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Associate Professor Civil Engineering at Johns Hopkins University Co-Director, Center for Systems Science and Engineering Member, Research Centre for Integrated Transport Innovation (rCITI) at UNSW Sydney Visiting Scientist, CSIRO (Australia)
Network Modeling of Transport Systems
Center for Systems Science and Engineering
SLIDE 2 Pricing for Transport Networks
- Model Road Pricing Schemes
- Consider Uncertainty/Information
- Compare Policy Options
- Quantify System Performance
Planning for Alternative Vehicle Technologies
- Integration of Power and Transport Systems
- User Behaviour
- Sustainability Metrics
- Policy Development
- Multi-Objective Network Design
Mobility and Epidemiology
- Role of Transportation in Disease Spread
- Quantification of Disease Spreading Risk
- Predicting Outbreak Behaviour Patterns
- Optimizing Intervention Strategies
Research Areas of Focus
SLIDE 3 Overview of Research Methods
Objective: Exploit available information to infer and predict local and global patterns of contagion, quantify the risk posed (e.g., by components of transport systems) in the spread
- f disease, and design optimal mitigation strategies
Methods: i. Mathematical modeling ii. Network theory
- iii. Optimization
- iv. Simulation
v. Statistics Contributions: i. Policy evaluation and decision support ii. Optimize resource allocation Hypothesis: The movement of people, pathogens and vectors (e.g., mosquitos) plays an integral role in the risk of disease.
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Case Geo-location Data Environmental, Land-use and Climate
Large-scale Data Requirements
Local Mobility Global Transport Networks Social Media, Cell phone, Credit Card, Google, etc
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Social-Contact Network Models
Can we use available spatiotemporal infection data (and other information) to better understand the risk posed by an outbreak?
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Modeling Public Transit Contact Patterns
Local Mobility Patterns
“Flu on the Bus” Problem
Define Contact Networks High Risk Transit Trips Bota, et al. (2017), Netw Spat Econ.
Apply network- based statistics, algorithms and simulation Extract ridership data
SLIDE 7 Vehicle trip network
- Nodes ← vehicle trips loads
- Links ← transfer passenger volumes
Public Transit Network Analysis
Contact network. Large circles represent vehicle-trips
- Fig. Vehicle-trip network
Vehicle Trip Network:
- 8002 nodes (vs 94,475)
- 263,792 links (vs 6,287,847)
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Planning for New Vehicles Technologies
Research Questions:
1. How will infrastructure and planning decisions change due to the presence of new vehicle technologies? e.g., Electricity Pricing, EV charging Station location, Transport System Design Objectives (safety, emissions, etc) 2. How does the behaviour of drivers change in the presence of these new technologies?
Research Applications:
Integration - Convergence of transport/power systems Planning - Demand-Supply for electric power grid User Behaviour – Range Anxiety, Routing Sustainability - Upstream emissions Policy Decisions - Charging Infrastructure Location Network Design Problem – Multiple objectives
SLIDE 9 Modeling Sustainable Transport Systems:
ELECTRIC VEHICLES
- improve battery storage
- power train configuration
Transportation:
- encourage clean sources
- reduce fossil fuel dependence
- standards
- incentives
- affordability
- business models
- financing
- consumers
- travellers
- range anxiety
- education
- network modelling
- distance limitations
- destination choices, route choice
- infrastructure improvement
Electric Power Systems: Economic: Policy: Energy: Technology: Behavioural:
- Smart Grid
- effective management
- charging infrastructure
- mobile storage devices
SLIDE 10 Modeling the System Impact of Travel Demand Variability on Emissions and Congestion
★ The expected performance of a system may not be correlated to the variability of the system
0% 1% 2% 3% 4% 5%
Change in network performance due to project Design Scenarios
Expected ΔTSTT Expected ΔTSEC_PEV
0% 2% 4% 6% 8% 10%
Change in network performance due to project Design Scenarios
ΔTSTT STD ΔTSEC_PEV STD
These are the the same design scenario and so on
SLIDE 11 Modelling Spatiotemporal EV Uptake and Energy Consumption Rates
Relative likelihoods that households in each CCD would purchase an electric vehicle. AECOM Vehicle Sales Forecast for Sydney GMA Average daily distance driven by a vehicle owner residing in each CCD.
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SLIDE 12
Thank You
Email: l.gardner@jhu.edu Office: Latrobe 104
Center for Systems Science and Engineering
