Hao Wu is a Research Associate in the City Futures Research Centre, the University of New South Wales. Prior to joining the UNSW team, he completed his PhD degree in 2021 with the University of Sydney on the subject of Ensemble Forecasting. He studied transportation engineering and graduated with a M.S. degree in Civil Engineering from the Georgia Institute of Technology in 2017. Hao's research focuses on accessibility, and the related transport, land-use and mode choice issues; his recent research interest is in the application of machine learning and ensemble forecasting in transport and planning problems. He authored Access Across Australia and Access Across New Zealand reports, and is a coauthor of the Transport Access Manual.
2022, 'Access-oriented design? Disentangling the effect of land use and transport network on accessibility', Transportation Research Interdisciplinary Perspectives, 13, http://dx.doi.org/10.1016/j.trip.2021.100536
,2022, 'All ridership is local: Accessibility, competition, and stop-level determinants of daily bus boardings in Portland, Oregon', Journal of Transport Geography, 99, http://dx.doi.org/10.1016/j.jtrangeo.2022.103294
,2022, 'Maximizing access in transit network design', Journal of Public Transportation, 24, http://dx.doi.org/10.1016/j.jpubtr.2022.100027
,2021, 'The ensemble approach to forecasting: A review and synthesis', Transportation Research Part C: Emerging Technologies, 132, http://dx.doi.org/10.1016/j.trc.2021.103357
,2021, 'Immigrant settlement patterns, transit accessibility, and transit use', Journal of Transport Geography, 96, http://dx.doi.org/10.1016/j.jtrangeo.2021.103187
,2021, 'Urban access across the globe: an international comparison of different transport modes (vol 1, 16, 2021)', NPJ URBAN SUSTAINABILITY, 1, http://dx.doi.org/10.1038/s42949-021-00035-9
,2021, 'Urban access across the globe: an international comparison of different transport modes', NPJ URBAN SUSTAINABILITY, 1, http://dx.doi.org/10.1038/s42949-021-00020-2
,2021, 'Commute mode share and access to jobs across US metropolitan areas', Environment and Planning B: Urban Analytics and City Science, 48, pp. 671 - 684, http://dx.doi.org/10.1177/2399808319887394
,2021, 'Optimum stop spacing for accessibility', European Journal of Transport and Infrastructure Research, 21, pp. 1 - 18, http://dx.doi.org/10.18757/ejtir.2021.21.2.4794
,2020, 'Measuring polycentricity via network flows, spatial interaction and percolation', Urban Studies, 57, pp. 2402 - 2422, http://dx.doi.org/10.1177/0042098019832517
,2020, 'Effects of Timetable Change on Job Accessibility', Findings, http://dx.doi.org/10.32866/001c.13184
,2020, 'Unifying access', Transportation Research Part D: Transport and Environment, 83, http://dx.doi.org/10.1016/j.trd.2020.102355
,2020, 'Towards a general theory of access', Journal of Transport and Land Use, 13, pp. 129 - 158, http://dx.doi.org/10.5198/jtlu.2020.1660
,2019, 'How transit scaling shapes cities', Nature Sustainability, 2, pp. 1142 - 1148, http://dx.doi.org/10.1038/s41893-019-0427-7
,2018, 'Comparing Google Maps and Uber Movement Travel Time Data', Transport Findings, http://dx.doi.org/10.32866/5115
,2021, New housing supply, population growth, and access to social infrastructure, http://dx.doi.org/10.18408/AHURI73233
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