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A practical model for predicting road traffic carbon dioxide emissions using inductive loop detector data

A practical model for predicting road traffic carbon dioxide emissions using inductive loop detector data
A practical model for predicting road traffic carbon dioxide emissions using inductive loop detector data
Local Government Authorities (LGAs) are typically responsible for roads outside a country’s strategic road network. LGAs play a key role therefore in facilitating the reduction of emissions from road traffic in urban areas, and must engage in emissions modelling to assess the impact(s) of transport interventions. Previous research has identified a requirement for road traffic Emissions Models (EMs) that balance capturing the impact on emissions of vehicle dynamics (e.g. due to congestion) against in-use practicality. This study developed such an EM through investigating the prediction of network-level carbon dioxide (CO2) emissions based on readily available data generated by Inductive Loop Detectors (ILDs) installed as part of Urban Traffic Control (UTC) systems.

Using Southampton, UK as a testbed, 514 GPS driving patterns (1 Hz speed-time profiles) were collected from 49 drivers of different vehicle types and used as inputs to an Instantaneous EM to calculate accurate vehicle emissions. In parallel, concurrent data were collected from ILDs crossed by vehicles during their journeys. Statistical analysis was used to examine relationships between traffic variables derived from the ILD data (predictor variables) and accurate emissions (outcome variable). Results showed that ILD data (when used in conjunction with categorisation of vehicle types) can form the basis for a practical road traffic CO2 EM that outperforms the next-best alternative EM available to LGAs, with mean predictions found to be 2% greater than proxy real-world values.
1361-9209
809-825
Grote, Matthew
f29566f9-42a7-498a-9671-8661a4287754
Williams, Ian
c9d674ac-ee69-4937-ab43-17e716266e22
Preston, Jonathan
ef81c42e-c896-4768-92d1-052662037f0b
Kemp, Simon
942b35c0-3584-4ca1-bf9e-5f07790d6e36
Grote, Matthew
f29566f9-42a7-498a-9671-8661a4287754
Williams, Ian
c9d674ac-ee69-4937-ab43-17e716266e22
Preston, Jonathan
ef81c42e-c896-4768-92d1-052662037f0b
Kemp, Simon
942b35c0-3584-4ca1-bf9e-5f07790d6e36

Grote, Matthew, Williams, Ian, Preston, Jonathan and Kemp, Simon (2018) A practical model for predicting road traffic carbon dioxide emissions using inductive loop detector data. Transportation Research Part D: Transport and Environment, 63, 809-825. (doi:10.1016/j.trd.2018.06.026).

Record type: Article

Abstract

Local Government Authorities (LGAs) are typically responsible for roads outside a country’s strategic road network. LGAs play a key role therefore in facilitating the reduction of emissions from road traffic in urban areas, and must engage in emissions modelling to assess the impact(s) of transport interventions. Previous research has identified a requirement for road traffic Emissions Models (EMs) that balance capturing the impact on emissions of vehicle dynamics (e.g. due to congestion) against in-use practicality. This study developed such an EM through investigating the prediction of network-level carbon dioxide (CO2) emissions based on readily available data generated by Inductive Loop Detectors (ILDs) installed as part of Urban Traffic Control (UTC) systems.

Using Southampton, UK as a testbed, 514 GPS driving patterns (1 Hz speed-time profiles) were collected from 49 drivers of different vehicle types and used as inputs to an Instantaneous EM to calculate accurate vehicle emissions. In parallel, concurrent data were collected from ILDs crossed by vehicles during their journeys. Statistical analysis was used to examine relationships between traffic variables derived from the ILD data (predictor variables) and accurate emissions (outcome variable). Results showed that ILD data (when used in conjunction with categorisation of vehicle types) can form the basis for a practical road traffic CO2 EM that outperforms the next-best alternative EM available to LGAs, with mean predictions found to be 2% greater than proxy real-world values.

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Practical method for predicting road traffic CO2 emissions v23 - Accepted Manuscript
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Grote (2018) Practical model to predict emissions using ILD data - Version of Record
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More information

Accepted/In Press date: 29 June 2018
e-pub ahead of print date: 20 July 2018
Published date: August 2018

Identifiers

Local EPrints ID: 422681
URI: http://eprints.soton.ac.uk/id/eprint/422681
ISSN: 1361-9209
PURE UUID: 5e2412ae-0259-4a95-9ce2-d4b895ce8699
ORCID for Ian Williams: ORCID iD orcid.org/0000-0002-0121-1219
ORCID for Jonathan Preston: ORCID iD orcid.org/0000-0002-6866-049X

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Date deposited: 30 Jul 2018 16:30
Last modified: 17 Dec 2019 05:18

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Contributors

Author: Matthew Grote
Author: Ian Williams ORCID iD
Author: Simon Kemp

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