Tutorial ¡TPAS ¡-‑ ¡ICRAT ¡2014 ¡ 11/06/14 ¡ 1 2 INDEX 1. Introduction: need for a micro-model 4D view of the ATM 2. Test-bed Platform for ATM Studies (TPAS) TPAS: 3. Micro-scale data framework to store and manage ATM state- TEST-BED PLATFORM FOR space information ATM STUDIES 4. TPAS functionalities and case studies 5. How to use TPAS (APIs and functionalities) TUTORIAL SESSION 6. Practical example: temporal looseness for ground delays 7. Other ATM studies and Future developments Speaker: Dr. Sergio Ruiz sergio.ruiz@uab.es Scientific supervisor: Dr. Miquel A. Piera miquelangel.piera@uab.es 3 4 1. INTRODUCTION: NEED FOR A 4D MICRO-MODEL ATM VIEW “A lack of a proper coordination among the main air traffic management (ATM) actors through their different decision making processes limits the actual system capacity and leads to an inefficient and congested air transport system.” 1. INTRODUCTION TBO framework (SESAR, NextGen … ) Need for a shared 4D micro-model view of the ATM 5 6 Introduction Introduction ATM CAPACITY AND MAIN ATM ACTORS CURRENT ATM MODEL • Focused on capacity management ATM capacity = “amount of aircraft that can be managed safely by Air Traffic Management system in a period of time” Actors: • Demand: Airspace Users (AUs), e.g., airlines • Supply: Air Traffic Control (ATC) centers and Airport Operators (AOs) • Network Manager (in Europe, EUROCONTROL, in charge of ATFCM DEMAND to ensure Demand and Capacity Balancing) CAPACITY “A lack of a proper coordination among the main air traffic management (ATM) actors through their different decision making processes limits the actual system capacity and leads to an inefficient and congested air transport system.” 1 ¡
Tutorial ¡TPAS ¡-‑ ¡ICRAT ¡2014 ¡ 11/06/14 ¡ 7 8 Introduction Introduction CURRENT ATM ATFCM VIEW OF THE OPERATIONS ATM Assume NO conflicts Assumed maximum sector pre-declared capacity: 5/5 1/5 1/5 1/5 2/5 0/5 0/5 PREDICTION: SIMULATION TIME 1 2/5 0/5 ATFCM does not see what is happening at micro-scale/trajectory level 9 10 Introduction Introduction ATFCM VIEW OF THE ATM ATC VIEW OF THE ATM Assumed maximum sector pre-declared capacity: 5/5 ATC PREDICTION 0/5 0/5 0/5 Capacity problem 0/5 6/5 Conflict predicted 1/5 PREDICTION: SIMULATION TIME 2 Regulation required 0/5 0/5 Regulation Applied ATC intervention required (delay) Sector capacities satisfied ATFCM does not see what is happening at micro-scale/trajectory level 11 12 Introduction Introduction ATC VIEW OF THE ATM SESAR ATM MODEL New ¡Concept ¡of ¡Operations ¡(SESAR ¡programme): ¡ ATC PREDICTION • Trajectory-‑based ¡operations ¡ (4D ¡Trajectory) ¡ 4D contract: AU assume the compromise of flying the trajectory planned for the full flight with enough precision in the 4 dimensions (3D + time), and the ATM services agree to facilitate. • Collaborative ¡planning ¡ (sharing ¡of ¡information ¡and ¡decisions) ¡ Conflict (emergent dynamic) ¡ Good trade-off between efficiency and capacity ¡ Anticipation of the Emergent Dynamics (safety) • Dynamic ¡airspace ¡ (flexible ¡route ¡structures ¡and ¡sectors) ¡ Conflict resolution applied • New ¡and ¡innovated ¡technologies ¡ (automation, ¡precision, ¡ (local view) reliability, ¡efficiency…) ¡ A network micro-scale view is required to identify the Emergent Sector capacities satisfied Dynamics of local decisions and to coordinate the main ATM actors 2 ¡
Tutorial ¡TPAS ¡-‑ ¡ICRAT ¡2014 ¡ 11/06/14 ¡ 13 14 Introduction Introduction DESIRABLE ATM MODEL: TBO-BASED ATM MODEL Coordination requires all the actors having the same TBO (SESAR, NEXTGEN...) (SESAR, NETXGEN…) traffic view at micro-scopic/ trajecotry level • Focus on synchronized 4D trajectories (arbitrated by Network Manager) • A microscopic 4D trajectory model of the traffic flows • Automated and coordinated stakeholders’ DSTs to ensure a more precise and stable traffic synchronization along the network • Allow the participation of the Airspace Users through arbitrated negotiation processes during the entire network planning process • A common overall sight of the ATM current and predicted states • The anticipation of the potential emergent dynamics at the COORDINATION network due to local decisions shared among all the ATM stakeholders DSTs 4D Trajectory 15 16 TPAS 2. TEST-BED PLATFORM FOR ATM STUDIES (TPAS) Basic TPAS functionalities Useful for: ATM Micro-model Fast prototyping framework Test DSS Benchmarking 2.TEST-BED FOR ATM STUDIES (TPAS) Advanced TPAS functionalities Available now for the ATM community User interaction 17 18 EXAMPLE: STREAM WP-E TPAS Strategic de-confliction of 4000 trajectories over Europe in 2 hour look-ahead Processing about 20 million of waypoints (including what-if trajectories) 3. MICRO-SCALE DATA FRAMEWORK Computational time to find a solution < 60 sec. (with a regular computer) 3 ¡
