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Tom van Vuren
Why this data source can no longer be ignored
Building trip matrices from
mobile phone data
V1 17/08/2016 Mo...
Why?
Not just call detail records, but all active and
passive event data:
Active events: @ cell level
Passive events: @ local a...
What
not?
Weaknesses
17/08/2016 Mott MacDonald | Presentation title 7
• Lack of socio-
demographic data
on travellers
• Mode distinction
poor
• Purpose
distinction limited
• Short trip
detecti...
Strengths
17/08/2016 Mott MacDonald | Presentation title 9
• Large number of
travellers detected (up
to 1/3 of population)
• All trips detected incl
static trips
• All modes and
pur...
Ethics and privacy
17/08/2016 Mott MacDonald | Presentation title 11
• In the UK covered by Data Protection Act
1998
• In EU covered by Privacy and Economic
Communications Regulations 2003 an...
Minimum cell value >15: London application (unexpanded): 9%
of cells filled but with 90% of total trips
Hence: function of...
How to determine a trip
17/08/2016 Mott MacDonald | Presentation title 14
Trip end detected through dwell time, i.e. phone stationary in cell
or not detected for a certain period of time;
How long...
Estimating modes
17/08/2016 Mott MacDonald | Presentation title 16
• Snap route through cells and LACs to networks
• Use minimum and maximum average speeds
• Use speeds on part of the trip ...
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%
AREAS Motorised Active Modes
Central London 48% 52%
Other London 70% 30%
Study Area Outside London 78% 22%
Other UK 89% 11...
Estimating purposes
17/08/2016 Mott MacDonald | Presentation title 20
• Home location can be inferred by
analysis of multiple days and eg where
phone resides at night
• Similarly, work locatio...
Mobile NTS (GB 2012) NTEM v6.2
HBW 3.6% 15.3% 20.1%
HBO 45.6% 62.6%
Other 50.8% 14.2%
Expanding observed
matrices
17/08/2016 Mott MacDonald | Presentation title 23
• Each provider captures approx. 1/3 of the market;
• Expand on basis of device market rate rather than trip
market rate;
...
Legend
LHR Population Data (2014 m
ExpF1
0.000000 - 1.000000
1.000001 - 2.500000
2.500001 - 5.000000
5.000001 - 10.000000
...
Examples of current
applications in the UK
17/08/2016 Mott MacDonald | Presentation title 27
• Highways England: 5 Regional
Traffic Models, highways only,
used for major roads investment
projects;
• South East Wales...
Validating resulting matrices
17/08/2016 Mott MacDonald | Presentation title 29
Test
ID
Demand
Indicator
Data Check / Comparison Analysis Approach Spatial Level
1
Total trips per
period/day of
week
- Pl...
Example validation checks for expanded data
Test ID
Demand
Indicator
Data Check / Comparison
Analysis
Approach
Spatial
Lev...
Symmetry of PRISM AM trip matrix and transposed PM trip matrix
http://www.fsutmsonline.net/images/uploads/reports/FR2_FDOT...
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
Compa...
SEWTM Spring ATCs (147
sites)
For discussion
17/08/2016 Mott MacDonald | Presentation title 36
• Mobile phone based matrices are an inevitable
component in future transport models;
• Understanding of strengths and wea...
Comparing matrices from
different sources
17/08/2016 Mott MacDonald | Presentation title 39
What does SSIM calculate?
Function of
• Mean (luminosity or volume)
• Variance (in pixels / OD cells)
• Covariance (betwee...
Building trip matrices from mobile phone data
Building trip matrices from mobile phone data
Building trip matrices from mobile phone data
Building trip matrices from mobile phone data
Building trip matrices from mobile phone data
Building trip matrices from mobile phone data
Building trip matrices from mobile phone data
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Building trip matrices from mobile phone data

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Tom Van Vuren

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Building trip matrices from mobile phone data

  1. 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
  2. 2. Why?
  3. 3. 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?
  4. 4. What not?
  5. 5. Weaknesses 17/08/2016 Mott MacDonald | Presentation title 7
  6. 6. • Lack of socio- demographic data on travellers • Mode distinction poor • Purpose distinction limited • Short trip detection weak • Privacy concerns
  7. 7. Strengths 17/08/2016 Mott MacDonald | Presentation title 9
  8. 8. • 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
  9. 9. Ethics and privacy 17/08/2016 Mott MacDonald | Presentation title 11
  10. 10. • 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)
  11. 11. 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
  12. 12. How to determine a trip 17/08/2016 Mott MacDonald | Presentation title 14
  13. 13. 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%
  14. 14. Estimating modes 17/08/2016 Mott MacDonald | Presentation title 16
  15. 15. • 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
  16. 16. 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%
  17. 17. 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)
  18. 18. Estimating purposes 17/08/2016 Mott MacDonald | Presentation title 20
  19. 19. • 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
  20. 20. Mobile NTS (GB 2012) NTEM v6.2 HBW 3.6% 15.3% 20.1% HBO 45.6% 62.6% Other 50.8% 14.2%
  21. 21. Expanding observed matrices 17/08/2016 Mott MacDonald | Presentation title 23
  22. 22. • 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)
  23. 23. 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
  24. 24. Examples of current applications in the UK 17/08/2016 Mott MacDonald | Presentation title 27
  25. 25. • 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.
  26. 26. Validating resulting matrices 17/08/2016 Mott MacDonald | Presentation title 29
  27. 27. 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
  28. 28. 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
  29. 29. 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
  30. 30. 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
  31. 31. SEWTM Spring ATCs (147 sites)
  32. 32. For discussion 17/08/2016 Mott MacDonald | Presentation title 36
  33. 33. • 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?
  34. 34. Comparing matrices from different sources 17/08/2016 Mott MacDonald | Presentation title 39
  35. 35. 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

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