Detecting Flight Trajectory Anomalies and Predicting Diversions in Freight TransportationClaudio Di Ciccio, Han van der Aa, Cristina Cabanillas, Jan Mendling, andJohannes PrescherEMISA 2016, Vienna, Austria

claudio.di.ciccio@wu.ac.at

Business processes intransport domain

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Continuous taskmonitoring

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Continuous taskmonitoring

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Continuous task monitoringin multimodal transport

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Continuous task monitoringin multimodal transport

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Diversion

Diversion airport

Dealing with flight diversionsA real-life scenario

Start

End

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Dealing with flight diversionsA real-life scenario

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Dealing with flight diversionsA real-life scenario

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Dealing with flight diversionsA real-life scenario

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Dealing with flight diversionsA real-life scenario

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Dealing with flight diversionsA real-life scenario

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Dealing with flight diversionsA real-life scenario

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Dealing with flight diversionsA real-life scenario

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Dealing with flight diversionsA real-life scenario

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Dealing with flight diversionsA real-life scenario

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Dealing with flight diversionsA real-life scenario

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Dealing with flight diversionsA real-life scenario

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Dealing with flight diversionsA real-life scenario

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Dealing with flight diversionsA real-life scenario

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Dealing with flight diversionsA real-life scenario

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Dealing with flight diversionsA real-life scenario

Modern technologycomes

into play

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Dealing with flight diversionsA real-life scenario

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Objective:monitor the

continuous taskand, in case of anomalies,

raise an alertat this time:

not at this time:

Motivation

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Flight diversion

Flight diversion is an example ofcontinuous task execution anomaly

Flight diversion

Flight diversion is an example ofcontinuous task execution anomaly

Which is going to be diverted?

Source: http://www.flightradar24.com/

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Motivation

Objective:monitor the

continuous taskand, in case of anomalies,

raise an alertat this time:

not at this time:

… with an automated integrated system

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Solution sketch

Gather and buffer flight data information Slice data into time-based intervals Extract flight features (deltas) representing the

flight in the interval

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Interval-basedprogress features

Features are extracted out of data Clustered into fixed-length time intervals

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Gather flight data events along a time interval

Interpolate attribute values

Redo

Solution sketch

Gather and buffer flight data information Slice data into time-based intervals Extract flight features (deltas) representing the

flight in the interval Let an automated classifier establish whether

the features are anomalous In our implementation:

Support Vector Machines (SVMs) After a given number of consecutive

anomalous intervals, raise an alert

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Interval-basedchecking

latitude

longitude

velocity (speed)

height (altitude)

timestamp

<lat,lon,v,h,t>

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Interval-basedchecking

<lat,lon,v,h,t><lat,lon,v,h,t><lat,lon,v,h,t> ⟨∆𝑑gain ,∆𝑑

cmpl ,∆𝑑ph ,∆𝑣 ,∆h ⟩

[features] [SVM]

