title: Exploring Better Traveller Market Segmentation for Students and Commuters in Strategic Transport Models
Creators: name: Yan, Yiping
Publisher: name: Griffith University
Year: 2023
Subjects: subject: students
subject: commuters
subject: segmentation
subject: travel

description: Like most urban transport models, the Brisbane Strategic Transport Model (BSTM) still applies conventional market segmentation structures for both students and commuters, where workers are categorised solely as ‘white collar’ and ‘blue collar’ workers, and students are categorised as ‘primary school’, ‘secondary school’ and ‘tertiary’ students. Such market segmentation structures are clearly insufficient to capture today’s students and commuters in transport models, which can cause significant adverse impact on the model performance. Australia currently has one of the highest rates of private school enrolment in the OECD. The post-industrial employment sector has been experiencing a significant rise of women’s participation, especially in the service sector. To improve transport model’s prediction accuracy, market segmentation structure may require an update to more effectively describe the variation in travel demands of school students and workers. This research seeks to address this problem. The overarching research questions are: i) how can we better conceive and apply segmentation of students to better model travel demand? and, ii) how can we better conceive and apply segmentation of workers to better model travel demand? In terms of approach and methods, the research project adopts deductive approach (i.e., conventional travel behaviour analysis study and policy analysis study) for the research question 1, exploring the travel behaviour difference between public and private school students using the 2017-2019 South East Queensland Travel Survey dataset and adopts inductive approach (i.e., unsupervised clustering analysis study) for the research question 2 to investigate the underlying commuters’ market segmentation structure using the 2017-2020 South East Queensland Travel Survey (SEQTS) dataset. To answer the first research question, the first ever comparative study to look at private school travel at a regional scale is conducted and suggests there is clearly different travel mode preference and trip distance for each mode between public and private school students in the Southeast Queensland context. To answer the second research question, a novel market segmentation structure for commuters is proposed, revealing the hidden gender gap in the conventional blue/white collar market segmentation for workers and highlighting the importance of ‘pink collar’ market segment which mainly represents for female workers. The main contributions of this research project include 1) conceptual innovations in understandings of private school travel and female workers’ travel behaviours, 2) methodological innovations in applying the k-nearest neighbour algorithm to overcome the data limitation and applying a more robust clustering analysis - unsupervised Partition Around Medoid(PAM) clustering technique in revealing meaningful commuter market segments for transport models, 3) applied contributions as the results from this research provide novel market segmentation structures for both students and commuters for transport models to improve its forecasting accuracy.