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O u t l i n e Changing Landscape Opportunities Business Models - PDF document

Nov 18, 2022 •22 likes •192 views

DIGITAL INNOVATIONS AND DISRUPTIVE MOBILITY HYPE OR REALITY? Image Credit : Adobe S t ock Professor Hussein Dia Chair, Department of Civil Engineering Deputy Director and Program Leader (Future Urban Mobility) S mart Cities Research

  1. DIGITAL INNOVATIONS AND DISRUPTIVE MOBILITY HYPE OR REALITY? Image Credit : Adobe S t ock Professor Hussein Dia Chair, Department of Civil Engineering Deputy Director and Program Leader (Future Urban Mobility) – S mart Cities Research Institute 1 O u t l i n e Changing Landscape Opportunities Business Models Impacts 2 Image Credit: Adobe Stock

  2. The three dimensions of tech-enabled urban mobility Infrastructure Creating smarter mobility with S upply and capacity Network management and control meaningful tech for Asset management better user experience S mart infrastructure Human factors Asset optimisation S afety Transport modelling Predictive modelling Traffic forecasting Technology Enhanced user experience Users S ensor networks Understanding of S mart devices travel demand and Traveller information Communication platforms traveller behaviour Behavioural modelling Control systems Data analytics Benefit-cost ratios for different transport investments Source: Low Carbon Mobilit y for Fut ure Cit ies: Principles and Applicat ions (Dia, H. ed., 2017)

  3. The changing landscape of urban mobility Conventional approaches Emerging approaches S upply and capacity Demand management and resilience Focus on mobility Accessibility S treet as road for vehicles S hared between all modes Physical dimensions S ocial dimensions Vehicle-oriented People-oriented and customer-focused Motorised transport Hierarchy of modes Travel as a derived demand Travel also a valued activity Minimisation of travel times Reliability of travel times Petrol taxes/ vehicle registration fees User-pay models Private car ownership Car-sharing and ride-sharing Source: Low Carbon Mobilit y for Fut ure Cit ies: Principles and Applicat ions (Dia, H. ed., 2017) 5 Car passenger-kilometres per capita 13500 13000 12500 12000 Canberra 11500 Pert h Melbourne 11000 Brisbane 10500 Adelaide 10000 S ydney 9500 9000 Sources: BITRE 2015 Yearbook; Peak Car Use in Australian Cities (Newman and Kenworthy, 2015); chartingtransport.com 6

  4. Is it a structural change? 59% 60% POSSIBLE CAUSES? Melbourne: Growth in private and public transport passenger kilometres since 2003 • Growth of public transport 50% • Growth of a culture of urbanism • Rise in fuel prices 40% • Reversal of urban sprawl • Ageing populations of cities 30% • Hitting the Marchetti wall 22% 20% 7% 10% 0% Private transport Public transport Population Sources: BITRE 2015 Yearbook; chart ingt ransport .com 7 Infrastructure challenges 75% of the infrastructure that will be in place by 2050 doesn't exist today. Most of that infrastructure will be transformative

  5. One of the most ambitious China’s trillion dollar Belt and Road infrastructure agenda geopolitical projects Aims to spend $1.3 trillion in loans by 2027 Around ten times what the US spent on the Marshall Plan in the aftermath of World War II e of Beijing's agenda. e of Beijing's agenda. The fourth dimension: No ordinary disruption Future of mobility: • Shared • On-demand Self-Driving Technologies • Electric • Autonomous (eventually!) Sharing Economy Vehicle Electrification (including tiny vehicles) Disruptive Mobility Underpinned by AI-based computational platforms Mobile and Cloud Blockchain where the mode of Computing transport will be a smart, self-moving device embedded in a digitalised eco-system. Internet of Things Images Credit : Adobe S t ock

  6. The merging worlds of technology, vehicles and shared mobility Uber – Huge Growth, Big Losses “ Explore strange new worlds— business models to come” 2014 2015 2016 2017 Jef f Bezos, CEO Amazon Market Capitalisation $40 billion $63 billion $69 billion $ 72 billion Gross Bookings $2.93 billion $10.8 billion $20.0 billion $37.0 billion Net Revenue $495 million $1.5 billion $6.5 billion $7.5 billion Auto Loss $671 million $987 million $2.8 billion $4.5 billion Manufactures April 2019 IPO: Estimated $120 billion $ $ In 2016, Australian Kilometre households spent Shared $65.8 billion a year on Tech $ Mobility as a utility Providers private vehicle travel Providers and $2.7 billion a year on public transport 11 Image Credit : Digit alTrends Uber claims more than 700,000 driving miles have been saved by UberPool in London (November 2015 – May 2016) UberPool is currently available in inner Melbourne suburbs. Trip must begin and end in this area.

