Including congestion effects in urban road traffic CO2 emissions modelling: Do Local Government Authorities have the right options?. (March 2016)

• Record Type: Journal Article
• Authors: Grote, Matt; Williams, Ian; Preston, John; Kemp, Simon
• Is Part Of: Transportation research. Volume 43(2016)
• Page Start: 95
• Page End: 106
• Publication Date: 2016-03
• Subjects: Road traffic model -- Emissions model -- Greenhouse gas -- Air quality -- Congestion -- Local government; Transportation -- Research -- Periodicals; Transportation -- Environmental aspects -- Periodicals; 354.76
• DOI: 10.1016/j.trd.2015.12.010
• Languages: English
• ISSNs: 1361-9209

Abstract

Highlights: LGAs are responsible for facilitating mitigation of urban road traffic emissions. LGAs require models that account for congestion impacts on network level emissions. Predicting emissions from traffic variables represents optimal model complexity. LGAs do not necessarily have the right options to meet their requirements. Research is required to further develop traffic variable emissions models. Abstract: Tailpipe emissions from vehicles on urban road networks have damaging impacts, with the problem exacerbated by the common occurrence of congestion. This article focuses on carbon dioxide because it is the largest constituent of road traffic greenhouse gas emissions. Local Government Authorities (LGAs) are typically responsible for facilitating mitigation of these emissions, and critical to this task is the ability to assess the impact of transport interventions on road traffic emissions for a whole network. This article presents a contemporary review of literature concerning road traffic data and its use by LGAs in emissions models (EMs). Emphasis on the practicalities of using data readily available to LGAs to estimate network level emissions and inform effective policy is a relatively new research area, and this article summarises achievements so far. Results of the literature review indicate that readily available data are aggregated at traffic level rather than disaggregated at individual vehicle level. Hence, a hypothesis is put forward that optimal EM complexity is one using traffic variables as inputs, allowing LGAs to capture the influence of congestion whilst avoiding the complexity of detailed EMs that estimate emissions at vehicle level. Existing methodologies for estimating network emissions based on traffic variables typically have limitations. Conclusions are that LGAs do not necessarily have the right options, and that more research in this domain is required, both to quantify accuracy and to further develop EMs that explicitly include congestion, whilst remaining within LGA resource constraints.