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  • ITLS Working Papers 2021
  • Recent submissions
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ITLS Working Papers 2021: Recent submissions

    • Working from Home in Australia in 2020: Positives, Negatives and the Potential for Future Benefits to Transport and Society 

      Beck, Matthew J.; Hensher, David A.
      Published 2021
      The year 2020 has been marked by the most extraordinary event we have witnessed since World War II. While other health threats and geographical disasters have occurred, none have been on the global scale of COVID-19. ...
      Open Access
      Working Paper
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    • Place-based disadvantage, social exclusion and the value of mobility 

      Stanley, John K.; Hensher, David A.; Stanley, Janet R.
      Published 2021
      Connections between mobility and social exclusion have been of research and policy interest for some years, a high value being estimated for additional trips by those at risk of mobility-related exclusion. Those values ...
      Open Access
      Working Paper
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    • Comparative Assessment of Zero Emission Electric and Hydrogen Buses in Australia 

      Hensher, David A.; Wei, Edward; Balbontin, Camila
      Published 2021
      The pace of electrification for public transit bus fleets is accelerating internationally. Clear targets have been established by transport policymakers to achieve a zero-emission bus target as early as 2030 in some ...
      Open Access
      Working Paper
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    • What does the Quantum of Working from Home do to the Value of Commuting Time used in Transport Appraisal? 

      Hensher, David A.; Beck, Matthew J.; Balbontin, Camila
      Published 2021
      The need to recognise and account for the influence of working from home on commuting activity has never been so real as a result of the COVID-19 pandemic. Given a recognition that WFH activity during the pandemic has ...
      Open Access
      Working Paper
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    • Forecasting Automobile Gasoline Demand in Australia Using Machine Learning-based Regression 

      Li, Zheng; Zhou, Bo; Hensher, David A.
      Published 2021
      We use a variant of machine learning (ML) to forecast Australia’s automobile gasoline demand within an autoregressive and structural model. By comparing the outputs of the various model specifications, we find that training ...
      Open Access
      Working Paper
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