By Tom Cherrett and Fraser McLeod (University of Southampton) at the Internet of Cars Symposium, 24 Sept 2014, held at University of Southampton, UK.

1. Understanding traffic patterns and
regular travellers using registration
plate data
Tom Cherrett, Fraser McLeod
Transportation Research Group
University of Southampton

2. Characteristics of commuter traffic
- 70% of commutes involve
travelling on local roads in a city
or town
- 74% in employment usually
work in a single work place
- Only 16% of commuters have
more than 1 route to work

3. Characteristics of commuter traffic
- 34% change route because of traffic
seen up ahead
- > income or education levels use
more than one route to work
- > the JT, the > the frequency of route
change
- Males change route more
- Older commuters make less route
changes

4. Traditional network monitoring
Vehicle Detector
Controller Signal Queue Length
Plans
Signal Plans
Vehicle
Detected

5. ANPR for understanding behaviour

6. ANPR for understanding regulars
-Who should be arriving in the foreseeable future?
- How habitual are their behaviour patterns?
- Can we use ‘regulars’ as indicators or network state?

7. ANPR for vehicle analysis
25000
20000
15000
10000
5000
0
Number of observations
Year of registration

8. Research Questions
Using ANPR data:
• How habitual are vehicle arrival patterns?
• Can the arrival time variability of ‘regular ‘ vehicles be
used to gauge network performance?
• How does ‘churn’ affect the supply of ‘regular’ vehicles?
Could one use ‘regular’ vehicles as information carriers in
an ‘internet of cars’?

9. Dorset Test Site
• Dorchester to Weymouth
• 22 ANPR cameras
• 50 million observations over
12 months
Dorchester
Weymouth

10. Dorset ANPR data

11. Dorset ANPR data
• 50 million records and counting
• 118,200 records added each day
• Periods covered:
– 23/7/2012 to 12/11/2012
– 4/4/2013 onwards
• 76% of data have confidence level >=90%
6000000
5000000
4000000
3000000
2000000
1000000
0
COUNT(number)
0
32
35
38
41
44
47
50
53
56
59
62
65
68
71
74
77
80
83
86
89
92
95
98
number of plates in dorsetanpr
Confidence level

12. Average flow profile
(NB, weekdays only)
400
350
300
250
200
150
100
50
0
1 3 5 7 9 11 13 15 17 19 21 23
Average #plates recorded
Time of day

13. Unique readings - One-off visitors?
0%
5%
10%
15%
20%
25%
A1.1.NB.1
A1.2.NB.1
A1.SB.1
A10.1.NB.1
A10.1.SB.1
A11.1.NB.1
A11.1.SB.1
A12.1.NB.1
A12.1.SB.1
A13.1.NB.1
A13.1.SB.1
A14.1.BI.1
A15.1.IB.1
A15.1.OB.1
A16.1.IB.1
A16.1.OB.1
A3.1.NB.1
A3.1.SB.1
A4.1.EB.1
A4.1.WB.1
A7.1.NB.1
A7.1.SB.1

14. Regular vehicles
07:30
07:20
Example regular vehicle at A1.2.NB.1 (standard deviation = 85s)

15. How many vehicles are regular?
Total of regular vehicles across 22 sites (0630-0930)
Minimum number (percentage) days observed
(out of 227 days)
max 
(mins)
30
(13.2%)
50
(21.9%)
70
(30.8%)
90
(39.6%)
110
(48.5%)
5 2386 1346 861 567 357
7 4666 2740 1764 1137 729
10 8662 5299 3484 2317 1516
12 11590 7202 4788 3188 2079
15 16246 10267 6831 4538 2919

16. Number of vehicles with s <=10mins,
40+ appearances, Apr-Dec 2013, AM
Location
07:00
07:15
07:15
07:30
07:30
07:45
07:45
08:00
08:00
08:15
08:15
08:30
08:30
08:45
08:45
09:00 Total
A1.1.NB.1 26 25 21 26 23 18 18 9 166
A1.2.NB.1 11 15 12 17 14 18 13 5 105
A1.SB.1 10 13 6 23 35 21 35 26 169
A10.1.NB.1 6 10 11 16 20 27 16 24 130
A10.1.SB.1 12 10 16 10 10 15 20 17 110
A11.1.NB.1 6 17 21 8 31 34 37 31 185
A11.1.SB.1 8 14 19 16 21 35 29 12 154
A12.1.NB.1 35 26 44 32 34 37 39 22 269
A12.1.SB.1 23 27 40 58 62 44 40 42 336
A13.1.NB.1 25 26 27 29 47 59 56 34 303
A13.1.SB.1 19 21 25 27 16 17 13 14 152
A14.1.BI.1 5 8 9 18 18 42 65 19 184
A15.1.IB.1 6 4 10 15 23 32 36 16 142
A15.1.OB.1 6 11 11 5 3 7 8 2 53
A16.1.IB.1 22 29 23 29 25 26 21 16 191
A16.1.OB.1 9 16 25 13 20 17 24 6 130
A3.1.NB.1 39 37 32 23 46 50 51 24 302
A3.1.SB.1 20 23 38 46 63 75 52 54 371
A4.1.EB.1 3 4 10 6 12 16 13 16 80
A4.1.WB.1 4 10 9 9 14 18 34 6 104
A7.1.NB.1 10 18 13 19 18 32 24 22 156
A7.1.SB.1 5 11 14 14 23 44 32 37 180
Total 310 375 436 459 578 684 676 454 3972

