The University of Southampton
University of Southampton Institutional Repository

Identification of traffic profiling scheme from operational data

Identification of traffic profiling scheme from operational data
Identification of traffic profiling scheme from operational data
Historic traffic profiles are widely used in many traffic applications as baseline traffic conditions for comparison purposes. They are usually constructed from traffic data obtained from a number of previous similar days. The most widely used profiling scheme is based on natural day groups such as weekdays, generally selected based on common sense and limited observations. The research reported here has increased knowledge of profiling schemes through a large scale investigation of traffic patterns using operational traffic flow and travel time data obtained over an extensive period. A new traffic profiling scheme has been developed using a data-driven approach. Operational traffic and travel time data collected by the NTCC (National Traffic Control Centre) on the strategic roads across England over a period of two years have been used to identify the most similar day groups. A general scheme with 10 day-groups and an exponential updating algorithm has been identified. Test results have revealed that the new scheme performed better than the traditional weekday based scheme. The research established a comprehensive understanding of traffic flow and travel time patterns in historical data. It is believed that the profiling scheme and methodologies developed in this research could be useful for traffic data profiling practice
Zheng, P.
a46dbafc-a753-4f22-b825-a00fd36ebd44
McDonald, M.
81d8ff0b-d137-40c7-881d-1edb74ba8209
Jeffery, D.
6b14749b-4e69-45bb-976b-3bf197dc6c88
Zheng, P.
a46dbafc-a753-4f22-b825-a00fd36ebd44
McDonald, M.
81d8ff0b-d137-40c7-881d-1edb74ba8209
Jeffery, D.
6b14749b-4e69-45bb-976b-3bf197dc6c88

Zheng, P., McDonald, M. and Jeffery, D. (2008) Identification of traffic profiling scheme from operational data. 87th Annual Meeting of the Transportation Research Board, Washington, United States. 13 - 17 Jan 2008.

Record type: Conference or Workshop Item (Paper)

Abstract

Historic traffic profiles are widely used in many traffic applications as baseline traffic conditions for comparison purposes. They are usually constructed from traffic data obtained from a number of previous similar days. The most widely used profiling scheme is based on natural day groups such as weekdays, generally selected based on common sense and limited observations. The research reported here has increased knowledge of profiling schemes through a large scale investigation of traffic patterns using operational traffic flow and travel time data obtained over an extensive period. A new traffic profiling scheme has been developed using a data-driven approach. Operational traffic and travel time data collected by the NTCC (National Traffic Control Centre) on the strategic roads across England over a period of two years have been used to identify the most similar day groups. A general scheme with 10 day-groups and an exponential updating algorithm has been identified. Test results have revealed that the new scheme performed better than the traditional weekday based scheme. The research established a comprehensive understanding of traffic flow and travel time patterns in historical data. It is believed that the profiling scheme and methodologies developed in this research could be useful for traffic data profiling practice

This record has no associated files available for download.

More information

Published date: 2008
Venue - Dates: 87th Annual Meeting of the Transportation Research Board, Washington, United States, 2008-01-13 - 2008-01-17

Identifiers

Local EPrints ID: 52816
URI: http://eprints.soton.ac.uk/id/eprint/52816
PURE UUID: a1e91c1c-2b96-4536-b93a-4ce167a089e5

Catalogue record

Date deposited: 26 Aug 2008
Last modified: 11 Dec 2021 17:32

Export record

Contributors

Author: P. Zheng
Author: M. McDonald
Author: D. Jeffery

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×