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Using automatic number plate recognition data to investigate the regularity of vehicle arrivals

Using automatic number plate recognition data to investigate the regularity of vehicle arrivals
Using automatic number plate recognition data to investigate the regularity of vehicle arrivals
This paper uses automatically-recorded vehicle number plate data obtained from a network of 22 cameras in Dorset, UK, over a period of ten months, to investigate the extent to which regular trip making can be determined using the regularity of individual vehicle arrival times across the same sites and time intervals over extended periods of several months and how a cohort of recognised regular vehicles may provide indicative evidence of traffic delays given the levels of churn one may expect in these cohorts over time. Regularity was defined based on a minimum number of observations over a given period and with a specified maximum value of standard deviation in arrival time, with sensitivity to different values being tested. It was found that around one-fifth of all vehicles were regular during the morning peak where the definition required at least 30 observations out of 210 working days and with a standard deviation in arrival time of no more than ten minutes; significantly fewer vehicles were found to be regular in the afternoon peak. The turnover, or churn, of regular vehicles was found to be considerable, with only one-tenth of defined regular vehicles being continuously regular throughout the period and with identified pools of regular drivers halving in size every three months, as vehicles ceased to be regular and where the pool was not updated. This suggests that any database of regular drivers should be updated at least quarterly to ensure that new regular vehicles are included and that old ones are discarded. These findings may have inferences for traffic information systems tailored for different driver groups according to assumed levels of network knowledge.

1567-7141
86-102
Mcleod, Fraser
93da13ec-7f81-470f-8a01-9339e80abe98
Cherrett, Tom
e5929951-e97c-4720-96a8-3e586f2d5f95
Box, Simon
2bc3f3c9-514a-41b8-bd55-a8b34fd11113
Waterson, Ben
60a59616-54f7-4c31-920d-975583953286
Pritchard, James
6eabbdbc-385b-4636-9bd5-c0ac239f2351
Mcleod, Fraser
93da13ec-7f81-470f-8a01-9339e80abe98
Cherrett, Tom
e5929951-e97c-4720-96a8-3e586f2d5f95
Box, Simon
2bc3f3c9-514a-41b8-bd55-a8b34fd11113
Waterson, Ben
60a59616-54f7-4c31-920d-975583953286
Pritchard, James
6eabbdbc-385b-4636-9bd5-c0ac239f2351

Mcleod, Fraser, Cherrett, Tom, Box, Simon, Waterson, Ben and Pritchard, James (2017) Using automatic number plate recognition data to investigate the regularity of vehicle arrivals. European Journal of Transportation and Infrastructure Research, 17 (1), 86-102.

Record type: Article

Abstract

This paper uses automatically-recorded vehicle number plate data obtained from a network of 22 cameras in Dorset, UK, over a period of ten months, to investigate the extent to which regular trip making can be determined using the regularity of individual vehicle arrival times across the same sites and time intervals over extended periods of several months and how a cohort of recognised regular vehicles may provide indicative evidence of traffic delays given the levels of churn one may expect in these cohorts over time. Regularity was defined based on a minimum number of observations over a given period and with a specified maximum value of standard deviation in arrival time, with sensitivity to different values being tested. It was found that around one-fifth of all vehicles were regular during the morning peak where the definition required at least 30 observations out of 210 working days and with a standard deviation in arrival time of no more than ten minutes; significantly fewer vehicles were found to be regular in the afternoon peak. The turnover, or churn, of regular vehicles was found to be considerable, with only one-tenth of defined regular vehicles being continuously regular throughout the period and with identified pools of regular drivers halving in size every three months, as vehicles ceased to be regular and where the pool was not updated. This suggests that any database of regular drivers should be updated at least quarterly to ensure that new regular vehicles are included and that old ones are discarded. These findings may have inferences for traffic information systems tailored for different driver groups according to assumed levels of network knowledge.

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Using automatic number plate recognition data to investigate the regularity of vehicle arrivals - Accepted Manuscript
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More information

Accepted/In Press date: 25 October 2016
Published date: January 2017
Organisations: Transportation Group

Identifiers

Local EPrints ID: 402568
URI: http://eprints.soton.ac.uk/id/eprint/402568
ISSN: 1567-7141
PURE UUID: 8a97f030-e71b-405c-93bd-89fb21d7373d
ORCID for Fraser Mcleod: ORCID iD orcid.org/0000-0002-5784-9342
ORCID for Ben Waterson: ORCID iD orcid.org/0000-0001-9817-7119

Catalogue record

Date deposited: 11 Nov 2016 15:18
Last modified: 18 Feb 2021 16:51

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