Traffic Flow Control in a Connected Environment Petros Ioannou - - PowerPoint PPT Presentation

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Traffic Flow Control in a Connected Environment Petros Ioannou - - PowerPoint PPT Presentation

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Traffic Flow Control in a Connected Environment Petros Ioannou Center for Advanced Transportation Technologies METRANS University Transportation Center University of Southern California Los Angeles, CA, USA 1 7/10/2019 NSF Workshop June 8-9,

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Petros Ioannou Center for Advanced Transportation Technologies METRANS University Transportation Center University of Southern California Los Angeles, CA, USA

7/10/2019 NSF Workshop June 8-9, 2019 1

Traffic Flow Control in a Connected Environment

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Brief History

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  • Automated Highway System Program started in the 80’s ended with 1997
  • Demo. Platooning plus other technologies
  • Replaced with IV initiative with vehicle safety as priority
  • 2004, 2005, 2007 DARPA Challenge Competition
  • Current: Autonomous Vehicles, 5 levels of Autonomy (Google Cars, Tesla, Uber

etc)

  • Efforts are to get rid of the driver when vehicles is the cause of congestion
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Vehicle Control Safety and Platooning

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Drag reduction: Fuel savings, lower pollution

𝑇

Ξ”V2 at collision Spacing 𝑇 P A M P: Platoon spacing ( 1 meter) A: Automated M: Manual; Capacity ∝

1 𝑇

π›¦π‘Š = π‘Š

1 βˆ’ π‘Š 2

π‘Š

1

π‘Š

2

Safety Objective: No vehicle should be put in a position it cannot handle

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WHAT IS THE MAIN TRANSPORTATION PROBLEM? This is what we usually see and experience

Image from www.msnbc.com Image from Wikipedia

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Transportation System for Moving Goods and People is far more complex

RAIL OCEAN ROAD PORT

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CURRENT TRANSPORTATION SYSTEM

  • Nonlinear Dynamical System of interconnected systems
  • Open Loop Most of the Time
  • Limited ineffective feedback
  • Lack of sensor data and connectivity

Consequences

  • Congestion
  • Inefficient utilization of infrastructure
  • Safety
  • Pollution
  • Long travel times, High cost
  • Unbalanced in time and space

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Connectivity will Revolutionize Transportation

  • Open loop operations will become more stable and robust via

active feedback

  • Information/data are crucial in optimizing processes and

movements of people and goods

  • Enhance coordination
  • Vehicle to Infrastructure Connectivity is Proven Technology
  • Private sector is moving faster to satisfy user needs

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Traffic Management Control (TMC) System

Data Acquisition & Processing Traffic System

Output:Traffic Data T2 T1 T0 Control Inputs

Traffic Controller

TMC System

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7/10/2019

Ramp Metering Beacons TMC

Optimum speed limits, Lane Change, VSL Routing, Pricing

Ramp metering commands

Closing the loop with the Highway System

Speed, Location, OD, status (incident report)

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MANETs For Lane Change Control and Collision Avoidance

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Control of traffic at incidents and bottlenecks

  • Highway congestions at bottlenecks is detrimental

to traffic mobility, safety and environment

  • Upstream drivers lack of information of bottleneck

therefore blindly change lanes when traffic slows down

  • Forced lane changes performed at vicinity of

bottlenecks introduce capacity drop, which further harm the flow rate

  • Appearance of trucks exacerbate the

congestion condition

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NO CONTROL NO CONNECTIVITY

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Modeling of Highway Bottleneck

Capacity drop

  • Capacity will drop when 𝜍

> πœπ‘’,𝑑.

  • Difficult to maintain maximum flow rate by controlling just the speed

π‘Ÿπ‘ = ࡝ π‘€π‘”πœ , 𝜍 ≀ πœπ‘’,𝑑 ሺ1 βˆ’ πœ—)𝐷𝑐, 𝜍 > πœπ‘’,𝑑

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Design of Lane Change Controller to prevent last minute forced lane changes Two Parts: ❖ Design of lane change control distance How far from incident should start recommending lane changes? ❖Design of lane change control pattern What lane change recommendation should give in each lane? Not a traditional control problem as the key variable is not time but space.

