Evaluation of the environmental impacts of Intelligent Transport Systems and transport infrastructure management schemes relies heavily on the development of accurate and reliable environmental emissions models. Existing state-of-the-art models estimate pollutants based on a typical urban driving cycle using an aggregate modelling approach where a 'characteristic' vehicle is used to represent dissimilar vehicle populations. While this approach has been accepted by transport planners for strategic level studies, it can be argued that modelling individual vehicle emissions based on vehicle dynamics would result in more reliable evaluations of operational-level project impacts. The primary objective of this thesis is to develop vehicle emissions and fuel consumption models under hot stabilised settings and various traffic conditions using Australian fleet data collected from laboratory tests. The models use second-by-second vehicle real-time data to predict fuel consumption (FC) and pollutant emissions (HC, CO, NOx) at different levels of speed, acceleration, air-to-fuel ratio and torque. The data required for model development, calibration and validation was collated from laboratory tests conducted by the Second National In-Service Emissions (NISE 2) project. A total of 27 vehicles (including small, medium and large passenger vehicles; four-wheel drive (small and large); and light commercial vehicles were used in model development. The laboratory data, which comprised more than 48,500 second-by-second observations, was then pre-processed and randomly assigned to calibration and validation data sets for model development. The thesis then adopted a rigorous approach to develop and evaluate a large number of neural network architectures to determine the most suitable modelling framework. First, a pilot test was conducted to test different model development scenarios and establish some guidelines on the general framework for model development. The results were used to determine some of the crucial neural network parameters (eg learning rule or optimisation technique and most appropriate architecture) for use in subsequent modelling. Selected models were then further refined using test data from individual and aggregate vehicle types. This resulted in further refinement of modelling inputs where, for example, sensitivity analysis showed that speed and acceleration were the two most crucial inputs and that including other input parameters did not improve the accuracy of the results. The performance of selected neural network models was then compared to a number of sophisticated and complex statistical techniques based on multiple and non-linear regression models. The results generally showed that ANN models are effective and suitable for modelling emissions and that they perform as well or even better than the complex regression models tested in this study. Another general finding across all vehicles and for all models (neural and statistical) is that predictions are more accurate for fuel consumption and CO emissions than for other vehicular emissions. The models were also found to under-predict the emissions values at the peaks of graphs, but were generally consistent in their outputs across all other driving conditions. In this study, it was also found that one of the main advantages of the neural network approach over regression is the ease of developing one model to accurately predict multiple outputs. This is in contrast to the regression modelling approach, where it was found that accurate results matching neural network performance can only be achieved using one distinct model for predicting each output. This would clearly undermine the statistical approach because a large number of models would then need to be developed for a road network where second-by-second data is available for hundreds of vehicle types. Hence, the benefits of using neural networks immediately become clear and more appealing. This thesis also identified a number of issues for future research directions. To increase the accuracy and overall quality of the models, future research needs to include further classifications of vehicle types and other pertinent variables such as manufacture year, odometer reading and making use of a larger sample of modern vehicles representing current vehicle fleet compositions. There is also scope to improve the testing procedures by including road grade and air condition use, which are important factors that impact on vehicle performance and emissions. Future research can also benefit from testing other drive cycles and cross validation of models across different driving cycles. Model performance can also be enhanced by collecting instantaneous data using instrumented vehicles where emissions can be collected under real-life conditions rather than from controlled laboratory environments. Finally, the real benefit from development of these models is the ability to interface them to micro-simulation models where instantaneous speed and acceleration data can be provided to the emissions model on a second-by-second basis. The neural network emissions model would then be used to evaluate the impacts of ITS and other traffic management strategies with the aim of identifying the best environment-friendly traffic management approaches. This thesis has successfully achieved its objectives by demonstrating the feasibility of using neural networks for modelling vehicle emissions. The thesis further demonstrated the superior quality and advantages of the neural network approach over the more established statistical regression methods. Finally, the models developed in this study will allow researcher and practitioners alike to develop a better understanding and appreciation of the environmental impacts resulting from transport schemes aimed at reducing traffic congestion and enhancing environmental quality.

