arXiv subjects

Daniel Delahaye

Publications and source records attributed to Daniel Delahaye.

14 recordsLinked to original sources

arXiv2026

Short-Range IAM Operations to Overcome Access Gaps Around Bologna Airport

We study Innovative Air Mobility (IAM) as a dedicated hub feeder and dispersal service for Bologna Guglielmo Marconi Airport (BLQ), addressing short-range airport-to-airport connectivity where scheduled fixed-wing services are inefficient and surface access is time-consuming. Demand is generated directly from BLQ's flight timetable via a staggered rolling-window model: arriving passengers become IAM-eligible after a 20-minute buffer (a baseline 70% ''staying'' share is considered and distributed across nearby airports using a gravity model), while feeder demand for departures is timestamped backward by IAM travel time plus a 2-hour pre-flight buffer. We build an IAM-only time-expanded network (airports as vertiports) and solve two sequential subproblems: passenger routing with A* to reveal edge-level demand pressure, followed by fleet dispatch with dynamic programming to schedule a finite IAM fleet. Using OAG schedules for 5 May 2024, we simulate 09:00-17:00 with a 50-vehicle fleet (10 seats, 30 m/s) and 5-minute windows. Results show large travel-time advantages versus rail for many links (e.g., BLQ-Il Ciocco: 46 min IAM vs. 237-294 min by train), while a few pairs remain competitive for rail (e.g., BLQ-Florence). Sensitivity to IAM speed and staying share quantifies how technology and adoption shape feasible coverage, timing feasibility, and hub accessibility, demonstrating the potential of IAM to strengthen BLQ's regional reach without altering airline schedules.

math.OC↗

arXiv2026

Regulation Zero 2: A Flow-Centric Sequential Regulation Planning Framework to Counter Regulation Cascading in Pre-tactical Air Traffic Flow Management

Air Traffic Flow Management (ATFM) traffic regulations are being increasingly used as rising demand meets persistent workforce shortages. This operational strain has amplified a critical phenomenon that we call \emph{regulation cascading}: the compounding, non-linear interactions that occur when multiple regulations influence one another in unpredictable ways. As the number and complexity of regulations grow, cascading effects become more pronounced, undermining the network operator's ability to protect sectors reliably. To address this challenge, we introduce Regulation Zero 2, an updated sequential planning framework that natively operates in the regulation space, optimizing over ordered sequences of flow-level regulations that remain compatible as much as possible with existing slot-allocation systems such as CASA and RBS++. We equipped Regulation Zero 2 with new heuristics to render flow finding more efficient. At its core, the method employs a hierarchical Monte Carlo Tree Search (MCTS) that first samples congestion hotspots and then selects candidate regulations synthesized by a local proposal engine. Each proposal is evaluated by a fast First-Planned-First-Served (FPFS) allocator to estimate its reward, with these feedbacks guiding the subsequent MCTS exploration. Experiments on many pan-European summer-peak traffic days that Regulation Zero delivers promising and consistent performance. Compared to a flight-centric simulated-annealing and NSGA-II baselines, it achieves markedly higher objective improvements, while maintaining a tighter scope of impact on the network. Ablation studies also found that Regulation Cascading could reduce up to 50\% of potential effectiveness. RZ also preserves FPFS fairness and supports expert knowledge injection, offering a pragmatic and low-disruption pathway toward automation in operations.

math.OC↗

arXiv2026

Trajectory-Based Optimization for Air Traffic Control in the Terminal Maneuvering Area

We present a trajectory-based optimization framework for arrival sequencing and scheduling in the terminal maneuvering area (TMA). Unlike node-link scheduling models that reduce trajectories to time-delay variables, the proposed method computes implementable per-aircraft speed profiles and path extensions that achieve required landing separation through terminal air traffic control actions. The framework combines an analytic TMA path model, consisting of a tangent leg, a radius-to-fix turn, and a final-approach segment, with a nonlinear program (NLP) that jointly optimizes path stretch and segment speeds under a weighted objective. Three landing-order policies are examined: First-Entry-First-Serve (FEFS), First-on-Final-First-Serve (FOFFS), and FOFFS with Constrained Position Shifting (CPS) up to $k$ positions. CPS is implemented through a two-phase approach coupling mixed-integer linear programming (MILP) with NLP to select an optimized landing order before trajectory optimization. The aircraft population follows a realistic weight-class fleet mix with pair-specific wake-turbulence separation, and each scenario is perturbed by a Gaussian wind sample projected onto each segment to convert commanded airspeeds into ground speeds. An online rolling-horizon formulation commits each aircraft trajectory irrevocably upon entry, enabling real-time decision-making. Monte Carlo experiments on the simplified A80 TMA show that: (i) FOFFS consistently outperforms FEFS in delay, path stretch, and fuel burn by exploiting geometric asymmetries among arrival streams; (ii) CPS further reduces separation violations and path stretch, though with diminishing returns and rapidly increasing solver cost; (iii) fuel estimates from BADA 3 and OpenAP show consistent qualitative trends; and (iv) per-entry optimization completes in near real-time, supporting practical deployment.

