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A practical model for predicting road traffic carbon dioxide emissions using Inductive Loop Detector data
- Source :
- Transportation Research Part D: Transport and Environment. 63:809-825
- Publication Year :
- 2018
- Publisher :
- Elsevier BV, 2018.
-
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.
- Subjects :
- 050210 logistics & transportation
Induction loop
business.industry
05 social sciences
Detector
Testbed
Transportation
010501 environmental sciences
01 natural sciences
Transport engineering
Vehicle dynamics
Outcome variable
0502 economics and business
Global Positioning System
Environmental science
business
Proxy (statistics)
Road traffic
0105 earth and related environmental sciences
General Environmental Science
Civil and Structural Engineering
Subjects
Details
- ISSN :
- 13619209
- Volume :
- 63
- Database :
- OpenAIRE
- Journal :
- Transportation Research Part D: Transport and Environment
- Accession number :
- edsair.doi.dedup.....860b2cbe9f45cd546c446bf2e6c39e2d