A lecture on ITS from a road freight transporter perspective. It talks about general demands and trends, about digitization in the transport industry in general and with the challenged in real-time data exploitation. Also, Big data is presented from a freight transport perspective.

1. Intelligent Transport Systems
From a freight company perspective
Per Olof Arnäs
Chalmers
@Dr_PO
per-olof.arnas@chalmers.se
!
slideshare.net/poar
Dog Intelligence by alicejamieson on Flickr (CC-BY,NC,SA)

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

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

5. A new global eco system
where new types of,
knowledge based,
industries compete with
traditional ones
http://jaysimons.deviantart.com/art/Map-of-the-Internet-1-0-427143215

6. Startups don’t compete with airlines...
by purchasing a bunch of planes
hiring a bunch of pilots
and locking up a bunch of terminals at airports.
Quote: bryce.vc/post/18404303850/the-problem-with-innovation
Image: Connecting the community, my Twitter strategy, and American Airlines at DFW by Trey Ratcliff on Flickr (CC-BY,NC,SA)

7. Startups don’t compete with airlines...
by purchasing a bunch of planes
hiring a bunch of pilots
and locking up a bunch of terminals at airports.
Startups compete with airlines by
inventing videoconferencing.
Quote: bryce.vc/post/18404303850/the-problem-with-innovation
Image: Connecting the community, my Twitter strategy, and American Airlines at DFW by Trey Ratcliff on Flickr (CC-BY,NC,SA)

8. Demand for
transport is
coupled with
economic
development
Source: European Commission, EU Transport in Figures, Statistical Pocketbook 2012

9. Passenger cars dominate modal split
Air transport is the fastest
growing mode (until 2007)
Road and sea transport are
the fastest growing modes in
freight transport
Source: European
Commission, EU
Transport in Figures,
Statistical
Pocketbook 2012

10. Increasing freight transport demand
http://www.eea.europa.eu/data-and-maps/figures/freight-transport-activity-growth-for-eu-25
EU-25

11. Final energy consumption, EU-28, 2012
(% of total, based on tonnes of oil equivalent)
Source: Eurostat

12. The triple bottom line
Environmental
performance
Social
performance
Sustainability
Economic
performance
Craig R. Carter Dale S. Rogers, (2008),"A framework of sustainable supply chain management: movngtoward new theory”,
International Journal of Physical Distribution & Logistics Management, Vol. 38 Iss 5 pp. 360 - 387

13. Environmental
performance
Social
performance
Sustainability
Economic
performance
Focus on transportation!

14. Focus on transportation!

15. So…
What is
ITS?
80 by Phil Dragash on Flickr (CC-BY,NC,SA)

16. !
Intelligent Transport
Systems (ITS) are advanced
applications which without
embodying intelligence as such aim
to provide innovative services relating
to different modes of transport and
traffic management and enable various
users to be better informed and make
safer, more coordinated and
‘smarter’ use of transport
networks.
ITS DIRECTIVE 2010/40/EU

17. !
In other words:
We use computers to make
transportation better.
!
(That doesn’t sound so hard, does it?)

18. RESOURCE UTILISATION LOW
Source: Kent Lumsden

19. RESOURCE UTILISATION
LOW
Safety imbalance
Variation in resource demand
Source: Kent Lumsden
Chain imbalance
Caused by the chain
Technological imbalance
E.g. mismatch in equipment
Operational imbalance
Goods and resource flow not compatible
Structural imbalance
Uneven transport demand

20. RESOURCE UTILISATION
LOW
Safety imbalance
Variation in resource demand
Source: Kent Lumsden
Chain imbalance
Caused by the chain
Technological imbalance
E.g. mismatch in equipment
Several of these
imbalances can be
Operational imbalance
Goods and resource flow not compatible
reduced by
reducing
uncertainties
Structural imbalance
Uneven transport demand

