Tom Van Vuren

1. Tom van Vuren
Why this data source can no longer be ignored
Building trip matrices from
mobile phone data
V1 17/08/2016 Mott MacDonald | Sydney 1

3. Why?

5. Not just call detail records, but all active and
passive event data:
Active events: @ cell level
Passive events: @ local area code (LAC)
level
Cell size = 500m – 7.5km in diameter
What?

6. What
not?

7. Weaknesses
17/08/2016 Mott MacDonald | Presentation title 7

8. • Lack of socio-
demographic data
on travellers
• Mode distinction
poor
• Purpose
distinction limited
• Short trip
detection weak
• Privacy concerns

9. Strengths
17/08/2016 Mott MacDonald | Presentation title 9

10. • Large number of
travellers detected (up
to 1/3 of population)
• All trips detected incl
static trips
• All modes and
purposes
• Non-intrusive
• Transparent
• Better value for
money

11. Ethics and privacy
17/08/2016 Mott MacDonald | Presentation title 11

12. • In the UK covered by Data Protection Act
1998
• In EU covered by Privacy and Economic
Communications Regulations 2003 and
Data Protection Directive 1995
• In practice adherence
achieved by operators
providing aggregated
trip matrices with minimum
cell values (eg >15)

13. Minimum cell value >15: London application (unexpanded): 9%
of cells filled but with 90% of total trips
Hence: function of duration of ‘survey’; minimum of 2 weeks,
recommended 1-2 months
consider spatial detail (zoning system) to minimise
values <15
be aware of segmentation (purpose, mode, time periods)
And: previous RSIs on 2 cordons,
90 sites, cost > $1M:
3% of cells filled

14. How to determine a trip
17/08/2016 Mott MacDonald | Presentation title 14

15. Trip end detected through dwell time, i.e. phone stationary in cell
or not detected for a certain period of time;
How long?
In general, 30 minutes is considered
reasonable!
Based on experience by a number of
providers
Avoiding false positives in congested
conditions
Not just stopped time; also accounts for
traversing through LACs and cells
Dwell times of 25 or 35 minutes decrease
and increase the number of detected trips
by approx. 10-12%

16. Estimating modes
17/08/2016 Mott MacDonald | Presentation title 16

17. • Snap route through cells and LACs to networks
• Use minimum and maximum average speeds
• Use speeds on part of the trip or eg use of
Motorways to determine motorised modes
• Use other data sources to estimate local speeds
• Use key locations such as stations to identify mode
• Use regularity of trips to strengthen evidence base
But…
• Slow modes hard to extract
• Rural areas: size of cells
• Urban areas: low speeds for mechanised modes
• Long distance rail easier to identify than local rail
• Bus, tram, LGV and HGV amalgamated with car
trips to road-based mode
• Other analyses and data sources required to
extract modes from road-based matrix

18. Mobile NTS (GB 2012) NTEM v6.2
Road 77.1% 73.2% 68.5%
Rail 0.4% 2.8% 1.3%
Other 22.5% 24.0% 30.2%

19. AREAS Motorised Active Modes
Central London 48% 52%
Other London 70% 30%
Study Area Outside London 78% 22%
Other UK 89% 11%
TOTAL 70% 30%
London Study Area Working Day Travel Mode Split (unexpanded)

20. Estimating purposes
17/08/2016 Mott MacDonald | Presentation title 20

21. • Home location can be inferred by
analysis of multiple days and eg where
phone resides at night
• Similarly, work location can be inferred by
where phone resides for majority of time
between 9AM and 5PM
But…
• Is it work or education (usual day-time
location?)
• Problems with (short) home to home trips
• Other purposes not easily identified
• Business purpose not easy to distinguish
• No known successful experience of
combining with detailed land use data to
infer more detailed purposes

22. Mobile NTS (GB 2012) NTEM v6.2
HBW 3.6% 15.3% 20.1%
HBO 45.6% 62.6%
Other 50.8% 14.2%

23. Expanding observed
matrices
17/08/2016 Mott MacDonald | Presentation title 23

24. • Each provider captures approx. 1/3 of the market;
• Expand on basis of device market rate rather than trip
market rate;
• Expand on basis of total population figures for home
zone;
• Home zone not determined by contract but inferred by
where phone resides at night;
• But considerations around what appropriate
population is (eg
excluding children
or correcting for
low and high users)

25. Legend
LHR Population Data (2014 m
ExpF1
0.000000 - 1.000000
1.000001 - 2.500000
2.500001 - 5.000000
5.000001 - 10.000000
10.000001 - 100000.000000

27. Examples of current
applications in the UK
17/08/2016 Mott MacDonald | Presentation title 27

28. • Highways England: 5 Regional
Traffic Models, highways only,
used for major roads investment
projects;
• South East Wales (Cardiff)
strategic model used for transport
policy and investment, highway
matrices only;
• Heathrow and Gatwick traffic
models, used for surface access
strategy development, covering all
of London incl M25, highways
only;
• Transport for London, multi-modal
matrices for whole of London,
project Edmond.