Evaluation of the environmental impacts of Intelligent Transport Systems and transport infrastructure management schemes relies heavily on the development of accurate and reliable environmental emissions models. Existing state-of-the-art models estimate pollutants based on a typical urban driving cycle using an aggregate modelling approach where a 'characteristic' vehicle is used to represent dissimilar vehicle populations. While this approach has been accepted by transport planners for strategic level studies, it can be argued that modelling individual vehicle emissions based on vehicle dynamics would result in more reliable evaluations of operational-level project impacts. The primary objective of this thesis is to develop vehicle emissions and fuel consumption models under hot stabilised settings and various traffic conditions using Australian fleet data collected from laboratory tests. The models use second-by-second vehicle real-time data to predict fuel consumption (FC) and pollutant emissions (HC, CO, NOx) at different levels of speed, acceleration, air-to-fuel ratio and torque. The data required for model development, calibration and validation was collated from laboratory tests conducted by the Second National In-Service Emissions (NISE 2) project. A total of 27 vehicles (including small, medium and large passenger vehicles; four-wheel drive (small and large); and light commercial vehicles were used in model development. The laboratory data, which comprised more than 48,500 second-by-second observations, was then pre-processed and randomly assigned to calibration and validation data sets for model development. The thesis then adopted a rigorous approach to develop and evaluate a large number of neural network architectures to determine the most suitable modelling framework. First, a pilot test was conducted to test different model development scenarios and establish some guidelines on the general framework for model development. The results were used to determine some of the crucial neural network parameters (eg learning rule or optimisation technique and most appropriate architecture) for use in subsequent modelling. Selected models were then further refined using test data from individual and aggregate vehicle types. This resulted in further refinement of modelling inputs where, for example, sensitivity analysis showed that speed and acceleration were the two most crucial inputs and that including other input parameters did not improve the accuracy of the results. The performance of selected neural network models was then compared to a number of sophisticated and complex statistical techniques based on multiple and non-linear regression models. The results generally showed that ANN models are effective and suitable for modelling emissions and that they perform as well or even better than the complex regression models tested in this study. Another general finding across all vehicles and for all models (neural and statistical) is that predictions are more accurate for fuel consumption and CO emissions than for other vehicular emissions. The models were also found to under-predict the emissions values at the peaks of graphs, but were generally consistent in their outputs across all other driving conditions. In this study, it was also found that one of the main advantages of the neural network approach over regression is the ease of developing one model to accurately predict multiple outputs. This is in contrast to the regression modelling approach, where it was found that accurate results matching neural network performance can only be achieved using one distinct model for predicting each output. This would clearly undermine the statistical approach because a large number of models would then need to be developed for a road network where second-by-second data is available for hundreds of vehicle types. Hence, the benefits of using neural networks immediately become clear and more appealing. This thesis also identified a number of issues for future research directions. To increase the accuracy and overall quality of the models, future research needs to include further classifications of vehicle types and other pertinent variables such as manufacture year, odometer reading and making use of a larger sample of modern vehicles representing current vehicle fleet compositions. There is also scope to improve the testing procedures by including road grade and air condition use, which are important factors that impact on vehicle performance and emissions. Future research can also benefit from testing other drive cycles and cross validation of models across different driving cycles. Model performance can also be enhanced by collecting instantaneous data using instrumented vehicles where emissions can be collected under real-life conditions rather than from controlled laboratory environments. Finally, the real benefit from development of these models is the ability to interface them to micro-simulation models where instantaneous speed and acceleration data can be provided to the emissions model on a second-by-second basis. The neural network emissions model would then be used to evaluate the impacts of ITS and other traffic management strategies with the aim of identifying the best environment-friendly traffic management approaches. This thesis has successfully achieved its objectives by demonstrating the feasibility of using neural networks for modelling vehicle emissions. The thesis further demonstrated the superior quality and advantages of the neural network approach over the more established statistical regression methods. Finally, the models developed in this study will allow researcher and practitioners alike to develop a better understanding and appreciation of the environmental impacts resulting from transport schemes aimed at reducing traffic congestion and enhancing environmental quality.

