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MaaSLab Guest Lecture_The Sydney Trial – synthesis and Lessons Learnt

May

25

MaaSLab Guest Lecture_The Sydney Trial – synthesis and Lessons Learnt

by MaaSLab

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Date and time

Tue, 25 May 2021

10:00 – 11:00 BST

Location

Online event

MaaS – The Sydney Trial – Synthesis and Lessons Learnt

About this event

Presenter: David, a Fellow of the Australian Academy of Social Sciences (FASSA), is the recipient of many awards including the 2009 International Association of Travel Behaviour Research (IATBR) Lifetime Achievement Award, 2006 Engineers Australia Transport Medal, the 2016 Vice-Chancellor’s Award for Outstanding Research and the 2019 John Shaw Medal which honours an industry champion who has made a lasting contribution to Australia's roads. In 2018 David was selected as one of 25 academics at the University of Sydney who have made a significant impact through engaging with industry and government. He has published over 675 papers in leading international transport and economics journals as well as 16 books with over 60,000 Google citations.

Abstract of Prensentation: During 2019 to mid-2020, we undertook a MaaS trial in Sydney to get a better appreciation of all the elements that need careful attention in bringing MaaS into an operational setting. The Sydney MaaS trial as it is now known, is one of the first trials worldwide with transparent reported quantitative and qualitative evidence on MaaS bundle uptake and induced changes in travel behaviour. In this talk David provides an overview of the design of the trial, setting out the trial set-up with a tripartite structure (broker, app developer, university) as a blueprint for trials as well as a synthesis of the main contributions and lessons learnt for future MaaS trials. Partners in the trial are IAG (Insurance Australia Group) and SkedGo under the umbrella of a research project funded to the University of Sydney Institute of Transport and Logistics Studies (ITLS) through the iMOVE CRC.

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MaaSLab Guest Lecture_The Sydney Trial – synthesis and Lessons Learnt


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