a clustering method based on repeated trip behaviour to identify road user classes using bluetooth...
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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: [email protected]
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!
Any questions?