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Intelligent Mobility for Smart Cities


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Presentation to the 2015 International Road Federation and Roads Australia Regional Conference, Sydney, 4-6 May 2015

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Intelligent Mobility for Smart Cities

  1. 1. 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
  2. 2. 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
  3. 3. The Urban Millennium Ambition “The age of nations is over. The new urban age has begun.” Parag Khanna Beyond City Limits 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
  4. 4. The Urban Challenge 2 . 50 . 75 . 80
  5. 5. The Mobility Challenge 1 Billion Vehicles in Operation
  6. 6. 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
  7. 7. The Cost Problem 2013: 440ppmEmissions
  8. 8. New approaches are needed to fund, manage, operate and optimise utilisation of transport infrastructure The Funding Challenge
  9. 9. The Opportunities
  10. 10. The Opportunities
  11. 11. Internet-Connected Devices
  12. 12. 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
  13. 13. 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’
  14. 14. 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
  15. 15. 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
  16. 16. New Business Models What if …? Automakers subsidise car purchases by working with technology companies to capitalise on the lifetime revenue opportunity of connected drivers?
  17. 17. What if …? Consumers replace traditional car ownership models with on-demand access to the vehicles they want? New Business Models
  18. 18. Leap Buses $6.00 vs $2.20 Market Forces
  19. 19. The “Autonomes” are here: How will they impact urban mobility?
  20. 20. 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
  21. 21. 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
  22. 22. The overall volume of car travel will likely increase Source: International Transport Forum, Urban Mobility System Upgrade
  23. 23. 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
  24. 24. 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
  25. 25. 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
  26. 26. Modelling and Evaluation of Smart Mobility Options Travel Behaviour Behavioural modelling and prediction Simulation and Modelling Multi-modal transport modelling Emissions modelling
  27. 27. Concluding Remarks Technology will play a major role in delivering sustainable mobility solutions but must be part of a holistic vision