a clustering method based on repeated trip behaviour to identify road user classes using bluetooth...

Institute for Transport StudiesFACULTY OF ENVIRONMENT

A clustering method based on repeated trip behaviour to identify road user classes using Bluetooth data

F. CrawfordInstitute for Transport Studies, University of LeedsEmail: ts12fc@leeds.ac.uk

Repeated trip making

Often assumed that urban traffic consists of commuters who drive between home and work at the same times each weekdayBut…• increases in part time, flexible and home working?• longer shop opening hours?

What proportion of travellers on the roads are these mythical regular commuters?

Point-to-point sensors e.g. Bluetooth

Methodology overview

Traveller 1: (s, t) (s, t) (s, t) (s, t) (s, t) (s, t) (s, t) (s, t) (s, t) (s, t) ….

Traveller 2: (s, t) (s, t) (s, t) (s, t) (s, t) (s, t) (s, t) (s, t) (s, t) (s, t) ….

Traveller 3: (s, t) (s, t) (s, t) (s, t) (s, t) (s, t) (s, t) (s, t) (s, t) (s, t) ….…….

Sensor 1 Sensor 2 Sensor 3 ………

………

Methodology overview

Traveller 1: (s, t) (s, t) (s, t) (s, t) (s, t) (s, t) (s, t) (s, t) (s, t) (s, t) ….

Traveller 2: (s, t) (s, t) (s, t) (s, t) (s, t) (s, t) (s, t) (s, t) (s, t) (s, t) ….

Traveller 3: (s, t) (s, t) (s, t) (s, t) (s, t) (s, t) (s, t) (s, t) (s, t) (s, t) ….…….

Sensor 1 Sensor 2 Sensor 3 ………

………

Methodology overview

Traveller 1: (s, t) (s, t) (s, t) (s, t) (s, t) (s, t) (s, t) (s, t) (s, t) (s, t) ….

Traveller 2: (s, t) (s, t) (s, t) (s, t) (s, t) (s, t) (s, t) (s, t) (s, t) (s, t) ….

Traveller 3: (s, t) (s, t) (s, t) (s, t) (s, t) (s, t) (s, t) (s, t) (s, t) (s, t) ….…….

Sensor 1 Sensor 2 Sensor 3 ………

Traveller 1: freq1, spat1, tod1

Traveller 2: freq2, spat2, tod2

Traveller 3: freq3, spat3, tod3

…….

Methodology overview

Traveller 1: (s, t) (s, t) (s, t) (s, t) (s, t) (s, t) (s, t) (s, t) (s, t) (s, t) ….

Traveller 2: (s, t) (s, t) (s, t) (s, t) (s, t) (s, t) (s, t) (s, t) (s, t) (s, t) ….

Traveller 3: (s, t) (s, t) (s, t) (s, t) (s, t) (s, t) (s, t) (s, t) (s, t) (s, t) ….…….

Sensor 1 Sensor 2 Sensor 3 ………

Traveller 1: freq1, spat1, tod1

Traveller 2: freq2, spat2, tod2

Traveller 3: freq3, spat3, tod3

…….

Cluster A Cluster DCluster CCluster B ………

Trip frequency

• Simply look at the number of trips per traveller in the data• Assume individual trips missing at random• Using data in this format can we calculate other measures

to provide other types of information?

Spatial variability: Sequence Alignment

A B D

E

C

F

- OD pairs?

Spatial variability: Sequence Alignment

A B D

E

C

F

- OD pairs?- Trip sequences?

Sequence Alignment

A B D

E

C

F

Seq1: ABDC

Spatial variability: Sequence Alignment

A B D

E

C

F

Seq1: ABDCSeq2: BEDF

Spatial variability:Sequence Alignment

Dissimilarity between sequence x and y:

Seq1: A B - D C

Seq2: - B E D F

Time of day variability

- Which are ‘comparable trips’? No information about trip purpose etc.

- Use as much data as possible- Time at most common site (likely to be near home/work?)- Avoid arbitrary cut-offs

The times of day I walk along my street

8am 5pm 8pm4pm7am 1pm

Time of day

Freq

uenc

y

The times of day I walk along my street

8am 5pm 8pm4pm7am 1pm

Time of day

Freq

uenc

y

Mixture of Gaussian Distributions?