The fuel consumption of a seagoing vessel is, through a combination of Box-Cox transforms and principal component analysis, reduced to a univariatefunction of the primary principle component with mean model error −3.2%and error standard deviation 10.3%. In the process, a Latin-hypercube-inspired space partitioning sampling technique is developed and successfully used to produce a representative sampleused in determining the regression coefficients. Finally, a formal optimisation problem for minimising the fuel use is described. The problem is derived from a parametrised expression for the fuel consumption, and has only 3, or 2 if simplified, free variables at each timestep. Some information has been redacted in order to comply with NDA restrictions. Most redactions are either names (of vessels or otherwise), units, andin some cases (especially on figures) quantities. / <p>Presentation was performed remotely using Zoom.</p>

Performance models provided by aircraft manufacturers are used by aircraft operators to perform flight path simulations aiming to reduce aircraft fuel consumption. However, performance models are generic and does not account for the performance deviations of each aircraft individual. The performance deviations, particularly in terms of fuel consumption, will affect the dynamic programming of flight path simulations. This may result in a less optimal flight path and ultimately lead to higher fuel consumption than expected. In hope of reducing this risk, a collection of local performance factors were derived. These factors describe the percentual deviation between the real fuel flow and the levels predicted by the performance model, and are allocated with respect to a range of flight parameters in a data library known as the performance library. A test environment is then constructed to simulate a continuous flow of flight data, where a new performance library is derived from the flight data of every month. The local performance factors of the previous month are then updated with the current; a learning process based on the weighted average ensemble approach. Further, the local performance factors are used in conjunction with the performance model to estimate the aircraft fuel consumption during cruise. The observed average prediction error is noticeably smaller than that of an equivalent global, scalar performance factor used by airlines today. The result also reveals that the prediction accuracy and versatility of the performance library is mainly determined by its resolution - higher resolution generally offers better accuracy at a cost of requiring more flight data, whereas lower resolutions are more versatile but of lower accuracy. Finally, the performance libraries of two identical aircraft are used to trace the performance deviation between them. The weighted average of all local performance factors in the performance library of respective aircraft reveal that the average fuel consumption is roughly -1.9 % and -2.5 % lower than the estimates by the performance model, ultimately proving that it is feasible to detect overall fuel efficiency deviation between two identical aircraft. / Prestandamodeller tillhandhållna av flygplanstillverkarna används oftast av flygbolagen för att utföra flygruttsimuleringar i syfte att bespara bränsle. Dock är prestandamodellerna generiska och tillgodoräknar inte prestandaavvikelserna som förekommer hos varje flygplansindivid. Dessa prestandaavvikelser, speciellt i form av bränsleförbrukning, kommer att påverka den dynamiska programmeringen i flygruttssimulationen. Följde när flygrutter som kan leda till högre förbrukningar än de ursprungligen uppskattades. I hopp om att minimera denna risk beräknades mängder av lokala prestandafaktorer, vilka grundar på prestandamodellens avvikelse från verkliga flygdata. Dessa koefficienter allokerades sedan till ett databibliotek (prestandabibliotek) med avseende på en samling av flygparametrar. En testmiljö konstruerades i följd för att simulera ett kontinuerligt dataflöde. Vidare skapades ett prestandabibliotek för varje månadsflygdata, där de nyskapade lokala prestandafaktorerna viktas med de motsvarandeparterna i föregående månadens prestandabibliotek, vilket är en inlärningsprocessbaserad på viktad medelvärdesensemble. Prestandabiblioteket applicerades sedan över prestandamodellen och det snittliga uppskattning felet observerades vara märkbart mindre än det från en motsvarande global, skalärbaserad prestandafaktor. Resultatet antyder också på att prestandabibliotekets uppskattningsnoggrannhet och allsidighet beror huvudsakligen på dess upplösning - en hög upplösning leder generellt till ökad uppskattningsnoggrannhet med på bekostnad av mer flygdata, medan lägre upplösningar tenderar att vara mer allsidiga men med mindre uppskattningsnoggrannhet. Slutligen användes prestandabiblioteken av två identiska flygplan för att spåra prestandaavvikelser som förekommer mellan dem. Viktat medelvärde av alla prestandafaktorer i respektiveflygplanets prestandabibliotek tyder på att snittförbrukningen är ungefär 1,9 % respektive2,5 % lägre än det som uppskattades av prestandamodellen. Härmed bevisades att det är möjligt att spåra varianser i snittförbrukningen mellan två identiska flygplan.