eess.SY↗

arXiv2026

Geometric Trajectory Optimization for TRACON Arrivals: An NLP Approach with ATC Vectoring Maneuver Modeling

Terminal airspace congestion remains a major bottleneck in the global air traffic network. Although the Aircraft Sequencing and Scheduling Problem (ASSP) has been widely studied, many methods rely on simplified node-link abstractions that ignore the practical flight path, producing schedules that can be hard to execute under real airspace geometric constraints. This paper introduces a high-fidelity trajectory optimization framework for Terminal Arrival Sequencing and Scheduling (TASS) that explicitly models controller vectoring maneuvers. We formulate a single-stage nonlinear programming (NLP) model with a weighted objective function that optimizes Baseleg path extension and segment-wise speed profiles for arriving aircraft. The model enforces nonlinear geometric coupling between Baseleg extensions and the required Radius-to-Fix (RF) turn for Final Approach Fix (FAF) intercept through closed-form expressions. Under the First-Come-First-Served (FCFS) rule, it generates separated, operationally feasible trajectories aligned with controller practice. Monte Carlo simulations with ample runs on the A80 TRACON in Atlanta demonstrate minimal separation violations below the runway capacity threshold through tactical path stretching, with violations occurring only when arrival rates exceed the maximum tolerable rate.

math.OC↗

arXiv2025

Game-theoretic Regulated Decentralized Coordination for Airspace Sector Overload Mitigation

Decentralized air traffic management systems offer a scalable alternative to centralized control, but often assume high levels of cooperation. In practice, such assumptions frequently break down since airspace sectors operate independently and prioritize local objectives. We address the problem of sector overload in decentralized air traffic management by proposing a regulated decentralized protocol that models self-interested behaviors based on best response dynamics. Each sector adjusts the departure times of flights under its control to reduce its own congestion, without requiring centralized joint optimization. A tunable cooperativeness factor models the degree to which each sector accounts for overload in other sectors, while a minimal admissibility rule prevents local updates from creating new overloads. We prove that the proposed protocol satisfies a potential game structure, ensuring that best response dynamics converge to a pure Nash equilibrium under this restriction. In addition, we identify a sufficient condition under which an overload-free solution corresponds to a global minimizer of the potential function. Numerical experiments using 24 hours of European flight data demonstrate that the proposed algorithm substantially reduces overload even with only minimal cooperation between sectors, while maintaining scalability and achieving solution quality comparable to the centralized benchmark.

eess.SY↗

arXiv2025

A Sequential Planning Framework for the Operational Reality of Interacting Air Traffic Flow Regulations and Traffic Flow Programs

math.OC↗

arXiv2025

Adaptive Traffic-Following Scheme for Orderly Distributed Control of Multi-Vehicle Systems

We present an adaptive control scheme to enable the emergence of order within distributed, autonomous multi-agent systems. Past studies showed that under high-density conditions, order generated from traffic-following behavior reduces travel times, while under low densities, choosing direct paths is more beneficial. In this paper, we leveraged those findings to allow aircraft to independently and dynamically adjust their degree of traffic-following behavior based on the current state of the airspace. This enables aircraft to follow other traffic only when beneficial. Quantitative analyses revealed that dynamic traffic-following behavior results in lower aircraft travel times at the cost of minimal levels of additional disorder to the airspace. The sensitivity of these benefits to temporal and spatial horizons was also investigated. Overall, this work highlights the benefits, and potential necessity, of incorporating self-organizing behavior in making distributed, autonomous multi-agent systems scalable.

cs.MA↗

arXiv2025

A Unified Theoretic and Algorithmic Framework for Solving Multivariate Linear Model with $\ell^1$-norm Approximation

It is a challenging problem that solving the \textit{multivariate linear model} (MLM) $\mathbf{A}\mathbf{x}=\mathbf{b}$ with the $\ell_1 $-norm approximation method such that $||\mathbf{A}\mathbf{x}-\mathbf{b}||_1$, the $\ell_1$-norm of the \textit{residual error vector} (REV), is minimized. In this work, our contributions lie in two aspects: firstly, the equivalence theorem for the structure of the $\ell_1$-norm optimal solution to the MLM is proposed and proved; secondly, a unified algorithmic framework for solving the MLM with $\ell_1$-norm optimization is proposed and six novel algorithms (L1-GPRS, L1-TNIPM, L1-HP, L1-IST, L1-ADM, L1-POB) are designed. There are three significant characteristics in the algorithms discussed: they are implemented with simple matrix operations which do not depend on specific optimization solvers; they are described with algorithmic pseudo-codes and implemented with Python and Octave/MATLAB which means easy usage; and the high accuracy and efficiency of our six new algorithms can be achieved successfully in the scenarios with different levels of data redundancy. We hope that the unified theoretic and algorithmic framework with source code released on GitHub could motivate the applications of the $\ell_1$-norm optimization for parameter estimation of MLM arising in science, technology, engineering, mathematics, economics, and so on.

