Hajmohammadi Hosseinabadi, Hajar; (2019) Microscopic Vehicle Emission Modelling. Doctoral thesis (Ph.D), UCL (University College London).

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Abstract

Vehicle emission models are widely used to estimate air pollution from road transport. This estimation can then be considered for transport management and traffic control policies, to quantify their impacts on urban air quality. The focus of this study is to investigate the relationship between vehicle dynamics and tailpipe emission by statistical methods. These methods are: log- polynomial and classified log-polynomial model based on acceleration and deceleration, lagged regression and transfer function model based on time series analysis, gear-based emission model based on estimated transmission gear components, and the general additive model for location, scale and shape (GAMLSS) based on spline functions. The dataset for this study is second-by-second emission laboratory measurements of four different vehicle types while following a driving cycle recorded in urban, suburban and motorway areas of London. The vehicles can be categorized by size (compact and saloon), fuel type (petrol and diesel) and transmission type (manual and automatic). For each vehicle type, CO2, CO and NOx emissions are estimated in each second of driving by the speed profile as the main explanatory variable. The six emission models developed in this study are: Log-polynomial (LP), classified log-polynomial (CLP), lagged regression (LR), transfer function (TF), gear-based and GAMLSS. These are evaluated using the BIC, total emission recovery and statistical time series analysis of the residuals. The GAMLSS model consistently has the best BIC values for all vehicle and emission types, while the recovery ratio of this model is within 1% for all vehicle types. In addition, statistical analysis of the ACF/PACF time series plots shows that the GAMLSS emission model is clearer from the significant lags compared to the parametric models (LP, TF, Gear-based, gear-based and CLP). Among the parametric models, the classified models represent the emission relationship better than others. The best BIC values (after GAMLSS) were achieved by the gear- based and the CLP emission models. These results indicate that the GAMLSS approach which uses spline functions and flexible error structure performs better than the other models investigated here. This model is validated by 10- fold cross-validation approach which shows that the prediction power of the GAMLSS emission model exceeds that of the parametric models. The models are evaluated by the BIC values, total emission recovery and analysis of the residuals. Based on these criteria, the GAMLSS emission model is the most effective, especially for CO and NOx emission modelling. This model is then validated by the K-fold cross-validation process. The suggestion for future research is to evaluate the performance of the developed models with track and real driving emission (RDE) tests. The calibrated model then will be implemented to a traffic microsimulation, where different transportation management and traffic policies can be simulated and evaluated by their impacts on air quality.

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