Methods for analysing emerging data sources to understand variability in traveller behaviour on the road network

This thesis argues that while simplifications are a necessary part of the modelling process, there is a lack of empirical research to identify which types of variability should be included in our models, and how they should be represented. This research aims to develop methodologies to undertake empirical analyses of variability on the road network, focusing specifically on traveller behaviour. This is particularly timely given the emergence of rich new data sources. Firstly, a method is proposed for examining predictable differences in daily link flow profiles by considering both magnitude and timing. Unlike previous methods, this approach can test for statistically significant differences whilst also comparing the shapes of the profiles, by applying Functional Linear Models to transportation data for the first time. Secondly, a flexible, data-driven method is proposed for classifying road users based on their trip frequency and spatial and temporal intrapersonal variability. Previous research has proposed methodologies for identifying public transport user classes based on repeated trip behaviour, but equivalent methods for data from the road network did not exist. As there was not an established data source to use, this research examines the feasibility of using Bluetooth data. Spatial variability is examined using Sequence Alignment which has not previously been applied to Bluetooth data from road networks, nor for spatial intrapersonal variability. The time of day variability analysis adapts a technique from smart card research so that all observations are classified into travel patterns and, therefore, systematic and random variability can be measured. These network- and traveller-focused analyses are then brought together using a framework which uses them concurrently and interactively to gain additional insights into traveller behaviour. For each of the methods proposed, an application to at least one year of real world data is presented.