Search

Recent years saw a continuing shift in labour force composition, e.g. greater participation of women and a prominent rise in part-time workers. There are as yet relatively few recent studies that examine systematically the influences on the travel of employed adults from such perspectives, particularly regarding possible transport disadvantages of the fastest growing segments of workers. A robust analysis requires systematic data on a wide range of explanatory variables and multiple travel outcomes including accessibility, mobility and trip frequency for different trip purposes. The UK NTS data does meet the majority of this demanding data requirement, but its full use has so far been hampered by methodological difficulties. To overcome complex endogeneity problems, we develop novel, integrated structural equation models (SEMs) to uncover the influences of latent land use characteristics, indirect influences on car ownership, interactions among trip purposes as well as residents’ self-selection and spatial sorting. This general-purpose method provides a new, systematic decomposition of the influences on travel outcomes, where the effects of each variable can be examined in turn with robust error terms. The new insights underline two direct policy implications. First, it highlights the contributions of land use planning and urban design in restraining travel demand in the 2000s, and their increasing influence over the decade. Secondly, it shows that there may still be a large mobility disadvantage among the fastest growing segments of workers, particularly in dense urban areas. This research further investigates trend breaking influences before and after 2007 through grouped SEM models, as a test of the methodology for producing regular and timely updates regarding the main influences on personal travel from a system level. [ABSTRACT FROM AUTHOR]

Abstract Rates of cycling to work vary significantly from one urban area to another but the reasons for these variations are not well understood. Existing literature highlights the importance of built environments, urban amenities, and high-quality bicycle networks in promoting cycling. However, few studies measure the respective contributions and weigh the collective magnitude of effects of these influences together. We present a multivariate model that reflects the influences of such factors for 36 cities and towns in Britain. The models reveal a complex web of forces shaping cycling to work, confirming that there is no single, silver-bullet factor even in cities with remarkably high commuter cycling. The model results highlight the importance in joining up network level interventions, for instance to reduce both route circuity and on-road stress, which are objectives often being pursued separately. The results also highlight the importance of non-transport aspects such as land use mix and landscape amenities along commuter routes, and the role of city-specific cycling culture. They also underscore the need for closer collaboration between promoters of commuter cycling and wider urban disciplines to create low-stress routes and supportive built environments in cities and their outskirts. Highlights • A complex web of forces shape cycle commuting in British cities, confirming there is no single, silver-bullet factor. • Joining up network level interventions is critical, e.g. lower route circuity and stress, but are often pursued separately. • Non-transport aspects such as land use mix and landscape amenities along commuter routes are important. • Collaboration between promoters of commuter cycling and wider urban disciplines is required to create supportive built environments. [ABSTRACT FROM AUTHOR]

A large evidence base demonstrates that the outcomes of COVID-19 and national and local interventions are not distributed equally across different communities. The need to inform policies and mitigation measures aimed at reducing the spread of COVID-19 highlights the need to understand the complex links between our daily activities and COVID-19 transmission that reflect the characteristics of British society. As a result of a partnership between academic and private sector researchers, we introduce a novel data driven modelling framework together with a computationally efficient approach to running complex simulation models of this type. We demonstrate the power and spatial flexibility of the framework to assess the effects of different interventions in a case study where the effects of the first UK national lockdown are estimated for the county of Devon. Here we find that an earlier lockdown is estimated to result in a lower peak in COVID-19 cases and 47% fewer infections overall during the initial COVID-19 outbreak. The framework we outline here will be crucial in gaining a greater understanding of the effects of policy interventions in different areas and within different populations. • A microsimulation framework for modelling disease transmission within communities. • Integration of data from multiple, diverse sources to estimate exposure and risk. • Computationally efficient implementation allows rapid assessment of scenarios. • Allows the effects of different non-pharmaceutical interventions to be assessed. • Results show substantial reduction in infections associated with earlier UK lockdown. [ABSTRACT FROM AUTHOR]