29. Validating resulting matrices
17/08/2016 Mott MacDonald | Presentation title 29

30. Test
ID
Demand
Indicator
Data Check / Comparison Analysis Approach Spatial Level
1
Total trips per
period/day of
week
- Plot total number of trips identified by day, time period and
mode
Graph Total
2 Matrix Symmetry - Origins versus Destinations by mode Regression / scatter plots MPD Zones
3 Trip Rates - Motorised and slow mode trips daily versus NTS trip rates Regression / scatter plots
MPD Zones /
District
4
Trip Length
Distribution
- TLD by motorised mode vs NTS
Comparison of distributions
and mean values
MPD Zones
5
Pattern of trip-
ends
- Scatter plot of trip-ends between MPD and NTEM Regression / scatter plots
MPD Zones /
District
6 Time Period Split - Time period split vs. NTEM / NTS Comparison of proportion County
Example validation checks for unexpanded data

31. Example validation checks for expanded data
Test ID
Demand
Indicator
Data Check / Comparison
Analysis
Approach
Spatial
Level
1
Removal of Rail
Trips
- HBW FH origins vs. Census JTW ‘home’ locations. Comparison with JTW
data with and without rail trips.
- HBW FH destinations vs. Census JTW ‘work’ locations. Comparison with
JTW data with and without rail trips.
Regression /
scatter plots
MPD Zones /
Districts
2
Matrix
Symmetry
- From-home vs. to-home (all purposes)
- All origins vs. all destinations (all purposes)
Regression /
scatter plots
MPD Zones
3
Trip Ends / Trip
Rates
- All day HBW from-home origins and to-home destinations vs. Census JTW
‘home’ locations
- All day HBW from-home destinations and to-home origins vs. Census JTW
‘work’ locations
Regression /
scatter plots
MPD Zones /
District
- All day trip production vs. NTEM trip-ends, separately for HBW, HBO, and
NHB.
Regression /
scatter plots
MPD Zones /
District
- FH trip rates vs. NTS (district level) - for all trips and excluding short trips
(less than 5 km)
Average Trip
Rates
County
4
Trip Length
Distribution
- HBW FH vs. JTW data
- HBW/HBO/NHB vs. NTS Data
Comparison of
distributions and
mean values
MPD Zones
5
Trip Purpose /
Direction Split
- HBW/HBO/NHB split vs. NTS
- FH/TH/NHB split by time period vs. NTS
Comparison of
proportion
County
6
Time Period
Split
- Time period split vs. NTS
Comparison of
proportion
County
7 Vehicle Flows - Assigned flows vs. counts across long screenlines
Comparison of
traffic volume

32. Symmetry of PRISM AM trip matrix and transposed PM trip matrix
http://www.fsutmsonline.net/images/uploads/reports/FR2_FDOT
_Model_CalVal_Standards_Final_Report_10.2.08.pdf

33. Mobile Phone NTS GB 2012 NTEM v6.2
Road 1.84 1.91 1.88
Rail 0.01 0.07 0.04
Other 0.56 0.63 0.83
Total 2.41 2.61 2.75
Comparison of trip rates

35. SEWTM Spring ATCs (147
sites)

36. For discussion
17/08/2016 Mott MacDonald | Presentation title 36

37. • Mobile phone based matrices are an inevitable
component in future transport models;
• Understanding of strengths and weaknesses is
increasing but confidence is growing;
• Continuous development of methods to interpret and
infer better outputs (trip rates, modes and purposes);
• Validation criteria are being developed and refined, and
tend to be stricter than for traditional survey methods;
• Fusion or merging with other data sources will
challenge profession but be the ultimate proof – data
science vs transport modelling;
• Privacy concerns are not a showstopper in the UK;
what needs doing in ANZ?

39. Comparing matrices from
different sources
17/08/2016 Mott MacDonald | Presentation title 39

42. What does SSIM calculate?
Function of
• Mean (luminosity or volume)
• Variance (in pixels / OD cells)
• Covariance (between pixels / OD cells)
van Vuren T and Day-Pollard, T (2015a) 256 shades of grey – comparing
OD matrices using image quality assessment techniques. Presented at
Scottish Transport Applications and Research Conference, Glasgow, and
published: http://www.starconference.org.uk/star/2015/Pollard.pdf