designing traffic flow management strategies under …...2015/06/20 · weather traverses the ny...

© 2015 The MITRE Corporation. All rights reserved.Joint Copyright with Sandip Roy, WSU

Approved for Public Release; Distribution Unlimited. 15-1785

Designing Traffic Flow Management Strategies Under Uncertainty

Dr. Christine Taylor

Tudor Masek

Dr. Craig Wanke

The MITRE Corporation

Center for Advanced Aviation System

Development (CAASD)

Prof. Sandip Roy

Washington State University

Department of Electrical Engineering

11th USA/Europe ATM R&D Seminar

Lisbon, Portugal23-26 June, 2015

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Approved for Public Release; Distribution Unlimited. 15-1785© 2015 The MITRE Corporation. All rights reserved. Joint Copyright with Sandip Roy, WSU

Strategic TFM Planning for Weather

12Z 14Z 16Z

Probabilistic forecasts identify regions of potential weather activity

What is the range and likelihood of different weather scenarios occurring?

What are the potential scenarios of ATM impacts?

What options are available to mitigate congestion and when do we have to act?

Short Range Ensemble Forecast (SREF) for 28 July, 2014

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Leveraging Weather Forecast Scenarios for TFM

12Z

14Z

16Z

Weather traverses just north of the NY Metro

12Z

14Z

16Z

12Z

14Z

16Z

Weather traverses the NY Metro

Weather traverses far north of the NY Metro

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Forecast Uncertainty Complicates Planning

16Z

Weather traverses just north of the NY Metro

16Z 16Z

Weather traverses the NY Metro

Weather traverses far north of the NY Metro

www.publicdomainpictures.net

TMI Plan for S1

TMI Plan for S3

TMI Plan for all Scenarios

TMI Plan for S2

S1Probability Low (2/21)

Impact HighS2

Probability Low (2/21)

Impact ModerateS3

Probability High (17/21)

Impact Low

Current TMI Plan & Advisories

Individual Scenario OptimizationDesigns the best strategy for each scenario

Robust OptimizationDesigns the best strategy considering all scenarios

Adaptive Optimization

Designs the best strategy considering scenario evolution and opportunity to adapt

TMI = Traffic Management Initiative

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The Adaptive Planning Framework

Provides strategic TFM decision support under forecast uncertainty

– Quantifies when, where, and how forecasts diverge

– Recommends executable plans (actions) and contingency plans (advisories)

– Capture trade-offs of acting now vs. waiting

– Accounts for the ability to adapt to new information

Wide range of literature on look-ahead decision-making (sequential decision-making via dynamic programming, model predictive control, etc.)

New features of this approach are:

– Uncertainties represented as spatio-temporal (trajectory) scenarios

– Realistic modeling of Traffic Management Initiatives (TMIs)

– Heuristic optimization enables large-scale simulation in the loop

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Develop Scenarios of Weather-Impact Evolution

Identify critical features

Measure Forecast

SimilaritiesTranslate to congestion

forecast

Forecast Ensemble Weather Impact Ensemble

Scenarios of Weather and

Capacity Impact

Demand forecast

Fast-time Traffic

SimulationImpact S1: p =2/21

S2: p =2/21

S3: p= 17/21

12z-14z

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Construct Adaptive Planning Framework

Forecast Look-ahead12Z 14Z 16Z

All forecasts

S1 Cluster

S2 & S3 Cluster

S1 Cluster

S3 Cluster

S2 ClusterCurrent Decision

Contingency Plan

Contingency Plan

Contingency Plan

Contingency Plan

Contingency Plan

Strategy 1

Strategy 2

Strategy 3

S1: p =2/21

S2: p =2/21

S3: p= 17/21

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Defining the TMI Design Space

FCAA08

BOSGDP

JFKGDP

EWR GDP

LGA GDP

GDP Parameters

Rate (#/hour)Start time (UTC)Duration (hours)Tier (distance)

AFP Parameters

Rate (#/hour)Start time (UTC)Duration (hours)

TMIs

Decision Periods

12 14 16 18 20

BOS EWR JFK LGA FCA

Design Space

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Total Ground Delay

Total Sector Delay

Total Arrival Schedule Delay

Other Delay Statistics

Throughput (Airports, Airspace)

Schedule Integrity

Surface Congestion

Expected value

Receding time horizon

Minimize maximum impact

Satisfy a goal/constraint

Develop robust (minimum variance) solutions

Quantifying Strategy Performance

Strategy Objective

Function of Cost and Scenario Probability

Cost

Measures Effectiveness of Strategy for Scenario

Strategy Objective

𝑆𝑂 =

𝑡

𝛼𝑡

𝑘

𝑝𝑘(𝑡) C𝑘(𝑡)

Cost

C𝑘 =

𝑡

𝐺𝐷𝑘(𝑡)+ 2∗𝑆𝐷𝑘(𝑡)