21. But the biggest problem in
transportation is time.
There is not enough of it.
Ever.
In Search Of Lost Time by bogenfreund on Flickr

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. Time horizons
Freight industry
Strategic Tactical Operational Predictive
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. Image: Alain Delorme, alaindelorme.com
The current
model is focused
on economy of
scale and
standardization

25. The current paradigm

26. So…
What are we
doing about all
this?

27. Gartners Hype Cycle for Emerging Technologies
Augmenting
humans with
technology
Machines
replacing
humans
Humans and
machines
working
alongside each
other
Machines
better
understanding
humans and
the
environment
Humans better
understanding
machines
Machines and
humans
becoming
smarter

28. Gartners Hype Cycle for Emerging Technologies
Source: Gartner August 2014

29. Gartners Hype Cycle for Emerging Technologies
Could affect freight transport

30. Time horizons
Strategic Tactical Operational Predictive
Real-time!
We are approaching
this boundary
…and we are
starting to
move past it!

31. Digitization
Increasing
goods volumes
Opportunities
New
technology
Political
interest
Quad Aces by fitzsean on Flickr

32. Functions
Goods Vehicle
Software
Computers
Paper based
Open
interface
Advanced order
handling
Simple order handling
Monitor fuel
cosnumption
Phone
Papers
Barcodes
Electronically documents
generated
freight Web
based UI
Platform based
systems
Hardware-oriented
Data collection
(proprietary)
systems
Based access
Performance
Based access
Digitization version 0 0.5 1.0 1.5 2.0
Road signs
Analogue
tools
RDS
E-mail
Fax
TMS-systems
Excel
Route
planning
GPS for navigation
RFID-tags
Communication with
vehicles E-invoice
Web based
booking
Business processes Infrastructure
Route
optimisation
The social web Open connectivity
Integrated
prognosis
Data collection
systems (open)
Tolling
systems
Webservices with
traffic data
Dynamic
routing
systems
Performance
Mashups
Multiple data
sources
Probe data
Individual
routing
information
Platooning
Platooning
Exceptions
handling
Smart goods
Manual
Distributed
decision
making
Goods as bi-directional
hyperlink
Paper based
CC-BY Per Olof Arnäs, Chalmers
Barcodes
RFID
Sensors
ERP systems
TMS systems
E-invoices
Cloudbased
services
Order handling
Driver support
Vehicle
economics
RDS-TMC
Road taxes
Active traffic
support
Predictive
maintenance
2014-10-14

33. Infrastructure
Business
processes
Vehicles
Goods
Stra-tegic
Tac-tical
Opera-tional
Pre-
What happens dictive
when access to
real-time data
increases?
not quite clear on the concept by woodleywonderworks on Flickr (CC-BY)

34. The Action of New York City by
Trey Ratcliff on Flickr (CC-BY,NC,SA)
Need for speed
Data collection
Data
processing
Data
exploitation

35. 3 mountaintops to climb…
En la cima! by Alejandro Juárez on Flickr (CC-BY)

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

37. Mountaintop #1
Collection of data in real-time
5 data domains
Vehicle Driver Cargo Company
En la cima! by Alejandro Juárez on Flickr (CC-BY)
Infrastructure/
facility
at le a s t…

38. Fixed Historical Snapshot
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
Vehicle
Cargo
Driver
Company
Infrastructure
/facility
Map
+ fixed data layers Traffic history
Current traffic
Queue
Availability
DATA MATRIX

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

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

41. Mountaintop #3
Exploiting data in real-time
Connected. 362/365 by AndYaDontStop
on Flickr (CC-BY)
Lisa for I/O Keynote by Max Braun on
Flickr (CC-BY)
En la cima! by Alejandro Juárez on Flickr (CC-BY)
Fulham-Manchester United
24-02-2007 by vuhlser on Flickr (CC-BY)