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Interval-basedchecking

<lat,lon,v,h,t><lat,lon,v,h,t><lat,lon,v,h,t> ⟨∆𝑑gain ,∆𝑑

cmpl ,∆𝑑ph ,∆𝑣 ,∆h ⟩

⟨∆𝑑gain ,∆𝑑cmpl ,∆𝑑

ph ,∆𝑣 ,∆h ⟩ <lat,lon,v,h,t><lat,lon,v,h,t><lat,lon,v,h,t>

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Interval-basedchecking

<lat,lon,v,h,t><lat,lon,v,h,t><lat,lon,v,h,t> ⟨∆𝑑gain ,∆𝑑

cmpl ,∆𝑑ph ,∆𝑣 ,∆h ⟩

⟨∆𝑑gain ,∆𝑑cmpl ,∆𝑑

ph ,∆𝑣 ,∆h ⟩ ⟨∆𝑑gain ,∆𝑑

cmpl ,∆𝑑ph ,∆𝑣 ,∆h ⟩

<lat,lon,v,h,t><lat,lon,v,h,t><lat,lon,v,h,t><lat,lon,v,h,t><lat,lon,v,h,t>

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Interval-basedchecking

<lat,lon,v,h,t><lat,lon,v,h,t><lat,lon,v,h,t> ⟨∆𝑑gain ,∆𝑑

cmpl ,∆𝑑ph ,∆𝑣 ,∆h ⟩

⟨∆𝑑gain ,∆𝑑cmpl ,∆𝑑

ph ,∆𝑣 ,∆h ⟩ ⟨∆𝑑gain ,∆𝑑

cmpl ,∆𝑑ph ,∆𝑣 ,∆h ⟩

⟨∆𝑑gain ,∆𝑑cmpl ,∆𝑑

ph ,∆𝑣 ,∆h ⟩

<lat,lon,v,h,t><lat,lon,v,h,t><lat,lon,v,h,t><lat,lon,v,h,t><lat,lon,v,h,t><lat,lon,v,h,t><lat,lon,v,h,t>

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Interval-basedchecking

<lat,lon,v,h,t><lat,lon,v,h,t><lat,lon,v,h,t> ⟨∆𝑑gain ,∆𝑑

cmpl ,∆𝑑ph ,∆𝑣 ,∆h ⟩

⟨∆𝑑gain ,∆𝑑cmpl ,∆𝑑

ph ,∆𝑣 ,∆h ⟩ ⟨∆𝑑gain ,∆𝑑

cmpl ,∆𝑑ph ,∆𝑣 ,∆h ⟩

⟨∆𝑑gain ,∆𝑑cmpl ,∆𝑑

ph ,∆𝑣 ,∆h ⟩ ⟨∆𝑑gain ,∆𝑑

cmpl ,∆𝑑ph ,∆𝑣 ,∆h ⟩

<lat,lon,v,h,t><lat,lon,v,h,t><lat,lon,v,h,t><lat,lon,v,h,t><lat,lon,v,h,t><lat,lon,v,h,t><lat,lon,v,h,t><lat,lon,v,h,t><lat,lon,v,h,t>

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Interval-basedchecking

<lat,lon,v,h,t><lat,lon,v,h,t><lat,lon,v,h,t> ⟨∆𝑑gain ,∆𝑑

cmpl ,∆𝑑ph ,∆𝑣 ,∆h ⟩

⟨∆𝑑gain ,∆𝑑cmpl ,∆𝑑

ph ,∆𝑣 ,∆h ⟩ ⟨∆𝑑gain ,∆𝑑

cmpl ,∆𝑑ph ,∆𝑣 ,∆h ⟩

⟨∆𝑑gain ,∆𝑑cmpl ,∆𝑑

ph ,∆𝑣 ,∆h ⟩ ⟨∆𝑑gain ,∆𝑑

cmpl ,∆𝑑ph ,∆𝑣 ,∆h ⟩

⟨∆𝑑gain ,∆𝑑cmpl ,∆𝑑

ph ,∆𝑣 ,∆h ⟩

<lat,lon,v,h,t><lat,lon,v,h,t><lat,lon,v,h,t><lat,lon,v,h,t><lat,lon,v,h,t><lat,lon,v,h,t><lat,lon,v,h,t><lat,lon,v,h,t><lat,lon,v,h,t><lat,lon,v,h,t><lat,lon,v,h,t>

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Interval-basedchecking

<lat,lon,v,h,t><lat,lon,v,h,t><lat,lon,v,h,t> ⟨∆𝑑gain ,∆𝑑

cmpl ,∆𝑑ph ,∆𝑣 ,∆h ⟩

⟨∆𝑑gain ,∆𝑑cmpl ,∆𝑑

ph ,∆𝑣 ,∆h ⟩

⟨∆𝑑gain ,∆𝑑cmpl ,∆𝑑

ph ,∆𝑣 ,∆h ⟩ ⟨∆𝑑gain ,∆𝑑

cmpl ,∆𝑑ph ,∆𝑣 ,∆h ⟩

⟨∆𝑑gain ,∆𝑑cmpl ,∆𝑑

ph ,∆𝑣 ,∆h ⟩ ⟨∆𝑑gain ,∆𝑑

cmpl ,∆𝑑ph ,∆𝑣 ,∆h ⟩

⟨∆𝑑gain ,∆𝑑cmpl ,∆𝑑

ph ,∆𝑣 ,∆h ⟩

<lat,lon,v,h,t><lat,lon,v,h,t><lat,lon,v,h,t><lat,lon,v,h,t><lat,lon,v,h,t><lat,lon,v,h,t><lat,lon,v,h,t><lat,lon,v,h,t><lat,lon,v,h,t><lat,lon,v,h,t><lat,lon,v,h,t><lat,lon,v,h,t><lat,lon,v,h,t>

