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

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

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

The Urban Challenge

2 . 50 . 75 . 80

The Mobility Challenge 1 Billion Vehicles in Operation

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

The Cost Problem

2013: 440ppm Emissions

New approaches are needed to fund, manage, operate and optimise utilisation of transport infrastructure

The Funding Challenge

The Opportunities

The Opportunities

Internet-Connected Devices

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

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’

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

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

New Business Models

What if …? Automakers subsidise car purchases by working with technology companies to capitalise on the lifetime revenue opportunity of connected drivers?

What if …? Consumers replace traditional car ownership models with on-demand access to the vehicles they want?

New Business Models

Leap Buses $6.00 vs $2.20

Market Forces

The “Autonomes” are here: How will they impact urban mobility?

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

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

The overall volume of car travel will likely increase

Source: International Transport Forum, Urban Mobility System Upgrade

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

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

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

Modelling and Evaluation of Smart Mobility Options

Travel Behaviour

Behavioural modelling and prediction

Simulation and Modelling

Multi-modal transport modelling Emissions modelling

Concluding Remarks

Technology will play a major role in delivering sustainable mobility solutions but must be part of a holistic vision