Cost 1

Cost 2

Cost 3

Strategy 1

Strategy 2

Strategy 3

S1: p =2/21

S2: p =2/21

S3: p= 17/21

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Design Space

Genetic Algorithm Overview

Genetic Algorithm

Construct Genome

Strategy Objective

Evaluate Fitness

BOS

EWR

FCA

JFK

LGA

BOS

EWR

FCA

JFK

LGA

Fitness Fitness

BOS

EWR

FCA

JFK

LGA

Fitness

FCA

Process continues until termination criteria occurs

Genetic Algorithms (GAs) are a class of heuristic search methods that mimic biological evolution

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Design Space

Optimizing Adaptive Strategies

Genetic Algorithm

Construct Genome

Strategy Objective

Evaluate Fitness

Flow Contingency Management (FCM)

Strategy Objective

𝑆𝑂 =

𝑡

𝛼𝑡

𝑘


Cost

C𝑘 =

𝑡


Scenarios of Weather and

Capacity Impact

Demand forecast

Fast-time Traffic

Simulation

TMI Strategies

Costs

Cost 1

Cost 2

Cost 3

Strategy 1

Strategy 2

Strategy 3

S1: p =2/21

S2: p =2/21

S3: p= 17/21

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WI Scenario TMIs

Planning Horizon (UTC)

12 14 16 18 20

S1

BOS 55 55 55 55

EWR 40

JFK 35 35

LGA 30 30 30

FCA 70

S2

BOS 55 55

EWR 25 25

JFK 50

LGA

FCA 70

S3

BOS

EWR 25 25 25

JFK

LGA

FCA

Individual Scenario Optimization

Genetic Algorithm

C𝑘 =

𝑡


Genetic Algorithm

Genetic Algorithm

Requires Additional Integration

Cost 1

Cost 2

Cost 3

Strategy 1

Strategy 2

Strategy 3

S1: p =2/21

S2: p =2/21

S3: p= 17/21

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Robust Scenario Optimization

Genetic Algorithm

C𝑘 =

𝑡


Cost 1

Cost 2

Cost 3

Robust Scenario

S1: p =2/21

S2: p =2/21

S3: p= 17/21

𝑆𝑂 =

𝑡

𝛼𝑡

𝑘


Strategy Objective

WI Scenario TMIs

Planning Horizon (UTC)

12 14 16 18 20

S1

BOS 55 55 55 55

EWR 40

JFK 35 35

LGA 30 30 30

FCA 70

S2

BOS 55 55

EWR 25 25

JFK 50

LGA

FCA 70

S3

BOS

EWR 25 25 25

JFK

LGA

FCA

Individual Scenario Optimization

WI Scenario TMIs

Decision Periods

12 14 16 18 20

All

BOS 40

EWR 25 25 25

JFK

LGA

FCA

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WI Scenario TMIs

Decision Periods

12 14 16 18 20

S1

BOS 40 40 40 40

EWR 25 25 25 25

JFK 50 50 50

LGA 30 30 30

FCA

S2

BOS 40 55 55 55

EWR 40 40

JFK 35 35

LGA 20 20 20

FCA 90 90

S3

BOS 40

EWR 40

JFK 35 35

LGA

FCA 90

Adaptive Scenario Optimization

S1: p =2/21

S2: p =2/21

S3: p= 17/21

Genetic Algorithm

Strategy 1

Strategy 2

Strategy 3

C𝑘 =

𝑡


Cost 1

Cost 2

Cost 3

𝑆𝑂 =

𝑡

𝛼𝑡

𝑘


Strategy Objective

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Comparing Strategy Performance

WI Scenarios Probability Robust Adaptive

S1 2/21 57757 45274

S2 2/21 13876 13201

S3 17/21 4305 4450

Strategy Objective

10307 9172

12 Z Decision

Robust EWR 25

Adaptive BOS 40

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Summary

Adaptive Planning Framework captures the trade-off between waiting and action under forecast uncertainty

– Promotes incremental decision making by formalizing the ability to adapt

– Identifies actions that may be critical for managing high-impact futures

Continuing research focus

– How do different costs and strategy objectives affect solutions?

– How does forecast uncertainty affect hedging options?

– What constraints on solution adaptability are required to ensure operational relevance?

– Can model (network) structure be exploited to improve GA performance?

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NOTICE

This work was produced for the U.S. Government under Contract DTFAWA-10-C-00080 and is subject to Federal Aviation Administration Acquisition Management

System Clause 3.5-13, Rights In Data-General, Alt. III and Alt. IV (Oct. 1996).

The contents of this document reflect the views of the author and The MITRE Corporation and do not necessarily reflect the views of the FAA or the DOT.

Neither the Federal Aviation Administration nor the Department of Transportation makes any warranty or guarantee, expressed or implied, concerning the content

or accuracy of these views.

2015 The MITRE Corporation. All Rights Reserved.

Joint copyright held by

Sandip Roy, Washington State University

designing traffic flow management strategies under …...2015/06/20 · weather traverses the ny...

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