Intelligent Mobility for Smart Cities
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1 Intelligent Mobility for Smart Cities A/Prof Hussein Dia Centre for Sustainable Infrastructure CRICOS Provider
2 Outline Explore the complexity of urban mobility and how the convergence of disruptive technologies will deliver innovative solutions that support smart, connected and liveable cities.
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 21 st century appears likely to be dominated by global cities, which will become the magnets of economy and engines of globalisation
4
5 The Urban Challenge
6 The Mobility Challenge 1 Billion Vehicles in Operation
7 The Cost Problem Traffic Congestion Costs around 1% - 3% of a country s GDP Road Safety Global annual fatalities: 1.2 million Economic Cost: $100 billion per year
8 The Cost Problem Emissions 2013: 440ppm
9 The Funding Challenge New approaches are needed to fund, manage, operate and optimise utilisation of transport infrastructure
10 The Opportunities
11 The Opportunities
12 Internet-Connected Devices
13
14 Transformation to Smart Cities 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 Path to Transformation Instrument to Manage Integrate to Innovate Optimise to Transform
15 Smart Mobility Knowledge Gaps Conventional Approaches Building additional infrastructure capacity (focus on supply) Vehicle-oriented Focus on reacting to congestion Emphasis on knowing and seeing Spending on physical infrastructure Trends and Targets Maximising efficiency, resilience, and sweating of assets (focus on managing demand) People-oriented Customer-centric Focus on positive business and operational outcomes Emphasis on predicting and anticipating in order to avoid Spending on data fusion, predictive analytics, Artificial Intelligence and adaptive tools
16 Disruptive Technologies Technology Mobile Internet The Internet of Things Cloud Technology Energy Storage Autonomous & Near Autonomous Vehicles Trends Increasingly inexpensive and capable mobile computing devices and Internet Connectivity Networks of low-cost sensors for data collection, monitoring, decision making, and process optimisation Use of computer hardware and software resources delivered over a network or the Internet, often as a service Devices or systems that store energy for later use, including batteries Vehicles that can navigate and operate with reduced or no human intervention
17 Participatory Sensing 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
18 New Business Models What if? Automakers subsidise car purchases by working with technology companies to capitalise on the lifetime revenue opportunity of connected drivers?
19 New Business Models What if? Consumers replace traditional car ownership models with on-demand access to the vehicles they want?
20 Market Forces Leap Buses $6.00 vs $2.20
21 The Autonomes are here: How will they impact urban mobility?
22 Nearly the same mobility can be delivered with 35% of the cars Peak Hours Modelling using MATSim Vehicle capacity: up to 8 passengers Maximum 5 minutes wait time Source: International Transport Forum, Urban Mobility System Upgrade
23 Nearly the same mobility can be delivered with 10% of the cars 24 hours scenario Complex mathematical model typically solved using LP Dynamic pickup and delivery problem with defined time windows Source: International Transport Forum, Urban Mobility System Upgrade
24 The overall volume of car travel will likely increase Source: International Transport Forum, Urban Mobility System Upgrade
25 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
26 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!
27 Smart Mobility Research Facility New Generation Traffic Management and Control Systems Connected and Cooperative Mobility Traffic Forecasting and Predictive Modelling Impact Assessment Tools Smart Mobility Network Performance Analysis Traffic Demand Profiling
28 Modelling and Evaluation of Smart Mobility Options Simulation and Modelling Multi-modal transport modelling Emissions modelling Travel Behaviour Behavioural modelling and prediction
29 Concluding Remarks Technology will play a major role in delivering sustainable mobility solutions but must be part of a holistic vision
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