  7. Arcade City: Ridesharing using tokens and blockchain Text line Image Credit : Arcade Cit y Electric Vehicles China’s EV Charging Network • Global impact on j obs • Impact on government coffers • The disruption of oil

  8. EMERGING BUSINESS MODELS 15 Global investment in future mobility start-ups Total disclosed investment in mobility start-ups 154 since 2010 companies North America Around $200 billion $79 billion China $50 billion 465 companies 159 United companies Kingdom Others Israel $18.5 billion $34 billion Singapore $6.0 billion $36 billion Japan $2.8 billion 51 India $2.5 billion companies Canada $2.2 billion Hong Kong $2.2 billion France $1.8 billion Source: McKinsey & Company

  9. E-scooters Globally, investors poured more than $5.7 billion into start-ups since 2015 Growth exceeded first- year adoption rates of similar services such as bike-sharing & ride-hailing VC expectation: S hared e-scooters will do to short distance travel what ride-hailing did to the taxi industry Commuting distance in capital cities Australian Bureau of Statistics 2016 Census Nil Distance Over 0 to less than 1 1 to less than 2.5 Commuting distance (km) 2.5 to less than 5 5 to less than 10 10 to less than 20 20 to less than 30 30 to less than 50 50 to less than 100 100 to less than 250 250 and over 0 5 10 15 20 25 30 Proportion of persons (%)

  10. Transport mode share in capital cities Australian Bureau of Statistics 2016 Census Active transport Public transport Private vehicle Private vehicles mode share: Greater Sydney Sydney 67% Greater Capital City Statistical Areas (GCCSA) Melbourne 76% Greater Melbourne Brisbane & Darwin 80% Canberra & Perth 83% Greater Brisbane Adelaide & Hobart 84% More than 85% of drivers who Greater Adelaide commute by private car don't share with other commuters. Greater Perth Greater Hobart Greater Darwin Australian Capital Territory 0 10 20 30 40 50 60 70 80 90 Mode share (%) Image Credit : Digit alt rends SELF-DRIVING VEHICLES 20

  11. 1.2 MILLION $500 BILLION THE MORAL IMPERATIVE 21 Impacts of autonomous shared mobility-on- demand systems 22 Image Credit : Adobe S t ock

  12. Impact on urban mobility? • Will they reduce or increase congestion? • Will they induce more demand for travel? • How will they impact VKT (per capita)? • Will they increase or decrease urban sprawl? • How will they impact urban form? • What impact will they have on parking? • Will they reduce or increase emissions? • How will they impact car ownership? 23 Image Credit : Adobe S t ock Melbourne Simulation Study Operational Includes parts of four different LGAs Area: 88.75 km 2 53 origins and destinations Simulation period: 07:00-09:00am

  13. Example simulation scenarios Trade-off between willingness to ride-share, fleet size & waiting times Scenario Ride-sharing Fleet size* Mean waiting Maximum time waiting time (Percent) (Percent) (minutes) (minutes) S cenario 1 90% 13% 2 10 S cenario 2 80% 19% 3 10 S cenario 3 40% 31% 4 12 * Required fleet size compared to base case scenario 25 Research findings: Autonomous Mobility on Demand Number of shared vehicles required to provide the same trips during peak hours 20% 80% increase in VKT – car sharing 30% increase in VKT - ridesharing 83% reduction in parking space 20% reduction in emissions when 80% of vehicles are shared 5 minutes waiting time Melbourne Case S tudy – First and Last Kilometre S olutions 26

  14. Questions remain Text line What is the expected future demand? Dynamic estimation of travel demand • Machine learning to predict travel demand for shared vehicles • S hort forecasting horizons • Training deep neural networks using historical data How to improve vehicle rebalancing algorithms? Extend linear programming methods to address the fleet balancing problem • Include constraints on VKT • Bounded waiting times Image Credit : Adobe S t ock REGULATORY CHALLENGES Who (or what) is behind the wheel? 28

  15. BAIDU 29 Visual “ Turing Test” for verifying AI software Questions remain • How to license a “ deep neural network” software? • S hould it pass a benchmark test before it can be recognised as a legal driver? • Who should develop such a test and what should it include? • What procedures can be used to verify compliance? 30

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