Presentation from Modelling World 2015 in London (June 4, 2015). Big data is a game changer for the transportation industry - but the data need to be tamed.

2. 892 by benmschmidt on Flickr (C)19th century shipping visualized through the logs of Matthew Fontaine Maury (1806-1873), US Navy
Shipping
movements in the
19th century

3. Things are happening outside the
freight industry
(and have been for some time)

4. Stage Coach Wheel by arbyreed on Flickr
<<<<<<<<<
Development of transportation
technology has been
fairly linear
…for the last 5500 years

5. We are in the middle of a gigantic exponential
development curve
beginning

7. Measure
real-time
system
behaviour
Emil Johansson - EJOH.SE

8. Manage complex systems
Image from: http://www.as-coa.org/watchlisten/ascoa-visits-rios-operations-center

9. Predict future events

10. Avoid unpleasant surprises

11. Domain
knowledge
critical!
See for instance: Waller, M. A. and Fawcett, S. E. (2013), Data
Science, Predictive Analytics, and Big Data: A Revolution
That Will Transform Supply Chain Design and Management.
JOURNAL OF BUSINESS LOGISTICS, 34: 77–84
Data scientists -
the new superstars
"Data Science Venn Diagram" by Drew Conway - Own work. Licensed under Creative Commons
Attribution-Share Alike 3.0 via Wikimedia Commons - http://commons.wikimedia.org/wiki/
File:Data_Science_Venn_Diagram.png#mediaviewer/File:Data_Science_Venn_Diagram.png

13. Human resources
Reduction in driver
turnover, driver
assignment, using
sentiment data
analysis
Real-time capacity
availability
Inventory
management
Examples of applications in freight
(Waller and Fawcett, 2013)
Transportation
management
Optimal routing, taking
into account weather,
traffic congestion, and
driver characteristics
Time of delivery,
factoring in weather,
driver characteristics,
time of day and date
Forecasting
Waller, M. A. and Fawcett, S. E. (2013), Data Science, Predictive Analytics, and Big Data: A Revolution That Will
Transform Supply Chain Design and Management. JOURNAL OF BUSINESS LOGISTICS, 34: 77–84

14. DHL 2013: ”Big Data in Logistics”

15. Jawbone measures sleep
interruption during earthquake
https://jawbone.com/blog/napa-earthquake-effect-on-sleep/

16. http://www.scdigest.com/ontarget/
14-01-21-1.php?cid=7767
Package item(s) as a package for
eventual shipment to a delivery address
Associate unique ID with package
Select destination geographic area for
package
Ship package to selected distribution
geographic area without completely
specifying delivery address
Orders satisfied by item(s)
received?
Package redirected?
Determine package location
Convey delivery address, package ID to
delivery location
Assign delivery address to package
Deliver package to delivery address
Convey indication of new destination
geographic area and package ID to
current location
Yes
Yes
No
No
smile! by Judy van der Velden (CC-BY,NC,SA)
Predictive
shipping

20. Image: Alain Delorme, alaindelorme.com
The current
model is focused
on economy of
scale and
standardization

21. But the biggest problem in
transportation is time.
There is not enough of it.
Ever.
InSearchOfLostTimebybogenfreundonFlickr

22. The transport
industry does not
like real-time
decisions.
At all.
Batch-handling
Zip codes Zones
Time-tables
DSC_9073.jpg by James England on Flickr (CC-BY)

23. Strategic Tactical Operational Predictive
Time horizons
Freight industry
Most (preferably all)
decisions in the
transportation industry are
made here. At the latest.
Uninformed,
ad-hoc, and
probably non
optimal,
decisions
Science
fiction

24. Multicolour Jelly Belly beans in Sugar! by MsSaraKelly on Flickr (CC-BY)
Requirements on
Big data specific to
freight transport
Geocoded data
Decentraliseddata
Flows
Goods
Resources
Value
Information
Products
Multiple
perspectives
Strategic
Tactical
Operative Predictive

25. http://dashburst.com/infographic/big-data-volume-variety-velocity/

26. En la cima! by Alejandro Juárez on Flickr (CC-BY)
3 mountaintops to climb…

27. En la cima! by Alejandro Juárez on Flickr (CC-BY)
3 data types
Mountaintop #1
Collection of data in real-time
Fixed Historical Snapshot

28. En la cima! by Alejandro Juárez on Flickr (CC-BY)
Mountaintop #1
Collecting data in real-time
5 data domains
Vehicle CargoDriver Company
Infrastructure/
facility
at least…

29. Length
Weight
Width
Height
Capacity
+ other PBS-criteria
Emissions
Fuel consumption
Route
Position
Speed
Direction
Weight
Origin
Destination
Accepted ETA
Temperature
+ other state variables
Temperature + other state
variables
Education/training
Speed (ISA)
Rest/break schedule
Traffic behaviour
Belt usage
Alco lock history
Schedule status (time to
next break etc.)
Contracts/
agreements
Previous interactions Backoffice support
Fixed Historical Snapshot
Vehicle
Cargo
Driver
Company
Infrastructure
/facility
Map
+ fixed data layers
Traffic history
Current traffic
Queue
Availability
DATA MATRIX

30. Say hi to the new sensors
…but they are still not enough

31. (Freight) companies want to share
as little data as possible,
with as little friction as possible,
to get the highest utility possible
Private Property by Nathan O'Nions on
Flickr (CC-BY)

32. Mountaintop #2
Processing data in real-time
En la cima! by Alejandro Juárez on Flickr (CC-BY)
Locals and Tourists #1 (GTWA #2): London by Eric Fischer on Flickr

33. Mountaintop #2
Processing data in real-time
En la cima! by Alejandro Juárez on Flickr (CC-BY)

34. Mountaintop #3
Exploiting data in real-time
En la cima! by Alejandro Juárez on Flickr (CC-BY)

35. Real-time decision making not
always successful…

36. Requirement
Fixed Historical Snapshot
Transport 1
Fixed Historical Snapshot
Transport 2
Fixed Historical Snapshot

37. Requirement
Fixed Historical Snapshot
Transport 1
Fixed Historical Snapshot
Transport 2
Fixed Historical Snapshot
No access!
Full access!

38. Smart access/guidance control

39. Smart access/guidance control

40. Smart access/guidance control

41. Smart access/guidance control

42. Fix
ed
Hist
oric
al
Sna
psh
ot
Fix
ed
Hist
oric
al
Sna
psh
ot
Requirements. Different.
Fix
ed
Hist
oric
al
Sna
psh
ot
Port area City centre
Freight terminal Bridge

43. CASES
(MANY)

44. CASES
(MANY MORE)

45. Challenges
The Challenger by Martín Vinacur on Flickr (CC-BY)
Cross-disciplinary
Cross-industries
Cross-borders

46. It’s not business as usual.
This is the internet
happening to freight
transport.
There is no ’usual’
anymore.
Hello Kitty Darth Vader by JD Hancock on Flickr (CC-BY)

47. It’s not business as usual.
Get used to it.
This is the internet
happening to freight
transport.
There is no ’usual’
anymore.
Hello Kitty Darth Vader by JD Hancock on Flickr (CC-BY)