The potential of incorporating travel habits and behaviour in modelling carbon emissions in the transport system to help build a low carbon future

Ban, Bo (2016) The potential of incorporating travel habits and behaviour in modelling carbon emissions in the transport system to help build a low carbon future. (PhD thesis), Kingston University, .


As more in-depth research is carried out in global warming, greenhouse gases and relevant fields, scholars are no longer satisfied with the achievements they have gained in investigating the operating mechanism of greenhouse gases; they want to develop more complex and challenging measures to reduce the impact of human behaviours on the environment and further achieve sustainable development based on the harmony bewteen man and nature. The transport sector, in most countries, has been identified as one of the major sources of the greenhouse gases, second to the industrial and energy sectors, giving rise to the assertion that a proper understanding of the CO[sub]2 emission mechanism in the transport sector, would be helpful to policy makers and urban designers with regard to reducing the emissions of greenhouse gases. However, the concept and related studies of CO[sub]2 emission reduction are not yet completed, leading to an absence of a systematic understanding on the CO[sub]2 emissions from small-scale transport system in towns and small regions. Typically when trying to analyse the preference of commuters over transport modes, the increasing number of variables substantially complicates the model; further, an insufficiently clear logic among variables makes the model more complex and existing models do not address the problem clearly. To make a significant contribution to current knowledge, this study has developed a model, covering small-scale regions with full consideration of human activities, which adopts the concept of the grey system. The grey system enables researchers to use historical data to repair data records in the case of constraints and faults in the records. In addition, the model also uses the artificial network algorithm, which functions as a self-improving algorithm, provided that sufficient preliminary data are available. With resort to the self-learning ability and the fuzzy calculating function of the algorithm, the model could simulate and predict the decision making of commuters in order to infer the CO[sub]2 emissions in a small-scale region. Kingston-upon-Thames in the UK is the basis in this model which collects and analyses related data from the transport network in the area. The predictive outcome of the model is found to be consistent with the outcome from a survey conducted by the local council, supported by the UK government. Compared with traditional models, this model can use sporadic data as the basis of the analysis to ensure the accuracy of prediction while substantially reducing costs. In addition, it can build a distinctive data blueprint for specific research questions, satisfy the demand of practitioners and strategists and policy makers, and describe local transport networks with specific travel goals (such as a tourist bus). Moreover, this model is highly adaptable and developed in line with different research needs (policy making, transport system planning, travel behaviour simulation, etc.) in different target groups and regions. This study also summarizes the limitations of the model in its final section while specifying the future direction required to achieve further reduction in CO[sub]2 emissions from transport networks.

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