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Interval-basedchecking

<lat,lon,v,h,t><lat,lon,v,h,t><lat,lon,v,h,t> ⟨∆𝑑gain ,∆𝑑

cmpl ,∆𝑑ph ,∆𝑣 ,∆h ⟩

⟨∆𝑑gain ,∆𝑑cmpl ,∆𝑑

ph ,∆𝑣 ,∆h ⟩ ⟨∆𝑑gain ,∆𝑑

cmpl ,∆𝑑ph ,∆𝑣 ,∆h ⟩

⟨∆𝑑gain ,∆𝑑cmpl ,∆𝑑

ph ,∆𝑣 ,∆h ⟩ ⟨∆𝑑gain ,∆𝑑

cmpl ,∆𝑑ph ,∆𝑣 ,∆h ⟩

⟨∆𝑑gain ,∆𝑑cmpl ,∆𝑑

ph ,∆𝑣 ,∆h ⟩ ⟨∆𝑑gain ,∆𝑑

cmpl ,∆𝑑ph ,∆𝑣 ,∆h ⟩

⟨∆𝑑gain ,∆𝑑cmpl ,∆𝑑

ph ,∆𝑣 ,∆h ⟩

<lat,lon,v,h,t><lat,lon,v,h,t><lat,lon,v,h,t><lat,lon,v,h,t><lat,lon,v,h,t><lat,lon,v,h,t><lat,lon,v,h,t><lat,lon,v,h,t><lat,lon,v,h,t><lat,lon,v,h,t><lat,lon,v,h,t><lat,lon,v,h,t><lat,lon,v,h,t><lat,lon,v,h,t><lat,lon,v,h,t><lat,lon,v,h,t>

AlertSEITE 40

Interval-basedchecking

<lat,lon,v,h,t><lat,lon,v,h,t><lat,lon,v,h,t> ⟨∆𝑑gain ,∆𝑑

cmpl ,∆𝑑ph ,∆𝑣 ,∆h ⟩

⟨∆𝑑gain ,∆𝑑cmpl ,∆𝑑

ph ,∆𝑣 ,∆h ⟩ ⟨∆𝑑gain ,∆𝑑

cmpl ,∆𝑑ph ,∆𝑣 ,∆h ⟩

⟨∆𝑑gain ,∆𝑑cmpl ,∆𝑑

ph ,∆𝑣 ,∆h ⟩ ⟨∆𝑑gain ,∆𝑑

cmpl ,∆𝑑ph ,∆𝑣 ,∆h ⟩

⟨∆𝑑gain ,∆𝑑cmpl ,∆𝑑

ph ,∆𝑣 ,∆h ⟩ ⟨∆𝑑gain ,∆𝑑

cmpl ,∆𝑑ph ,∆𝑣 ,∆h ⟩

⟨∆𝑑gain ,∆𝑑cmpl ,∆𝑑

ph ,∆𝑣 ,∆h ⟩

<lat,lon,v,h,t><lat,lon,v,h,t><lat,lon,v,h,t><lat,lon,v,h,t><lat,lon,v,h,t><lat,lon,v,h,t><lat,lon,v,h,t><lat,lon,v,h,t><lat,lon,v,h,t><lat,lon,v,h,t><lat,lon,v,h,t><lat,lon,v,h,t><lat,lon,v,h,t><lat,lon,v,h,t><lat,lon,v,h,t><lat,lon,v,h,t>

