About the Project
Transport choice models have historically relied on manually collected survey data which are expensive to obtain and generally have limited sample sizes and lower update frequencies. They are also prone to biases and reporting errors. This has led to a growing reliance on data from surveys presenting people with hypothetical choices, which are themselves subject to different biases.
On the other hand, over the last decade, passively collected big data sources have emerged as a very promising source of mobility and energy usage data for researchers and practitioners. These include GPS traces, mobile phone records, bank and loyalty card transactions and geo-coded social-media data. But the application of these data have been primarily limited to visualizations and development of machine learning based predictions. The machine learning techniques for analyzing big data are however very often data-driven and lack behavioural underpinning which questions there applicability in predicting behaviour in radically different future scenarios.
In our recent research, we have developed methodologies to enhance mode choice models with large-scale GPS data (Calastri et al. 2017) where we proved how the panel nature of the data can be utilized to capture additional behavioural complexity. Further, we have developed methodologies to successfully use mobile phone data in modelling trip generation (Bwambale et al. 2017) and route choice behavior (Bwambale 2018). This research proposes to build on these novel methodologies and combine mobile phone and GPS data with data from traditional sources (household surveys, census, roadside interviews and sensor counts) to further enhance this research direction which is not only academically novel and challenging but also capable of producing results that have real world benefits. In particular, combining the choice modelling and machine learning techniques will be investigated and solutions will be formulated. Further, the potential to apply these types of big data in jointly modelling activity durations and patterns beyond transport will be investigated which can play a crucial role in developing next generation energy consumption models.
The effective combination of machine learning and choice modelling will enable us to make better use of emerging data and enable us to develop better and more comprehensive models, which can be updated more regularly and without new expensive data collection, allowing analysts to produce up to date results for the better evaluation of new transport and energy policies. Furthermore, the new methodologies will be immensely useful for smaller authorities or poorer countries were data and resource limitations are major barriers for developing transport and energy models.
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