Intelligent Mobility for Smart Cities
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Transcript of Intelligent Mobility for Smart Cities
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CRICOS Provider 00111D
A/Prof Hussein Dia Centre for Sustainable Infrastructure
@HusseinDia
Intelligent Mobility for Smart Cities
Presentation to the IRF & Roads Australia Regional Conference (Asia and Australasia) Sydney, Australia May 4-6, 2015
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Explore the complexity of urban mobility and how the convergence of disruptive technologies will deliver innovative solutions that support smart, connected and liveable cities.
Outline
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The Urban Millennium Ambition
“The age of nations is over. The new urban age has begun.” Parag Khanna
Beyond City Limits www.paragkhanna.com
New York & London represent 40% of the global market capitalisation
100 cities account for 30% of the global economy and innovation
21st century appears likely to be dominated by global cities, which will become the magnets of economy and engines of globalisation
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The Urban Challenge
2 . 50 . 75 . 80
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The Mobility Challenge 1 Billion Vehicles in Operation
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The Cost Problem
Traffic Congestion
Road Safety
Costs around 1% - 3% of a country’s GDP
Global annual fatalities: 1.2 million Economic Cost: $100 billion per year
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The Cost Problem
2013: 440ppm Emissions
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New approaches are needed to fund, manage, operate and optimise utilisation of transport infrastructure
The Funding Challenge
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The Opportunities
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The Opportunities
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Internet-Connected Devices
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Principles
• Increase the amount of data collected from assets
• Share, integrate and filter real-time data from networked infrastructure
• Optimise operations using predictive analytics, data mining and modelling
• Enhanced information flow to citizens and service providers
Transformation to Smart Cities
Instrument to Manage
Integrate to Innovate
Optimise to Transform
Path to Transformation
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Conventional Approaches Trends and Targets Building additional infrastructure capacity (focus on supply)
Maximising efficiency, resilience, and sweating of assets (focus on managing demand)
Vehicle-oriented People-oriented Customer-centric
Focus on reacting to congestion Focus on positive business and operational outcomes
Emphasis on “knowing and seeing”
Emphasis on “predicting and anticipating in order to avoid”
Spending on physical infrastructure
Spending on data fusion, predictive analytics, Artificial Intelligence and adaptive tools
Smart Mobility ‘Knowledge Gaps’
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Disruptive Technologies
Technology Trends Mobile Internet Increasingly inexpensive and capable
mobile computing devices and Internet Connectivity
The Internet of Things Networks of low-cost sensors for data collection, monitoring, decision making, and process optimisation
Cloud Technology Use of computer hardware and software resources delivered over a network or the Internet, often as a service
Energy Storage Devices or systems that store energy for later use, including batteries
Autonomous & Near Autonomous Vehicles
Vehicles that can navigate and operate with reduced or no human intervention
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Real-time mobility monitoring using smartphones Large-scale sensing data from sensor-rich smart mobile devices deliver information that can be used to provide users with more travel options depending on time of travel, weather, price and destination Data fusion methods to sanitise and filter the data, and derive mobility patterns, origins-destinations, travel times, and other mobility information Reduces reliance on data from fixed sensors
Participatory Sensing
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New Business Models
What if …? Automakers subsidise car purchases by working with technology companies to capitalise on the lifetime revenue opportunity of connected drivers?
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What if …? Consumers replace traditional car ownership models with on-demand access to the vehicles they want?
New Business Models
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Leap Buses $6.00 vs $2.20
Market Forces
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The “Autonomes” are here: How will they impact urban mobility?
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Nearly the same mobility can be delivered with 35% of the cars – Peak hours scenario
Modelling using MATSim Vehicle capacity: up to 8 passengers Maximum 5 minutes wait time
Source: International Transport Forum, Urban Mobility System Upgrade
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Nearly the same mobility can be delivered with 10% of the cars – 24 hours scenario
Source: International Transport Forum, Urban Mobility System Upgrade
Complex mathematical model typically solved using LP “Dynamic pickup and delivery problem with defined time windows
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The overall volume of car travel will likely increase
Source: International Transport Forum, Urban Mobility System Upgrade
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Impacts on emissions, air quality and utilisation
Austin, Texas MATSim Study Emissions • Average 26 trips per day (versus 3) • Average in use 8 hours per day (versus 1) • Fewer cold starts and dynamic ridesharing could
offset part of higher VKT
Utilisation • 300 km per day (110,000 km per year) • Need replacement every 3-5 years
Source: Fagnant, D. et al (2015). Operations of a Shared Autonomous Vehicle Fleet for the Austin, Texas Market
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Disruptive Technologies
The technology is rapidly advancing or experiencing breakthroughs
The potential scope of impact is broad
Significant economic value could be affected
Economic impact is potentially disruptive Doing nothing is not an option!
The smart mobility vision
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Connected and Cooperative Mobility
Traffic Demand Profiling
Traffic Forecasting and Predictive
Modelling
Network Performance Analysis
New Generation Traffic Management and Control Systems
Impact Assessment Tools
Smart Mobility Research Facility
Smart Mobility
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Modelling and Evaluation of Smart Mobility Options
Travel Behaviour
Behavioural modelling and prediction
Simulation and Modelling
Multi-modal transport modelling Emissions modelling
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Concluding Remarks
Technology will play a major role in delivering sustainable mobility solutions but must be part of a holistic vision