A practical method for predicting road traffic carbon dioxide emissions

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.

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Identifiers

ORCID for Matthew Grote: orcid.org/0000-0001-5590-7150

ORCID for Ian Williams: orcid.org/0000-0002-0121-1219

ORCID for Jonathan Preston: orcid.org/0000-0002-6866-049X

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