17. Number of vehicles with s <=10mins
40+ appearances, Apr-Dec 2013, PM
Location
16:30
16:45
16:45
17:00
17:00
17:15
17:15
17:30
17:30
17:45
17:45
18:00
18:00
18:15
18:15
18:30 Total
A1.1.NB.1 1 0 2 1 0 0 0 2 6
A1.2.NB.1 2 4 2 4 0 0 0 0 12
A1.SB.1 5 0 2 6 3 1 4 1 22
A10.1.NB.1 0 2 1 2 0 0 1 3 9
A10.1.SB.1 4 8 5 4 1 2 1 1 26
A11.1.NB.1 2 2 4 3 0 0 3 0 14
A11.1.SB.1 0 2 0 0 0 0 2 1 5
A12.1.NB.1 4 2 8 2 2 2 0 1 21
A12.1.SB.1 11 3 3 8 6 5 2 3 41
A13.1.NB.1 13 9 13 5 3 0 1 0 44
A13.1.SB.1 4 2 0 0 2 1 0 2 11
A14.1.BI.1 14 19 9 4 7 0 1 0 54
A15.1.IB.1 2 3 1 1 5 0 0 0 12
A15.1.OB.1 0 0 0 0 0 2 0 0 2
A16.1.IB.1 3 2 2 2 1 0 0 1 11
A16.1.OB.1 14 6 24 5 1 2 1 1 54
A3.1.NB.1 9 8 9 5 2 0 0 3 36
A3.1.SB.1 10 11 1 8 4 7 2 3 46
A4.1.EB.1 0 1 2 2 0 0 1 0 6
A4.1.WB.1 1 2 1 3 0 0 1 2 10
A7.1.NB.1 2 3 1 2 3 1 2 1 15
A7.1.SB.1 1 5 0 1 0 2 3 0 12
Total 102 94 90 68 40 25 25 25 469

18. Can regular vehicles indicate
network performance?
1.2
1
0.8
0.6
0.4
0.2
0
-0.2
-0.4
-0.6
4/4/13 4/5/13 4/6/13 4/7/13 4/8/13
average standard score
Average standard scores at A12, northbound, 0745-0830

19. Vehicle lateness (A12, northbound,
Wed. 8/5/13, 0745-0830)

20. The problem of Churn
Turnover (‘Churn’) of regular vehicles occurs due to:
- Changes in vehicle ownership
- Changes in job status/working conditions
- Changes in home life
A traffic management system using the variability in arrival
rates of regular vehicles would need a constant update of
the ‘regular’ drivers
Churn was investigated by defining regular vehicles
(standard deviation of arrival time less than 10 minutes
based on more than 30 observations, 0630-0930)

21. Rolling analysis period
Churn investigated over rolling 4-month periods:
• Period 1 (P1) = 4/4/13 to 4/8/13
• Period 2 (P2) = 4/5/13 to 4/9/13
• Period 3 (P3) = 4/6/13 to 4/10/13
• Period 4 (P4) = 4/7/13 to 4/11/13
• Period 5 (P5) = 4/8/13 to 4/12/13
• Period 6 (P6) = 4/9/13 to 4/1/14
• Period 7 (P7) = 4/10/13 to 4/2/14
• Period 8 (P8) = 4/11/13 to 4/3/14

22. Rolling analysis period
30%
25%
20%
15%
10%
5%
0%
160
140
120
100
80
60
40
20
0
1 2 3 4 5 6 7 8
Percentage of vehicles
Number of vehicles
Number of 4-month periods in which vehicle was
a regular

23. Implications for network
management
- An additional method of monitoring issues on the
network
- Churn has implications for the supply of ‘regular’ vehicles
- How regular do vehicles need to be to identify potential
issues?
- Results suggest major roads during the morning
commute could be monitored in this way
- Interesting scope for live monitoring of different vehicle
types and CO2 footprints.