Simulating micro-level attributes of railway passengers using big data

Abstract

In the absence of a comprehensive, representative, and attribute-rich population, a spatial microsimulation is necessary to simulate or reconstruct a population for use in the analysis of complex mobility on the railways. Novel consumer datasets called ‘big-data’ are exhaustive but they only reveal a subset of the wider population who consume a specific digital service. Further, big-data are measured for a particular purpose and so do not have the broad spectrum of attributes required for their wider application. Harnessing big-data by spatial microsimulation has the potential to resolve the above shortcomings. This paper explores the relative merits of different spatial microsimulation methodologies, and a case study illustrates how best to simulate a micro-population linking rail ticketing big-data with the 2011 Census commute to work data and a National Rail Travel Survey (NRTS). The result is a representative attribute-rich micro-level population, which is likely to have a significant impact on the quality of inputs to strategic, tactical and operational rail-sector analysis planning models.