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A practical method for predicting road traffic carbon dioxide emissions

A practical method for predicting road traffic carbon dioxide emissions
A practical method for predicting road traffic carbon dioxide emissions
Responsibility for roads outside a country’s strategic road network typically lies with Local Government Authorities (LGAs). LGAs have a key role therefore in facilitating the reduction of emissions from road traffic, 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 strike a balance between capturing the impact on emissions of vehicle dynamics (e.g. due to congestion), whilst remaining practical to use.
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 Global Positioning System (GPS) driving patterns (1Hz 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 (assumed to represent ‘real-world’ 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 categorization 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 observed values.
Grote, Matthew
f29566f9-42a7-498a-9671-8661a4287754
Williams, Ian
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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 (2017) A practical method for predicting road traffic carbon dioxide emissions. Transportation Research Board 96th Annual Meeting (TRB 2017), Washington, United States. 08 - 12 Jan 2017. 18 pp .

Record type: Conference or Workshop Item (Paper)

Abstract

Responsibility for roads outside a country’s strategic road network typically lies with Local Government Authorities (LGAs). LGAs have a key role therefore in facilitating the reduction of emissions from road traffic, 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 strike a balance between capturing the impact on emissions of vehicle dynamics (e.g. due to congestion), whilst remaining practical to use.
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 Global Positioning System (GPS) driving patterns (1Hz 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 (assumed to represent ‘real-world’ 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 categorization 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 observed values.

Text
Grote (2017) Practical method to predict CO2 emissions (Conf) - Accepted Manuscript
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More information

Published date: January 2017
Venue - Dates: Transportation Research Board 96th Annual Meeting (TRB 2017), Washington, United States, 2017-01-08 - 2017-01-12

Identifiers

Local EPrints ID: 426272
URI: http://eprints.soton.ac.uk/id/eprint/426272
PURE UUID: 09565f67-b1fc-474d-a202-ca46f908a8dc
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: 21 Nov 2018 17:30
Last modified: 08 Aug 2020 01:36

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