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Design of Lane Change Controller based on an empirical model developed using simulation tests

Length of LC Control Segment: 𝑒𝑀𝐷 = 𝜊 β‹… π‘œ n: number of lanes closed 𝜊: design parameter based on the demand and capacity

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Effect of Lane Change Control

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Effect of Lane Change Control

Without LC Control:

  • Data points for πœπ‘’ ≀ πœπ‘’,𝑑 fits the linear

relation very well;

  • Significant capacity drop occurs, πœ— β‰ˆ 0.16
  • Data points concentrate in high density

area

With LC Control:

  • No obvious capacity drop
  • πœπ‘’ at πœπ‘’ > πœπ‘’,𝑑 is approximately linear

with a negative

  • Most data points scatter close to πœπ‘’ >

πœπ‘’,𝑑

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Protecting the Network

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Variable Speed Limit Control

  • If demand increases to the point that exceeds

capacity of bottleneck then congestion will kick in. Need a control mechanism to protect the network

  • Provide speed recommendations upstream

the bottleneck or incident in order to slow down the traffic flow to become close to the throughput of the bottleneck.

  • Approach is implemented at various highways

in Europe and US but in an adhoc way

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1.Carlos F Daganzo. The cell transmission model: A dynamic representation of highway track consistent with the hydrodynamic

  • theory. Transportation Research Part B: Methodological, 28(4):269-287, 1994.
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Traffic Flow Model and Stability Analysis

Let 𝐽 = αˆΊπ·π‘’, 𝐷, 𝑒) be the state of the network and Ξ© be the set of feasible values of 𝐽 with 𝑒 β‰₯ 0, 𝐷𝑒 > 0, 𝐷 > 0. All possible relationships between 𝐷𝑒, 𝐷 and 𝑒 are described by the tree diagram below:

Capacity Drop No Capacity Drop

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Equilibrium Points when Inflow = Outflow i.e. π‘Ÿ1 = π‘Ÿ2 ሢ 𝜍 = 0

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Variable Speed Limit Control

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Variable Speed Limit (VSL) Control

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VSL Controller Density Model

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Main Theorem The proposed VSL Controller guarantees that densities converge exponentially to a single equilibrium point πœβˆ— =

min[𝑒,𝐷𝑒] 𝑀𝑔

that corresponds to maximum possible flow and speed under any demand and capacity constraints.

Proof: based on simple Lyapunov stability arguments

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𝑒 < 1 βˆ’ 𝜁0 βˆ— 𝐷𝑒

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𝑒 = 1 βˆ’ 𝜁0 βˆ— 𝐷𝑒

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1 βˆ’ 𝜁0 βˆ— 𝐷𝑒 < 𝑒 ≀ 𝐷𝑒

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𝐷𝑒 < 𝑒 < 𝐷

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Why it Works: Less for More https://www.youtube.com/watch?v=9QwPfe-_T7s

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Multiple Sections

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Numerical Simulation

Simulation Setup:

  • 1. Simulation Network:

16km-long southbound segment of I-710 freeway in California, whose normal capacity without an accident is about 6800 veh/h.

  • 2. Incident Scenarios:

We construct accident scenarios with different accident durations

  • 3. Monte Carlo Simulation

10 sets of Monte Carlo simulation for each scenario in microscopic simulations.

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Fundamental Diagram under Control

  • Traffic states can be stabilized in a small region for different

demand levels

  • Density stops increasing when demand higher than the

capacity

  • Flow speed decreases when density close to the critical value

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Performance

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Criteria Control Improvements for considered scenarios Total Time Spent in Network 10-15%. Number of Stops 80-90% Number of Lane Changes 6-10% NOx 6-7% CO2 7-8% Fuel 7-8% PM25 4-7%

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Coordination and connectivity in multimodal: Co-Simulation Optimization Control Approach

7/10/2019 NSF Workshop June 8-9, 2019 36 Final Decision Transportation Network Network Simulation Models Optimization Network states Controller Network Data Stopping Criteria

NSF CPS Synergy: Cyber Physical Regional Freight Transportation System

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Conclusions

  • Connectivity (V to V and V to I) is a key technology in achieving

transportation efficiency

  • Connectivity will generate vital information and provide missing data

that are necessary for effective control and optimization designs

  • Vehicle automation, self driving vehicles will face the major challenge
  • f Safety
  • The main causes of congestion are too many vehicles. Getting rid of

the driver and keeping the vehicle is unlikely to reduce congestion

  • Congestion is a system level problem. The system is dynamical and

feedback control and optimization are important tools to make it stable, robust and efficient

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THANK YOU

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