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The segmentation of commuters into either blue or white-collar workers remains is still common in urban transport models. Internationally, models have started to use more elaborate segmentations, more reflective of changes in labour markets, such as increased female participation. Finding appropriate labour market segmentations for commute trip modelling remains a challenge. This paper harnesses a data-driven approach using unsupervised clustering–applied to 2017–20 South East Queensland Travel Survey (SEQTS) data. Commuter types are grouped by occupational, industry, and socio-demographic variables (i.e., gender, age, household size, household vehicle ownership and worker skill score). The results show that at a large number of clusters (i.e., k = 8) a highly distinct set of commuter types can be observed. But model run times tend to require a much smaller number of market segments. When only three clusters are formed (k = 3) a market segmentation emerges with one female-dominated type ('pink collar'), one male-dominated type ('blue collar') and one with both genders almost equally involved ('white collar'). There are nuances as to which workers are included in each segment, and differences in travel behaviours across the three types. 'Pink collar' workers are mostly comprised of female clerical and administrative workers, community and personal service workers and sales workers. They have the shortest median commutes for both private motorised and active transport modes. The approach and methods should assist transport planners to derive more accurate and robust market segmentations for use in large urban transport models, and, better predict the value of alternative transport projects and policies for all types of commuters. [ABSTRACT FROM AUTHOR]

Visitor dispersal is critical in spreading tourism benefits to peripheral regions, with imbalances of visitors a problem for many tourism organisations that warrants better tools for analysis. Gini decomposition of the length of stay and expenditures was used to measure visitor dispersal based on survey data of international visitors (n = 819) departing from Brisbane International Airport. Biplot visualisation and stacked contribution bar charts revealed multiple dimensions of visitor traits influencing dispersal across eight subregions in Queensland and their complex offsetting or compounding dispersion/concentration effects. Our results suggest that including expenditure provides a more comprehensive understanding of dispersal, highlighting "high spend, short stay" visitors. [ABSTRACT FROM AUTHOR]

While the period of heightened oil prices in 2005–2014 has attracted considerable academic and policy attention, there is scant cross-disciplinary research considering energy price, transport and land use together. In car dependent societies many socio-economically disadvantaged areas often lack public and active transport accessibility. Emerging research focus has been placed on issues of transport equity including effects of fuel prices. We reviewed 45 years (1972–2017) of publications about fuel price impacts, transport, and urban context, drawn from Web of Science listed publications. Bibliographic citation analysis reveals eight major research clusters with a set of inter-city comparative studies at their centre. The historical evolution, geographical trends, research approaches and the key themes are revealed by context analysis. The paper highlights the need for further studies looking at energy, transport and land use interaction, and suggests a greater focus on transport equity. [ABSTRACT FROM AUTHOR]

Passengers’ preference and origin location data (home postcodes) can be extremely useful for marketing purposes, with the potential for more effective customer segmentation and targeting, yet traditional airport market research has often neglected these aspects. This paper utilises passenger survey data from the Gold Coast Airport in the state of Queensland, Australia, to perform a geo-demographic classification analysis in conjunction with census data. With geo-coded passenger preference data, the trip characteristics and airport decision preferences were cross-compared with demographic data with socio-economic variables. The results revealed distinctive contrasts in passenger origin location for short-haul domestic trips and long-haul international trips, in which passengers from afar are willing to travel longer distances to reach a second-tier airport to make use of cheaper airfares. One of the implications of this study is that low-cost carriers’ businesses (the airlines or the airports) should better target their customers by offering geographically targeted marketing. [ABSTRACT FROM PUBLISHER]

This study quantified and visualised oil vulnerability (OV) across Statistical Local Areas (SLAs) of South East Queensland (SEQ), and its changes between 2001 to 2006, with specific focus on Master Planned Estates (MPEs). A classification chart was developed to categorise different types of OV, and to identify SLAs that showed significant change in their level of OV over the period from 2001 to 2006. Our findings are that non-metropolitan SLAs exhibited significantly higher OV due to: (i) the lack of alternatives to private motor vehicle use; (ii) longer road network distances travelled on average; and (iii) a generally lower socioeconomic status associated with these areas. Metropolitan SLAs had consistently lower OV due to: (i) a shorter average commuting distance; (ii) more comprehensive non-motorised access to public transport; and (iii) a generally higher socioeconomic status. SLAs containing large MPEs also proved to be more vulnerable due to their relatively high automobile dependence, limited non-motorised access to public transportation, and longer average commuting distances. [ABSTRACT FROM AUTHOR]