Development of vehicle emissions models for Australian conditions

Abstract

Microscopic simulation models such as AIMSUN, VISSIM and/or PARAMICS have the ability to output emissions based on default values for emission factors derived mainly from European test data. Emission algorithms in those models are based on overseas vehicle emissions datasets, which do not reflect the different Australian vehicles, fuels, climate and fleet composition. The proposed research provides a set of emission algorithms to be used in conjunction with traffic simulation modelling, to better represent local conditions. Macro level models based on average vehicle speeds may not be appropriate for use at a more localized and detailed level when vehicle speed profiles may change significantly. Emission rates for a number of vehicles were compared using Australian data based on dynamometer The thesis discusses the limitations of existing emissions estimation approaches at the micro level. A methodology to establish emission models for predicting emission pollutants other than CO 2 is proposed. The models adopt a genetic algorithm approach to select the predicting variables. The approach is capable of solving combinatorial optimisation problems. Overall, iii the emission prediction results reveal that the proposed new models outperform conventional equations.There is a need to match emission modelling estimation to the accuracy levels of confidence in the outputs of transport models. In order to quantify the likely level of uncertainty attached to forecasts of emissions, an analysis of errors needs to be undertaken. The two major sources of error are the deficiency inherent in the model structure itself and the uncertainty in the input data used. This thesis deals with both of these error types in relation to CO 2 emissions modelling using a case-study from Brisbane, Australia. To estimate input data uncertainty, an analysis of different traffic conditions using Monte Carlo simulation is shown here. Model structure induced uncertainties are also quantified by statistical analysis for a number of traffic scenarios. To arrive at an optimal overall CO 2 prediction, the interaction between the two components was taken into account. Since a more complex model does not necessarily yield higher overall accuracy, a balanced solution needs to be found. The results obtained suggest that the CO 2 model used in the analysis produces low overall uncertainty under free flow traffic conditions. However, when average traffic speeds approach congested conditions, there are significant errors associated with emissions estimates.Using different scenarios for different road configurations and traffic conditions, the results of applying the new approach are compared with those obtained by using default emissions parameters commonly found in a simulation package.The enhancement of emission predictions rests to a large extent on the further improvements to traffic micro-simulation models. The results obtained suggest that the new approach produces low overall errors under several traffic conditions. The accuracy of emissions p...