A practical model for predicting road traffic carbon dioxide emissions using Inductive Loop Detector data. (August 2018) - Grote, Matt; Williams, Ian; Preston, John; Kemp, Simon
Highlights: A practical network-level road traffic CO2 emissions model was developed. Model inputs are ILD data, a free by-product of Urban Traffic Control systems. Congestion's impact on emissions is captured using traffic variable model inputs. In highly specific tests, the model outperformed the next-best alternative. Further work is needed to see if this outperformance can be generalised. 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.