Session 2015-16 - Closing Date Extended to 13 April 2015 (23:59 pm)

Further information on the application procedure can be found at: www.leeds.ac.uk/rsa/postgraduate_scholarships/esrc-info

Project Partner: Mott MacDonald

Transport and mobility models have traditionally relied on manually collected survey data which are expensive to obtain and thereby generally have limited sample sizes and lower update frequencies and are prone to biases and reporting errors. On the other hand, over the last decade, passively collected big data sources have emerged as a very promising source of mobility data for researchers and practitioners. These include GPS tracts, mobile phone records, card transactions and geo-coded social-media data which 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 transportation planning context, methodological limitations and practical issues have reduced the applicability (and acceptability) of these passively collected data in practice.

This research proposes to combine mobile phone and GPS data with data from traditional sources (household surveys, census, roadside interviews and sensor counts) and develop robust transport models that utilize the strengths of the mobile phone and GPS data to complement the traditional data sources and vice versa. 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. Both empirical and simulation based methodologies will be explored in this regard.

Effective use of emerging data will enable us to develop stronger, more comprehensive models, faster and cheaper – removing previous barriers for smaller authorities or poorer countries.