AlertSEITE 41

Interval-basedchecking

<lat,lon,v,h,t><lat,lon,v,h,t><lat,lon,v,h,t> ⟨∆𝑑gain ,∆𝑑

cmpl ,∆𝑑ph ,∆𝑣 ,∆h ⟩

⟨∆𝑑gain ,∆𝑑cmpl ,∆𝑑

ph ,∆𝑣 ,∆h ⟩ ⟨∆𝑑gain ,∆𝑑

cmpl ,∆𝑑ph ,∆𝑣 ,∆h ⟩

⟨∆𝑑gain ,∆𝑑cmpl ,∆𝑑

ph ,∆𝑣 ,∆h ⟩ ⟨∆𝑑gain ,∆𝑑

cmpl ,∆𝑑ph ,∆𝑣 ,∆h ⟩

⟨∆𝑑gain ,∆𝑑cmpl ,∆𝑑

ph ,∆𝑣 ,∆h ⟩ ⟨∆𝑑gain ,∆𝑑

cmpl ,∆𝑑ph ,∆𝑣 ,∆h ⟩

⟨∆𝑑gain ,∆𝑑cmpl ,∆𝑑

ph ,∆𝑣 ,∆h ⟩

<lat,lon,v,h,t><lat,lon,v,h,t><lat,lon,v,h,t><lat,lon,v,h,t><lat,lon,v,h,t><lat,lon,v,h,t><lat,lon,v,h,t><lat,lon,v,h,t><lat,lon,v,h,t><lat,lon,v,h,t><lat,lon,v,h,t><lat,lon,v,h,t><lat,lon,v,h,t><lat,lon,v,h,t><lat,lon,v,h,t><lat,lon,v,h,t>

AlertSEITE 42

Evaluation:Flight data

Flight data gathered from FlightStats.com and FlightRadar24.com July-August 2013 (Semi-)publicly available

K-fold cross validation

Area Diverted Regular OverallEU 46 746 792US 22 316 338

Total 68 1,062 1,130

* Thanks to Han van der Aa for his contributionSEITE 43

Evaluation:Train & validation (tuning)

F-score, Precision, Recall F-Score v. time-to-predict

* Thanks to Han van der Aa for his contributionSEITE 44

Evaluation:Test results

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Further reading

Claudio Di Ciccio, Han van der Aa, Cristina Cabanillas, Jan Mendling, and Johannes Prescher (2016)Detecting flight trajectory anomalies and predicting diversions in freight transportation Decision Support Systems, 88, 1 - 17http://dx.doi.org/10.1016/j.dss.2016.05.004

Cristina Cabanillas, Claudio Di Ciccio, Jan Mendling, andAnne Baumgrass (2014)Predictive Task Monitoring for Business ProcessesBPM 2014, Springerhttp://dx.doi.org/10.1007/978-3-319-10172-9_31

Anne Baumgrass, Cristina Cabanillas, and Claudio Di Ciccio (2015)A Conceptual Architecture for an Event-based Information Aggregation Engine in Smart LogiticsEMISA 2015 (GI)http://subs.emis.de/LNI/Proceedings/Proceedings248/109.pdf

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Detecting Flight Trajectory Anomalies and Predicting Diversions in Freight TransportationClaudio Di Ciccio, Han van der Aa, Cristina Cabanillas, Jan Mendling, andJohannes PrescherEMISA 2016, Vienna, Austria

claudio.di.ciccio@wu.ac.at

Detecting Flight Trajectory Anomalies and Predicting Diversions in Freight Transportation

Extra slides

System Architecture:Which component does what

!

Further reading

Claudio Di Ciccio, Han van der Aa, Cristina Cabanillas, Jan Mendling, and Johannes Prescher (2016)Detecting flight trajectory anomalies and predicting diversions in freight transportation Decision Support Systems, 88, 1 - 17http://dx.doi.org/10.1016/j.dss.2016.05.004

Cristina Cabanillas, Claudio Di Ciccio, Jan Mendling, andAnne Baumgrass (2014)Predictive Task Monitoring for Business ProcessesBPM 2014, Springerhttp://dx.doi.org/10.1007/978-3-319-10172-9_31

Anne Baumgrass, Cristina Cabanillas, and Claudio Di Ciccio (2015)A Conceptual Architecture for an Event-based Information Aggregation Engine in Smart LogiticsEMISA 2015 (GI)http://subs.emis.de/LNI/Proceedings/Proceedings248/109.pdf

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