detecting flight trajectory anomalies and predicting diversions in freight transportation
TRANSCRIPT
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
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
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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