building trip matrices from mobile phone data

1

Tom van Vuren

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Why this data source can no longer be ignored

Building trip matrices from mobile phone data

V1 01/05/2023 Mott MacDonald | Sydney

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Not just call detail records, but all active and passive event data:

Active events: @ cell levelPassive events: @ local area code (LAC) level

Cell size = 500m – 7.5km in diameter

What?

What not?

01/05/2023 Mott MacDonald | Presentation title 7

Weaknesses

• Lack of socio-demographic data on travellers

• Mode distinction poor

• Purpose distinction limited

• Short trip detection weak

• Privacy concerns


Strengths

• 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


Ethics and privacy

• 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)

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 monthsconsider spatial detail (zoning system) to minimise values <15be aware of segmentation (purpose, mode, time

periods)

And: previous RSIs on 2 cordons, 90 sites, cost > $1M: 3% of cells filled


How to determine a trip

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 providersAvoiding false positives in congested conditionsNot just stopped time; also accounts for traversing through LACs and cellsDwell times of 25 or 35 minutes decrease and increase the number of detected trips by approx. 10-12%

01/05/2023 Mott MacDonald | Presentation title

Estimating modes

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• 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 baseBut…• 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

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 ModesCentral 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)


Estimating purposes

• 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

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

• 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)

LegendLHR Population Data (2014 mid-year estimates) - Hybrid Zoning Option 1 v2ExpF1

0.000000 - 1.000000

1.000001 - 2.500000

2.500001 - 5.000000

5.000001 - 10.000000

10.000001 - 100000.000000

01/05/2023 27

Examples of current applications in the UK

Mott MacDonald | Presentation title

• 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.


Validating resulting matrices

Test ID

Demand Indicator Data Check / Comparison Analysis Approach Spatial Level

1Total 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

Example validation checks for expanded data

Test ID Demand Indicator Data Check / Comparison Analysis

ApproachSpatial 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


MPD Zones / District

- All day trip production vs. NTEM trip-ends, separately for HBW, HBO, and NHB.


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

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

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

SEWTM Spring ATCs (147 sites)


For discussion

• 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?


Comparing matrices from different sources

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

building trip matrices from mobile phone data

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