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Insights from Some Studies on Control in Traffic Networks
Srinivas Peeta Georgia Institute of Technology
peeta@gatech.edu
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Insights from Some Studies on Control in Traffic Networks Srinivas - - PowerPoint PPT Presentation
Insights from Some Studies on Control in Traffic Networks Srinivas - - PowerPoint PPT Presentation
Insights from Some Studies on Control in Traffic Networks Srinivas Peeta Georgia Institute of Technology peeta@gatech.edu 1 Transportation Ecosystem Infrastructure Human Vehicle 2 Transportation System Supply Demand Technology 3
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Supply Demand Technology
Transportation System
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❑Real-time dynamic traffic network control
▪ Time-dependent conditions ▪ Stochasticity: demand, supply ▪ Technology: Role of information ▪ Traveler behavior
❑How information/connectivity can be used to manage networks and their performance
▪ Congested traffic networks ▪ Disaster response – planning and operations ▪ Connected and/or autonomous transportation ▪ Collaborative freight networks ▪ Organizational networks
Control in Networks: Some Studies
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❑Realism
▪ Traffic flow characteristics (analytical, simulation) ▪ Travel behavior (departure time/route/mode, learning & evolution, familiarity, risk-taking) ▪ Driver behavior (familiarity, experience, aggressiveness)
❑Factors
▪ Heterogeneity (traveler/driver/human/vehicle) ▪ Stochasticity (demand/supply) ▪ Time scale (behavior/planning/operations) ▪ Interactions (vehicle-human-infrastructure)
❑Goals
▪ State (description, prediction, evolution) ▪ Performance and control
Modeling Challenges
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❑Mathematical properties
▪ Realism in modeling vs mathematical tractability ▪ Complexity of traffic flows ▪ Traveler behavior and learning ▪ Stochasticity, heterogeneity, dynamics
❑Computational time
▪ Real-time needs ▪ Tradeoffs with accuracy ▪ Sensitivity issues under emerging technologies
Modeling Challenges
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❑Connectivity
▪ Reliability ▪ Congestion ▪ Control ▪ Security
❑Automation
▪ Stability ▪ Mixed flows ▪ Platooning ▪ Traffic characteristics ▪ Safety and mobility
Emerging Technologies
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❑Human-vehicle interactions
▪ Transition of control
❑In-vehicle interactions
▪ In-vehicle devices ▪ Mobile apps
❑Motion planning
▪ Maneuvering ▪ Platooning ▪ Intersections
❑Connectivity-based control
▪ In-vehicle/personal devices
Needs
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❑Transition and mixed traffic flows
▪ Level of automation ▪ Level of connectivity ▪ Vehicle characteristics ▪ Asymmetry in human behavior ▪ Differences in human and machine approaches to driving ▪ Misperception of AV capabilities
❑Data
▪ What does it reveal? ▪ How to connect disparate data? ▪ How can it be used to enhance modeling realism? ▪ Human in the loop
Needs
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❑Platooning
▪ Cooperative braking control (CVs) ▪ Under V2X communications ▪ CV information transmission time delays
❑Vehicular traffic flow
▪ Sliding mode controller ▪ Non-lane discipline ▪ Leveraging vehicle characteristics under connecitvity
Insights: Some Problems Addressed
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❑Transportation community
▪ Control as a goal enabler ▪ Effectiveness, goals (mobility, safety, energy, emissions) ▪ Vehicular interactions (behavior, traffic flow theory)
▪ Car-following, merge/diverge,
▪ Network-level
▪ Traffic interactions, traveler/driver behavior, topology/infrastructure effects
❑Control community
▪ Focus on controller and its properties ▪ Convergence, stability, consensus ▪ Vehicle as individual agent (inter-vehicle gap, velocity)
▪ Negative spacing/velocity, uncomfortable acceleration/deceleration
▪ Micro- and corridor-level
▪ Lateral control, longitudinal control
Insights
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❑Value in collaboration
▪ Increased role of technology, especially automation, as a catalyst ▪ Data as enabler (Google, Nvidia, etc.) ▪ Focus on realism
Opportunities
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Infrastructure Human Vehicle
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Thank you!
Popular Mechanics, 1950s
Contact Information: Srinivas Peeta Email: peeta@gatech.edu