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Changing work and work-related travel and the impact of Covid-19 (2021)
Journal Article
Chatterjee, K., & Crawford, F. (in press). Changing work and work-related travel and the impact of Covid-19. Built Environment, 47(3),

The nature of work was undergoing dramatic change before the pandemic as the digital age continued to transform all sectors of our society. In this paper we describe pre-pandemic trends in types of work, the workforce and working arrangements in the... Read More about Changing work and work-related travel and the impact of Covid-19.

Analysing spatial intrapersonal variability of road users using point-to-point sensor data (2021)
Journal Article
Crawford, F., Connors, R., & Watling, D. (in press). Analysing spatial intrapersonal variability of road users using point-to-point sensor data. Networks and Spatial Economics, https://doi.org/10.1007/s11067-021-09539-4

The availability of newly emerging forms of data in recent years has provided new opportunities to study spatial intrapersonal variability, namely the variability in an individual’s destination and route choices from day to day. As well as providing... Read More about Analysing spatial intrapersonal variability of road users using point-to-point sensor data.

Segmenting travellers based on day-to-day variability in work-related travel behaviour (2020)
Journal Article
Crawford, F. (2020). Segmenting travellers based on day-to-day variability in work-related travel behaviour. Journal of Transport Geography, 86, https://doi.org/10.1016/j.jtrangeo.2020.102765

Travel needs for commute and business trips are complex and choices are not made based on the characteristics of individual trips, but instead based on the needs over weeks and months. For example, the cost per trip of commuting by bus varies dependi... Read More about Segmenting travellers based on day-to-day variability in work-related travel behaviour.

The potential for personalised public transport solutions to enhance job seekers' access to employment sites. (2018)
Presentation / Conference
Calvert, T., Crawford, F., Parkhurst, G., & Parkin, J. (2018, July). The potential for personalised public transport solutions to enhance job seekers' access to employment sites. Paper presented at Transport Practitioners' Meeting (TPM), Oxford, UK

This paper examines the importance of the accessibility of employment locations to job-seekers’ perceived work options. It investigates the potential of an employment site-oriented Personalised Collective Transport Service (PCTS) to help solve the li... Read More about The potential for personalised public transport solutions to enhance job seekers' access to employment sites..

Identifying road user classes based on repeated trip behaviour using Bluetooth data (2018)
Journal Article
Crawford, F., Watling, D. P., & Connors, R. D. (2018). Identifying road user classes based on repeated trip behaviour using Bluetooth data. Transportation Research Part A: Policy and Practice, 113, 55-74. https://doi.org/10.1016/j.tra.2018.03.027

© 2018 The Authors Analysing the repeated trip behaviour of travellers, including trip frequency and intrapersonal variability, can provide insights into traveller needs, flexibility and knowledge of the network, as well as inputs for models includin... Read More about Identifying road user classes based on repeated trip behaviour using Bluetooth data.

A statistical method for estimating predictable differences between daily traffic flow profiles (2016)
Journal Article
Crawford, F., Watling, D. P., & Connors, R. D. (2017). A statistical method for estimating predictable differences between daily traffic flow profiles. Transportation Research Part B: Methodological, 95, 196-213. https://doi.org/10.1016/j.trb.2016.11.004

It is well known that traffic flows in road networks may vary not only within the day but also between days. Existing models including day-to-day variability usually represent all variability as unpredictable fluctuations. In reality, however, some o... Read More about A statistical method for estimating predictable differences between daily traffic flow profiles.