships across all types ofships/markets globally in the first 6 months of 2020
Case of study• Daily active and idle ships in 2019 (in blue) and2020 (in orange) of the supertankers, VLCC andULCC.
• Evident decrease (increase) of active (idle)
ships from April, 2020, not present in 2019.
• This is confirming the use of a significant
subset of supertankers as oil storage.

L. M. Millefiori, P. Braca, D. Zissis, G. Spiliopoulos, S. Marano, P. K. Willett and S. Carniel, “COVID-19 impact on
global maritime mobility,” Nature Scientific Reports, 2021.

Monthly CNM density difference between 2020 and 2019
• Monthly CNM densitydifference between
2020 and 2019.
• The considered time
period is from 13 March
to 13 April.

• Each grid cell is coloredbased on the variationof the 2020 value withrespect to 2019.

L. M. Millefiori, P. Braca, D. Zissis, G. Spiliopoulos, S. Marano, P. K. Willett and S. Carniel, “COVID-19 impact on
global maritime mobility,” Nature Scientific Reports, 2021.

Monthly CNM density difference – Suez Canal

L. M. Millefiori, P. Braca, D. Zissis, G. Spiliopoulos, S. Marano, P. K. Willett and S. Carniel, “COVID-19 impact on global maritime mobility,”
Nature Scientific Reports, 2021.

Supply chain crisis in 2021-2022
Waiting times analysis

Work inprogress…

Conclusion• Data-driven approach to theglobal maritime mobility• Analysis based on the AIS• Unprecedent decrease of themobility• However the supply chain was
not disrupted showing a certain
amount of resilience• More details at• https://www.nature.com/articles/s41598-021-97461-7

L. M. Millefiori, P. Braca, D. Zissis, G. Spiliopoulos, S. Marano, P. K. Willett and S. Carniel, “COVID-19
impact on global maritime mobility,” Nature Scientific Reports, 2021.

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