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Title: Enhancing urban road traffic carbon dioxide emissions models
Author: Grote, Matthew John
ISNI:       0000 0004 6347 8781
Awarding Body: University of Southampton
Current Institution: University of Southampton
Date of Award: 2017
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The aim of this project was to provide a more accurate representation of road traffic carbon dioxide (CO2) emissions in urban areas, whilst remaining within limited Local Government Authority (LGA) resources. Tailpipe emissions from vehicles on urban roads have damaging impacts, with the problem exacerbated by the common occurrence of congestion. The scope of the project was CO2 because it is by far the largest constituent (99%) of road traffic greenhouse gas emissions. LGAs are typically responsible for facilitating mitigation of these emissions and must engage in emissions modelling to quantify the impact of transport interventions. A review of relevant literature identified a research gap, which constituted an investigation into whether a Traffic Variable Emissions Model (EM) (i.e. based on input data aggregated at traffic level rather than disaggregated at vehicle level) represented optimal complexity for LGAs, improving on the ability of wellestablished Average Speed EMs to capture the influence on emissions of congestion, whilst remaining within resource constraints. British LGAs (n=34) were surveyed to discover general attitudes to emissions modelling. Results showed that resource scarcity is important, with particular importance attached to EM reusability and convenient input data sources. Data sources rated highly for convenience were Urban Traffic Control (UTC) systems and Road Traffic Models (RTMs). A new Traffic Variable EM was developed termed the Practical EM for Local Authorities (PEMLA). Using Southampton as a testbed, 514 real-world GPS driving patterns (1Hz speed-time profiles) were collected from 49 drivers of different vehicle types and used as inputs to a detailed, instantaneous EM to calculate accurate vehicle CO2 emissions (assumed to represent 'real-world' emissions). Concurrent data were collected from Inductive Loop Detectors (ILDs installed as part of UTC systems) crossed by vehicles during their journeys and used to calculate values for selected traffic variables. Relationships between traffic variables (predictor variables) and accurate emissions (outcome variable) were examined using statistical analysis. Results showed that PEMLA outperformed the well-established, next-best alternative EM available to LGAs (an Average Speed EM), with mean predictions of PEMLA found to be 2% greater than observed values, whilst mean predictions of the alternative EM were 12% less. PEMLA's contribution is two-fold. Firstly, it is closer to optimal complexity than the well-established Average Speed EM alternative. This was for two reasons: (1) PEMLA was more accurate through using as inputs other traffic variable congestion indicators (in addition to traffic average speed), which improved its ability to capture the influence of congestion on emissions; and (2) PEMLA consumes similar (or potentially lower) resources to operate because inputs are generated from ILD data, which are a by-product of UTC systems or can be readily simulated in RTMs. Secondly, it possesses attributes that addressed the identified limitations of other Traffic Variable EM alternatives. These two contributions make PEMLA a suitable option to be recommended for LGA use.
Supervisor: Williams, Ian ; Preston, Jonathan ; Kemp, Simon Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available