math.OC↗

arXiv2025

Noncooperative Coordination via a Trading-based Auction

Noncooperative multi-agent systems often face coordination challenges due to conflicting preferences among agents. In particular, when agents act in their own self-interest, they may prefer different choices among multiple feasible outcomes, leading to suboptimal outcomes or even safety concerns. We propose an algorithm named trading auction for consensus (TACo), a decentralized approach that enables noncooperative agents to reach consensus without communicating directly or disclosing private valuations. TACo facilitates coordination through a structured trading-based auction, where agents iteratively select choices of interest and provably reach an agreement within an a priori bounded number of steps. A series of numerical experiments validate that the termination guarantees of TACo hold in practice, and show that TACo achieves a median performance that minimizes the total cost across all agents, while allocating resources significantly more fairly than baseline approaches.

cs.GT↗

arXiv2024

On the Detection of Aircraft Single Engine Taxi using Deep Learning Models

The aviation industry is vital for global transportation but faces increasing pressure to reduce its environmental footprint, particularly CO2 emissions from ground operations such as taxiing. Single Engine Taxiing (SET) has emerged as a promising technique to enhance fuel efficiency and sustainability. However, evaluating SET's benefits is hindered by the limited availability of SET-specific data, typically accessible only to aircraft operators. In this paper, we present a novel deep learning approach to detect SET operations using ground trajectory data. Our method involves using proprietary Quick Access Recorder (QAR) data of A320 flights to label ground movements as SET or conventional taxiing during taxi-in operations, while using only trajectory features equivalent to those available in open-source surveillance systems such as Automatic Dependent Surveillance-Broadcast (ADS-B) or ground radar. This demonstrates that SET can be inferred from ground movement patterns, paving the way for future work with non-proprietary data sources. Our results highlight the potential of deep learning to improve SET detection and support more comprehensive environmental impact assessments.

cs.LG↗

arXiv2021

Analyzing and comparing door-to-door travel times for air transportation using aggregated Uber data

Improving the passenger air travel experience is one of the explicit goals set by the Next Generation Air Transportation System in the United States and by the Advisory Council for Aeronautics Research in Europe FlightPath 2050. Both suggest door-to-door travel times as a potential metric for these objectives. In this paper, we propose a data-driven model to estimate door-to-door travel times and compare the reach and performance of different access modes to a city, as well as conduct segment analysis of full door-to-door trips. This model can also be used to compare cities with respect to the integration of their airport within their road structure. We showcase multiple applications of this full door-to-door travel time model to demonstrate how the model can be used to locate where progress can be made.

cs.OH↗

arXiv2020

Putting the Air Transportation System to sleep: a passenger perspective measured by passenger-generated data

This paper aims at analyzing the effect on the US air transportation system of the travel restriction measures implemented during the COVID-19 pandemic from a passenger perspective. Flight centric data are not already publicly and widely available therefore the traditional metrics used to measure the state of this system are not yet available. Seven metrics based on three different passenger-generated datasets are proposed here. They aim to measure in close to real-time how the travel restriction measures impacted the relation between major stakeholders of the US air transportation system, namely passengers, airports and airlines.

physics.soc-ph↗

arXiv2019

A Reinforcement Learning Perspective on the Optimal Control of Mutation Probabilities for the (1+1) Evolutionary Algorithm: First Results on the OneMax Problem

We study how Reinforcement Learning can be employed to optimally control parameters in evolutionary algorithms. We control the mutation probability of a (1+1) evolutionary algorithm on the OneMax function. This problem is modeled as a Markov Decision Process and solved with Value Iteration via the known transition probabilities. It is then solved via Q-Learning, a Reinforcement Learning algorithm, where the exact transition probabilities are not needed. This approach also allows previous expert or empirical knowledge to be included into learning. It opens new perspectives, both formally and computationally, for the problem of parameter control in optimization.

cs.NE↗

arXiv2013

Airport Gate Scheduling for Passengers, Aircraft, and Operation

Passengers' experience is becoming a key metric to evaluate the air transportation system's performance. Efficient and robust tools to handle airport operations are needed along with a better understanding of passengers' interests and concerns. Among various airport operations, this paper studies airport gate scheduling for improved passengers' experience. Three objectives accounting for passengers, aircraft, and operation are presented. Trade-offs between these objectives are analyzed, and a balancing objective function is proposed. The results show that the balanced objective can improve the efficiency of traffic flow in passenger terminals and on ramps, as well as the robustness of gate operations.

eess.SY↗