O u t l i n e Changing Landscape Opportunities Business Models - - PDF document
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
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HYPE OR REALITY? Professor Hussein Dia
Chair, Department of Civil Engineering Deputy Director and Program Leader (Future Urban Mobility) – S mart Cities Research Institute
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Image Credit: Adobe Stock
Creating smarter mobility with meaningful tech for better user experience Infrastructure
S upply and capacity Network management and control Asset management S mart infrastructure Asset optimisation Transport modelling Traffic forecasting Understanding of travel demand and traveller behaviour
Technology Users
S ensor networks S mart devices Communication platforms Control systems Data analytics Human factors S afety Predictive modelling Traveller information Behavioural modelling Enhanced user experience
Source: Low Carbon Mobilit y for Fut ure Cit ies: Principles and Applicat ions (Dia, H. ed., 2017)
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
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
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Source: Low Carbon Mobilit y for Fut ure Cit ies: Principles and Applicat ions (Dia, H. ed., 2017)
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9000 9500 10000 10500 11000 11500 12000 12500 13000 13500 Canberra Pert h Melbourne Brisbane Adelaide S ydney
Sources: BITRE 2015 Yearbook; Peak Car Use in Australian Cities (Newman and Kenworthy, 2015); chartingtransport.com
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0% 10% 20% 30% 40% 50% 60%
Private transport Public transport Population
Melbourne: Growth in private and public transport passenger kilometres since 2003
59% 22% 7%
Sources: BITRE 2015 Yearbook; chart ingt ransport .com
POSSIBLE CAUSES?
e of Beijing's agenda. e of Beijing's agenda. One of the most ambitious 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
China’s trillion dollar Belt and Road infrastructure agenda
Disruptive Mobility Self-Driving Technologies Sharing Economy Internet of Things Blockchain Mobile and Cloud Computing Vehicle Electrification (including tiny vehicles)
Images Credit : Adobe S t ock
Future of mobility:
Underpinned by AI-based computational platforms where the mode of transport will be a smart, self-moving device embedded in a digitalised eco-system.
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April 2019 IPO: Estimated $120 billion
Image Credit : Digit alTrends
Uber – Huge Growth, Big Losses
“ Explore strange new worlds— business models to come” Jef f Bezos, CEO Amazon 2014 2015 2016 2017 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 Loss $671 million $987 million $2.8 billion $4.5 billion Auto Manufactures Tech Providers Shared Mobility Providers
$ $ $
In 2016, Australian households spent $65.8 billion a year on private vehicle travel and $2.7 billion a year
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.
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Image Credit : Arcade Cit y
China’s EV Charging Network
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North America
$79 billion
China
$50 billion
465 companies 154 companies
United Kingdom
$34 billion
Others
$36 billion
51 companies 159 companies
Total disclosed investment in mobility start-ups since 2010 Around $200 billion
Israel $18.5 billion Singapore $6.0 billion Japan $2.8 billion India $2.5 billion Canada $2.2 billion Hong Kong $2.2 billion France $1.8 billion
Source: McKinsey & Company
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
5 10 15 20 25 30 250 and over 100 to less than 250 50 to less than 100 30 to less than 50 20 to less than 30 10 to less than 20 5 to less than 10 2.5 to less than 5 1 to less than 2.5 Over 0 to less than 1 Nil Distance Proportion of persons (%) Commuting distance (km)
Commuting distance in capital cities
Australian Bureau of Statistics 2016 Census
10 20 30 40 50 60 70 80 90 Australian Capital Territory Greater Darwin Greater Hobart Greater Perth Greater Adelaide Greater Brisbane Greater Melbourne Greater Sydney Mode share (%) Greater Capital City Statistical Areas (GCCSA)
Transport mode share in capital cities
Australian Bureau of Statistics 2016 Census
Active transport Public transport Private vehicle Private vehicles mode share: Sydney 67% Melbourne 76% Brisbane & Darwin 80% Canberra & Perth 83% Adelaide & Hobart 84% More than 85% of drivers who commute by private car don't share with other commuters.
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Image Credit : Digit alt rends
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Operational
Includes parts of four different LGAs Area: 88.75 km2 53 origins and destinations Simulation period: 07:00-09:00am
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Scenario Ride-sharing (Percent) Fleet size* (Percent) Mean waiting time (minutes) Maximum waiting time (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
Trade-off between willingness to ride-share, fleet size & waiting times
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Number of shared vehicles required to provide the same trips during peak hours
80% increase in VKT – car sharing 30% increase in VKT - ridesharing 83% reduction in parking space 20% reduction in emissions when 80%
5 minutes waiting time
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Dynamic estimation of travel demand
hort forecasting horizons
Extend linear programming methods to address the fleet balancing problem
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BAIDU
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network” software?
hould it pass a benchmark test before it can be recognised as a legal driver?
and what should it include?
verify compliance?
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Research should look beyond the immediate benefits and establish long-term impacts of new technologies
Developing rigorous but flexible evaluation frameworks and tools Adapt governance systems and develop agile and outcome-focused regulations Facilitate and encourage active transport, tiny vehicles and public transport innovations
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the interplay between the different parameters and constraints of emerging modes of transport
realised, but sharing is going to be key to their success
understanding of the potential future demand and patterns of use
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