Model-based clustering using Maximum Likelihood Estimation

Which cluster does each observation belong to?What are the parameters associated with each cluster?

Likelihood function:

P(X,Z|Ѳ)

- Expectation-Maximisation algorithm

Overall clustering

Traveller 1: (s, t) (s, t) (s, t) (s, t) (s, t) (s, t) (s, t) (s, t) (s, t) (s, t) ….

Traveller 2: (s, t) (s, t) (s, t) (s, t) (s, t) (s, t) (s, t) (s, t) (s, t) (s, t) ….

Traveller 3: (s, t) (s, t) (s, t) (s, t) (s, t) (s, t) (s, t) (s, t) (s, t) (s, t) ….…….

Sensor 1 Sensor 2 Sensor 3 ………

Traveller 1: freq1, spat1, tod1

Traveller 2: freq2, spat2, tod2

Traveller 3: freq3, spat3, tod3

…….

Cluster A Cluster DCluster CCluster B ………

Empirical example - Wigan

Data from the 23 fixed Bluetooth detectors in and around the town of Wigan (Figure 3) is analysed for a full year (2015). Data from the 23 fixed Bluetooth detectors in and around the town of Wigan (Figure 3) is analysed for a full year (2015).

A full year of data (2015) from 23 fixed Bluetooth detectors in and around Wigan

Trip frequency

The data for 2015 included:• 7.5 million trips• 327,264 unique MAC addresses• almost 28% of the travellers had only 1 trip• just 2% had greater than or equal to 260 trips (equivalent to

at least one trip per working day in the year)

Spatial variability

15 most common sequences in one spatial cluster

A-B-M-N-R-T-W A-B-G-N-R-T-W A-B-M-R-WA-B-G-M-N-R-T-W A-B-R-T-W A-B-M-N-S-WA-B-N-R-T-W A-B-M-R-T-W A-B-M-N-S-T-WB-G-M-N-R-T-W A-B-R-W A-B-G-M-R-T-WA-B-M-N-R-W A-B-N-R-W A-B-G-M-N-R-W

A B G

M

N WR

T

S

Road user classes

Using the Elbow Method, decided on 9 road user classes

Approximately 3 groups of 3:• infrequent (< 1 / week), • frequent, and • very frequent (> 1.5 / day)

Trips in 20150

500

1000

1500

2000

2500

2 4 12 92226

415

685

1177

2,308

Average trip per person

Infrequent travellers (ABC)

• 92% of travellers• 23% of trips

• Less than 1 trip per week (6 trips per year on average)• Intrapersonal variability?

Trips in 20150

2

4

6

8

10

12

14

A1.5

B4.2

C 12.3

Average trip per person

More frequent travellers

Freq travellers (DEF)

Very freq travellers (GHI)

Total trips observed 57%

Travellers observed 8%

Frequency 1/week to 1.5/day(50-550)

Average trips per spatial cluster

4-10

% trips in most common spatial cluster

29%

Average number of time of day clusters

2-4

Average time of day cluster variance

More trips -> more clusters with smaller

variance

More frequent travellers

Freq travellers (DEF)

Very freq travellers (GHI)

Total trips observed 57% 20%

Travellers observed 8% 0.5%

Frequency 1/week to 1.5/day(50-550)

>1.5/day(550-6155)

Average trips per spatial cluster

4-10 12-23

% trips in most common spatial cluster

29% 25-20%

Average number of time of day clusters

2-4 4.5-5.5

Average time of day cluster variance

More trips -> more clusters with smaller

variance

Smaller variance on average than DEF, but fairly constant by trips

Conclusions

• A method to identify road user classes was presented• Method was successfully applied to a fairly large case study

area• User classes depend on trip frequency and tell us about

spatial and temporal variability• Future work

Acknowledgements

Supervised by Professor David Watling and Dr Richard Connors at ITS

Funded by

Data from

http://www.its.leeds.ac.uk/people/f.crawford

Thank you for listening!

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