City Futures Mobility

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The Mobility Program explores how urban travel shapes our environment, economy, health, and wellbeing. Our vision is that walking, cycling, micromobility, and public transport are the easiest, most natural choices. Through rigorous research and real-world solutions, we shape policy, planning, design, and delivery models that make low-impact, people-focused mobility the default, helping cities meet ambitious environmental, economic, and social goals while enhancing everyday quality of life.

Competitive advantage 

We are a multidisciplinary research team specialising in mobility systems innovation. Our expertise spans urban design, urban planning, landscape architecture, transport planning, economics, data analytics, and modelling, enabling integrated analysis and evidence-based solutions. We collaborate with stakeholders across public and private sectors, apply rigorous research to inform policy, optimise planning processes, and advance implementation strategies. Our projects drive measurable improvements in accessibility, environmental, economic, and enjoyment performance of urban transport networks and the places they interact with.

Our team is connected with local, state, national, and international networks at the forefront of mobility, ensuring we remain informed and engaged with emerging trends and innovations. Recent research projects include infrastructure scenario planning, using VR to test new technologies and street design concepts (national and state projects), analysing local government project delivery models and optimising them through consensus building (international, state and local projects), conducting surveys from national to local street scales (national and local projects), and undertaking project evaluations (international, state and local projects).

Impact

Our research portfolio encompasses a diverse array of impactful projects. Some notable initiatives include contributing to the development of NSW cycleway design guidelines (iMOVE CRC). A key objective of this project was to test the designs in the NSW Cycleway Design Toolbox with ‘interested but concerned’ cyclists and inform the first review and next iteration of the Toolbox. Our team has created an interactive tool for rapid assessment of cycling infrastructure scenarios, optimising the location, routing, and types of cycleways to increase investment return (ARC Linkage). We are collaborating with Transport for NSW, CWANZ, 12 NSW local governments, the Committee for Sydney, and Vivendi Consulting to examine and optimise cycleway project delivery practices (ARC Fellowship). Our efforts extend to establishing a national platform for cycling data and analytics (ARC LIEF) and we continue to collaborate on an Australian-Canadian initiative with our colleagues at Monash University on interventions for all ages and abilities in bicycle networks and speed management (NHMRC).

Successful applications and research highlights

Over the past five years the Mobility Program has been successful in securing several major grants including:

  • two-year grant from iMOVE CRC with Transport for NSW “Safer cycling and street design: A guide for policymakers”
  •  two-year ARC Linkage Infrastructure, Equipment and Facilities (LIEF) grant “The National Cycling Data and Analytics Platform” 
  • five-year National Health and Medical Research Council (NHMRC) grant: “Building CapaCITY/É for Sustainable Transportation”
  • three-year ARC Fellowship “Project Delivery Harmonisation for Urban Micromobility Infrastructure”.

Capabilities and facilities

The Mobility team is supported through various state-of-the-art facilities. The City Analytics Lab (CAL), at the UNSW Kensington Campus is equipped with video walls and interactive touch screens to support both the co-design of new digital tools and end-user workshops to explore location-based insights into the form and function of the city. We collaborate with our colleagues at UNSW’s Research Centre for Integrated Transport Innovation (rCITI) using VR technology to test street design options and emerging technologies.

 

 

Projects

Current

Professional development

    • Chia J; Lee J; Han H, 2020, 'How does the location of transfer affect travellers and their choice of travel mode?—A smart spatial analysis approach', Sensors (Switzerland), 20, pp. 1 - 17, http://dx.doi.org/10.3390/s20164418

