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. They have been used successfully for human travel pattern visualization, route choice modelling, traffic model calibration and traffic flow estimation. Despite the obvious opportunity to reduce survey costs and improve information availability in a large scale travel behaviour context (e.g. mode, route, destination and departure time choice), methodological limitations and practical issues have reduced the applicability (and acceptability) of these passively collected data in practice.
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 behaviour (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, measures to account for the sampling bias, coarse resolution, discontinuities and lack of user info in the data 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 use of emerging data will 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.
How to Apply: You must submit an online PhD application by the deadline stated above. Details of how to apply can be found here: http://www.its.leeds.ac.uk/courses/phd/apply/
. You must clearly state the name of your chosen project in the project details. You do not need to include a research proposal, but you do need to upload a ‘statement of motivation’. This should be 1-2 pages and should include information about why you feel you are well suited to the topic. This may refer to your academic background and any other relevant experience and could include an indication of how you would choose to interpret the project. Any enquiries about the application process can be sent to Deborah Goddard ([email protected]
Informal enquiries about the project can be sent to Dr Charisma Choudhury ([email protected]
Entry requirements: The minimum requirement is a UK Upper Second Class Honours or equivalent in a Quantitative Discipline.
• Strong numerical aptitude
• Some experience in computer programming
• Interest in choice and behaviour modelling using Big Data
Further information about entry requirements can be found here: http://www.its.leeds.ac.uk/courses/phd/apply/
Calastri C; Hess S; Choudhury C; Daly A; Gabrielli L (2017) Mode choice with latent availability and consideration: Theory and a case study, Transportation Research Part B: Methodological, doi: 10.1016/j.trb.2017.06.016
Bwambale A; Choudhury CF; Hess S (2017) Modelling trip generation using mobile phone data: A latent demographics approach, Journal of Transport Geography, doi: 10.1016/j.jtrangeo.2017.08.020
Bwambale A; Choudhury CF; Hess S (2018) Modelling long-distance route choice using mobile phone data: A case study of Senegal, Annual Meeting of the Transportation Research Board, Washington DC (forthcoming)