42. Mountaintop #3
Exploiting data in real-time
Boeing-KC-97 Stratotanker by x-ray delta one on Flickr (CC-BY)
En la cima! by Alejandro Juárez on Flickr (CC-BY)

43. Big data in freight
transport
!
Film by Foursquare. Google: checkins foursquare

44. Gartners Hype Cycle for Emerging Technologies
”Fast Up-and-Coming
Movers Toward the Peak
Are Fueled by Digital
Business and Payments”
”…the market has settled
into a reasonable set of
approaches, and the new
technologies and practices
are additive to existing
solutions”
(regarding the decline of Big data on the curve)
Gartner, August 2014

45. So…
What is
Big data?
{ biométrique ... } by David Jubert om Flickr (CC-BY,NC,SA)

46. 2011 2013 2015
”Big data is an all-encompassing
term for
any collection of data sets
so large and complex that
it becomes difficult to
process using on-hand
data management tools or
traditional data
processing applications.”
- Wikipedia
2015

47. Google flights
https://www.google.se/flights/

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

49. Not statistics
Exhausted by Adrian Sampson on Flickr (CC-BY)
just

50. Not
just
Business
Intelligence
Basingstoke Office Staff Desk "No computer" by John Sheldon on Flickr (CC-BY,NC,SA)

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

52. Examples of applications in freight
Human resources
Reduction in driver
turnover, driver
assignment, using
sentiment data
analysis
(Waller and Fawcett, 2013)
Inventory
management
Real-time capacity
availability
Transportation
management
Optimal routing, taking
into account weather,
traffic congestion, and
driver characteristics
Forecasting
Time of delivery,
factoring in weather,
driver characteristics,
time of day and date
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

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

54. Predict future events

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

56. Avoid unpleasant surprises

57. Vizualisation
http://blog.digital.telefonica.com/?press-release=telefonica-dynamic-insights-launches-smart-steps-in-the-uk

58. Big Data Best Practice Across Industries 7
Operational Efficiency Customer Experience New Business Models
Usage of data in order to:
Increase Level of
Transparency
Optimize Resource
Consumption
Improve Process Quality
and Performance
New
Business Models
Exploit for: Capitalize on data by:
Increase customers
loyalty and retention
Performing precise
customer segmentation
and targeting
Optimize customer
interaction and service
revenue
streams from existing
products
Creating new revenue
streams from entirely
new (data) products
Customer
Experience
Operational
Efficiency
Use data to:
• Increase level of
transparency
• Optimize resource
consumption
• Improve process quality
and performance
Exploit data to:
• Increase customer
loyalty and retention
• Perform precise customer
segmentation and targeting
• Optimize customer interaction
and service
Capitalize on data by:
• Expanding revenue streams
from existing products
• Creating new revenue
streams from entirely new
(data) products
Figure 4: Value dimensions for Big Data use cases; Source: DPDHL / Detecon
DHL 2013: ”Big Data in Logistics”

59. 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

60. smile! by Judy van der Velden (CC-BY,NC,SA)
Speculative
shipping
http://www.scdigest.com/ontarget/
14-01-21-1.php?cid=7767

61. http://www.scdigest.com/ontarget/
14-01-21-1.php?cid=7767
Speculative
shipping 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)

62. CASES
(MANY)

63. CASES
(MANY MORE)

64. Not all ideas age with grace
The Challenger by Martín Vinacur on Flickr (CC-BY)

65. Someone must do the work
The Challenger by Martín Vinacur on Flickr (CC-BY)

66. Not everyone will want to
The Challenger by Martín Vinacur on Flickr (CC-BY)
adopt new things…

68. !
Remember:
We use computers to make
transportation better.
!
(That doesn’t sound so hard, does it?)

69. Intelligent Transport Systems
From a freight company perspective
Per Olof Arnäs
Chalmers
@Dr_PO
per-olof.arnas@chalmers.se
!
slideshare.net/poar
Dog Intelligence by alicejamieson on Flickr (CC-BY,NC,SA)