    • Chia J; Lee B, 2019, 'Extending Public Transit Accessibility Models to Recognise Transfer Location', Journal of Transport Geography
    • Farmer D; Kim H; Lee B, 2023, 'The Relationship Between Exposure to and Trust in Automated Transport Technologies and Intention to Use a Shared Autonomous Vehicle', International Journal of Human-Computer Interactionhttp://dx.doi.org/10.1080/10447318.2023.2247553
    • Han H; Chen H; Lee J, 2021, 'Spatiotemporal changes in vertical heterogeneity: High-rise office building floor space in Sydney, Australia', Buildings, 11, pp. 374 - 374, http://dx.doi.org/10.3390/buildings11080374
    • Harris, M. S. (2023). Bicycle Infrastructure and Street Politics: Sydney’s Flagship Cycleway to COVID-19 Pop-Ups. In K. Bishop & L. Corkery (Eds.), Routledge Handbook of Urban Landscape Researchhttps://www.routledgehandbooks.com/pdf/doi/10.4324/9781003109563-6
    • Harris, M. S., & McCue, P. (2022). Pop-up cycleways: How a COVID-19 ‘policy window’ changed the relationship between urban planning, transport and health in Sydney, Australia. Journal of the American Planning Associationhttps://doi.org/10.1080/01944363.2022.2061578
    • Mazumdar S; Jaques K; Conaty S; De Leeuw E; Gudes O; Lee J; Prior J; Jalaludin B; Harris P, 2023, 'Hotspots of change in use of public transport to work: A geospatial mixed method study', Journal of Transport and Health, 31, pp. 101650 - 101650, http://dx.doi.org/10.1016/j.jth.2023.101650
    • Mousavi A; Bunker J; Lee J, 2021, 'Exploring socio-demographic and urban form indices in demand forecasting models to reflect spatial variations: Case study of childcare centres in hobart, Australia', Buildings, 11, pp. 493 - 493, http://dx.doi.org/10.3390/buildings11100493
    • Nepal MP; Hon C; Lee J; Xiang Z, 2021, 'Towards an integrated approach to infrastructure damage assessment in the aftermath of natural hazards', Buildings, 11, pp. 450 - 450, http://dx.doi.org/10.3390/buildings11100450
    • Salih SH; Lee J, 2022, 'Measuring transit accessibility: A dispersion factor to recognise the spatial distribution of accessible opportunities', Journal of Transport Geography, 98, http://dx.doi.org/10.1016/j.jtrangeo.2021.103238
    • Xie R; Zlatanova S; Lee J, 2022, '3D indoor environments in pedestrian evacuation simulations', Automation in Construction, 144, http://dx.doi.org/10.1016/j.autcon.2022.104593
    • Xie R; Zlatanova S; Lee J, 2022, '3D indoor environments in pedestrian evacuation simulations', Automation in Construction, 144, http://dx.doi.org/10.1016/j.autcon.2022.104593
    • Yan J; Zlatanova S; Lee JB; Liu Q, 2021, 'Indoor Traveling Salesman Problem (ITSP) Path Planning', ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION, 10, http://dx.doi.org/10.3390/ijgi10090616
    • Yan J; Lee J; Zlatanova S; Diakité AA; Kim H, 2022, 'Navigation network derivation for QR code-based indoor pedestrian path planning', Transactions in GIS, 26, pp. 1240 - 1255, http://dx.doi.org/10.1111/tgis.12912
    • Yan J; Zlatanova S; Lee J, 2023, 'An indoor service area determination approach for pedestrian navigation path planning', Cartography and Geographic Information Science, 50, pp. 321 - 332, http://dx.doi.org/10.1080/15230406.2022.2142849
    • Yoo S; Kim H; Kim W; Kim N; Lee J, 2022, 'Controlling passenger flow to mitigate the effects of platform overcrowding on train dwell time', Journal of Intelligent Transportation Systems: Technology, Planning, and Operations, 26, pp. 366 - 381, http://dx.doi.org/10.1080/15472450.2020.1853539
    • Yoo S; (Brian) Lee J, 2023, 'Revising bus routes to improve access for the transport disadvantaged: A reinforcement learning approach', Journal of Public Transportation, 25, http://dx.doi.org/10.1016/j.jpubtr.2023.100041
    • Yoo S; Lee JB; Han H, 2023, 'A Reinforcement Learning approach for bus network design and frequency setting optimisation', Public Transport, 15, pp. 503 - 534, http://dx.doi.org/10.1007/s12469-022-00319-y
    • Wu H; Lee J; Levinson D, 2023, 'The node-place model, accessibility, and station level transit ridership', Journal of Transport Geography, 113, http://dx.doi.org/10.1016/j.jtrangeo.2023.103739
    • Zhang M; Lee B, 2023, 'Make TOD More Bicycling-Friendly: An Extended Node-Place Model Incorporating a Cycling Accessibility Index', Buildingshttp://dx.doi.org/10.3390/buildings13051240