Advance transit oriented development typology: case study in Brisbane,Australia |
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| Institution: | 1. School of Civil Engineering and the Built Environment, Queensland University of Technology, 2 George Street, Brisbane, Queensland 4000, Australia;2. School of Public Health and Social Work, Queensland University of Technology, Victoria Park Road, Kelvin Grove, Brisbane, Queensland 4059, Australia;1. Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, PO Box 217, 7500 AE Enschede, The Netherlands;2. Centre for Transport Studies, Department of Civil Engineering, Faculty of Engineering and the Built Environment, University of Cape Town, Private Bag X3, Rondebosch, 7701 Cape Town, South Africa;1. China Sustainable Transportation Center, 19 Jianguomenwai Avenue, CITIC Building, Room1903, Beijing 100004, China;2. Cockrell School of Engineering, The University of Texas at Austin, USA;3. School of Architecture, Tsinghua University, Room500, Beijing 100084, China;4. Department of Urban Studies and Planning, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Room 10-485, Cambridge, MA 02139, USA;1. School of Civil Engineering and the Built Environment, Queensland University of Technology, 2 George Street, Brisbane, Queensland, Australia;2. School of Population Health, The University of Western Australia, Nedlands, Western Australia 6009, Australia;3. School of the Built Environment, University of Ulster, Shore Road, Newtownabbey, County Antrim BT37 0QB, Northern Ireland, UK;4. Institute of Transport Studies, Monash University, Clayton, Victoria, Australia;5. McCaughey VicHealth Centre for Community Wellbeing, Melbourne School of Population Health, University of Melbourne, Melbourne, Victoria 3010, Australia;6. School of Public Health and Social Work, Queensland University of Technology, Victoria Park Road, Kelvin Grove, Brisbane, Queensland 4059, Australia;1. School of Civil Engineering and the Built Environment, Queensland University of Technology, 2 George Street, Brisbane, Queensland 4000, Australia.;2. School of Earth Environment and Biological Sciences, Queensland University of Technology, 2 George Street, Brisbane, Queensland 4000, Australia.;3. School of the Built Environment, University of Ulster, Shore Road, Newtownabbey, County Antrim, BT37 0QB Northern Ireland, UK;4. School of Public Health and Social Work, Queensland University of Technology, Victoria Park Road, Kelvin Grove, Brisbane, Queensland 4059, Australia. |
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| Abstract: | Internationally, transit oriented development (TOD) is characterised by moderate to high density development with diverse land use patterns and well connected street networks centred around high frequency transit stops (bus and rail). Although different TOD typologies have been developed in different contexts, they are based on subjective evaluation criteria derived from the context in which they are built and typically lack a validation measure. Arguably there exist sets of TOD characteristics that perform better in certain contexts, and being able to optimise TOD effectiveness would facilitate planning and supporting policy development. This research utilises data from census collection districts (CCDs) in Brisbane with different sets of TOD attributes measured across six objectively quantified built environmental indicators: net employment density, net residential density, land use diversity, intersection density, cul-de-sac density, and public transport accessibility. Using these measures, a Two Step Cluster Analysis was conducted to identify natural groupings of the CCDs with similar profiles, resulting in four unique TOD clusters: (a) residential TODs, (b) activity centre TODs, (c) potential TODs, and (d) TOD non-suitability. The typologies are validated by estimating a multinomial logistic regression model in order to understand the mode choice behaviour of 10,013 individuals living in these areas. Results indicate that in comparison to people living in areas classified as residential TODs, people who reside in non-TOD clusters were significantly less likely to use public transport (PT) (1.4 times), and active transport (4 times) compared to the car. People living in areas classified as potential TODs were 1.3 times less likely to use PT, and 2.5 times less likely to use active transport compared to using the car. Only a little difference in mode choice behaviour was evident between people living in areas classified as residential TODs and activity centre TODs. The results suggest that: (a) two types of TODs may be suitable for classification and effect mode choice in Brisbane; (b) TOD typology should be developed based on their TOD profile and performance matrices; (c) both bus stop and train station based TODs are suitable for development in Brisbane. |
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| Keywords: | Transit Oriented Development (TOD) TOD typology Advanced TOD planning Mode choice behaviour Public transport accessibility level (PTAL) Brisbane |
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