Tutorial ¡TPAS ¡-‑ ¡ICRAT ¡2014 ¡ 11/06/14 ¡ 19 20 SDS SDS SPATIAL DATA 3. SPATIAL DATA STRUCTURES STRUCTURE (SDS) • Spatial Data Structure (SDS): is a database that represents a spatial region (e.g. an air A micro-scale data framework to support the representation sector) by using individual memory positions to represent each of the discrete (3D) coordinates of the air traffic demand on the different airspace sectors at of the sector. • Spatial data : spatial information (e.g., discrete trajectory representation) and non-spatial aircraft trajectory (microscopic) level è è “ATM 4D snapshot” information (e.g., Flight Number id) • Such memory positions are sorted in a way that, given a certain coordinate, the information Safety envelope in stored inside the SDS is easily recoverable applying linear mathematical formula: a given instant Y, Z : order or size of dime sion Y and Z. Physical concept of a SDS Logical concept of a SDS Turbulence generated in a given instant Δ t = 1sec. Δ t = 1sec. Δ t = 1sec. Δ t = 1sec. 21 22 SPATIAL DATA SDS SDS SPATIAL DATA STRUCTURES STRUCTURES Similar (but different) types of data structures: Other non-spatial state-space info can be added • Occupancy grids: specialized usage of SDSs used in robotics to build in real time maps (for navigation purposes) • Hash tables: to store keys/pointers –and only keys/pointers– to the data values of interest (not necessarily related with any kind of spatial data) • Look-up tables: to store pre-calculated values for a given function in order to avoid online –time consuming– calculations 23 24 SDS SDS SPATIAL DATA ANY DECISION MUST STRUCTURES PRESERVE SAFETY • Granularity or resolution : distance between discrete points of the SDS Avoid accidents & incidents Safety • Similarly to digital cameras, the resolution determines both the “quality” of the SS stored and the “efficiency” at processing and managing the spatial data. Air Traffic • The excess of resolution may lead to a loss of computer Environ. Management performance as well as to an inoperable amount of Be sustainable Capacity Allocate demand impact (ATM) memory requirements • A lack of resolution may lead to lose some important objects of the space (thus missing the detection of some existing conflicts in the CD process, i.e. false negative Cost errors) or to a lose of filtering performance. Minimum cost efficiency ATM services: facilitate orderly and safe air transportation system 4 ¡
Tutorial ¡TPAS ¡-‑ ¡ICRAT ¡2014 ¡ 11/06/14 ¡ 25 SDS FOR CONFLICT 26 SDS SDS MICRO-SCALE DATA DETECTION FRAMEWORK FOR ATM Two methods of Confliction Detection based on SDS: Each coordinate of the grid has memory positions in the database to store • Build and store the safety envelope (4D tube) information Each trajectory processed is stored and compared with trajectories using same spatial resources Y 9 8 7 6 • Store the point-mass position and pairwise comparisons (only between near cells) Conflicts are detected 5 at the moment of storing the 4D envelope points 4 by comparing time-windows Time-windows 3 comparison is only performed • perform 2 here 1 No comparison Conflicts are detected at the moment of storing the position by comparing is needed!!! 2 1 3 4 5 6 7 8 9 10 distances at each time-step X 27 28 28 SDS SDS CASE STUDY: TIME- SDS FOR A LARGE ATM AND BASED MTCD A LARGE NUMBER OF 4DT Logical concept of a SDS Physical concept of a SDS Aircraft must avoid the turbulences generated by other traffic Relational SDS Time-Space Data Structure S.Ruiz, M.A.Piera, I. del Pozo, “A Medium Term Conflict Detection and Resolution system for (TSDS) Terminal Maneuvering Area based on Spatial Data Structures and 4D Trajectories” , Journal of Transportation Research part C: Emerging Technologies, Elsevier, 2012 Information is stored in a way Faster Conflict Detection that saves 98% of memory 29 30 SDS SDS SDS 3D VS. TSDS RTSDS EXAMPLE SDS 3D SDS 4D Tr1, Tr2, Tr3, Tr4 and Tr5 processed in sequential order è t0 t1 Case 1: A A A è t0 B B B è t0 Case 2: A A A è t0 B B B è t1 Comparison always Lot of comparisons needed saved 5 ¡
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