Showing 1–28 of 28 results for author: Li, M Z

Augmenting airline networks using airside-to-airside buses to strengthen system resilience under disruptions

Authors: Micah M. Borrero, Max Z. Li

Abstract: Each year, disruptions in the air transportation network strand millions of passengers and cost airlines billions in revenue. Airline networks prioritize operational and cost efficiency through hub-and-spoke structures that maximize revenue; however, these hubs also act as critical choke points during disruptions. Previous studies have focused on reactionary measures in response to air transportat… ▽ More Each year, disruptions in the air transportation network strand millions of passengers and cost airlines billions in revenue. Airline networks prioritize operational and cost efficiency through hub-and-spoke structures that maximize revenue; however, these hubs also act as critical choke points during disruptions. Previous studies have focused on reactionary measures in response to air transportation network disruptions, whereas this work proposes a proactive strategy to improve resilience by reconfiguring the network's topology. Specifically, we consider airside-to-airside bus lines as a low-cost, frequent alternative to short, regional flights, offering service that can circumvent air traffic-related delays. We develop a network construction model that augments the existing air transportation network with these bus lines. The augmented networks are analyzed through an agent-based simulation, where increased resilience is measured in terms of decreased average hourly passenger delays under both nominal and disrupted conditions. Our results demonstrate that converting 10 regional routes from air service to airside-to-airside bus service, for a baseline scenario that is constrained by a $10 million investment budget, can reduce passenger delays by an average of 8% on disrupted days and 6% on nominal days. Furthermore, through a sensitivity analysis, we show that while augmenting the system using these buses decreases operational costs compared to the historic air-only network, continuously expanding bus parameters (i.e., range and investment budget) yields diminishing returns in delay mitigation. Finally, we discuss real-world precedents alongside regulatory and political hurdles to implementation. The proposed framework offers airlines, airports, and regulators a decision-support tool for integrating multimodal strategies into future disruption management policies. △ Less

Submitted 30 June, 2026; originally announced June 2026.

Optimizing Agricultural Drone Operations: From Launch and Recovery Siting to Tiered Routing Strategies

Authors: Ethan Kolby, Josh Noble, Max Z. Li

Abstract: Drones are increasingly used in agriculture, where tight margins demand efficient planning. Current optimization tools suffer from exponential runtimes as problem sizes grow, necessitating practical heuristics for daily operations. This paper presents an operational framework and benchmarking analysis for drone spraying operations. We evaluate the trade-offs between facility siting methods and tie… ▽ More Drones are increasingly used in agriculture, where tight margins demand efficient planning. Current optimization tools suffer from exponential runtimes as problem sizes grow, necessitating practical heuristics for daily operations. This paper presents an operational framework and benchmarking analysis for drone spraying operations. We evaluate the trade-offs between facility siting methods and tiered routing parameters. For facility siting, comparing a Mixed-Integer Program (MIP) baseline against a $p$-Median heuristic shows that the heuristic reduces runtime by three orders of magnitude, from over 97 seconds to under 1.2 seconds, with only a 4\% reduction in serviced field area. For route planning, a tiered problem decomposition approach partitioning the target area into 6 to 8 spatial clusters reduces computation time by an order of magnitude with minimal degradation in serviced area. This framework achieves minute-scale planning on commodity hardware, demonstrating operational relevance. Future research will incorporate weather modeling, integrated optimization of facility location and routing, and validation across diverse field geometries. △ Less

Submitted 18 June, 2026; originally announced June 2026.

Comments: 33 pages, 4 tables, 10 figures, preprint submitted to Drone Systems & Applications

Integrated Exploration-Aware UAV Route Optimization and Path Planning

Authors: Jimin Choi, Grant Stagg, Cameron K. Peterson, Max Z. Li

Abstract: Uncrewed aerial vehicles (UAVs) are increasingly used for exploration-driven monitoring in hazardous environments such as disaster zones, contaminated sites, wildfire areas, and damaged infrastructure, where limited flight endurance must be allocated between visiting reported locations and gathering new information. In these settings, prior information regarding hazards is often incomplete, spatia… ▽ More Uncrewed aerial vehicles (UAVs) are increasingly used for exploration-driven monitoring in hazardous environments such as disaster zones, contaminated sites, wildfire areas, and damaged infrastructure, where limited flight endurance must be allocated between visiting reported locations and gathering new information. In these settings, prior information regarding hazards is often incomplete, spatially imprecise, and subject to change during execution. For example, initial reports may identify a region where a hazard is likely to exist, but the actual hazard may be displaced, partially observed, or entirely unreported. We present an integrated exploration-aware UAV route optimization and path planning framework for hazard monitoring under uncertain and evolving prior information. The environment is represented as a spatial risk map, where each location has an associated belief of hazardous conditions. Reported hazards are modeled as uncertain regions of interest (ROIs) rather than confirmed target locations, requiring the UAV to inspect reported areas while also using its limited flight endurance to explore informative regions. The proposed method solves a vehicle routing problem over reported ROIs, augments the route with auxiliary pseudo-nodes to improve spatial coverage, allocates the remaining flight distance budget across route segments, and optimizes dynamically feasible B-spline trajectories for local exploration. During execution, UAV measurements update a grid-based belief map, and the remaining trajectory is replanned when new information and the remaining budget justify adaptation. Across 48 scenario configurations, online replanning improves average KL reduction by 15.9% over the offline optimized planner and 48.6% over straight-line traversal. △ Less

Submitted 18 June, 2026; v1 submitted 27 May, 2026; originally announced May 2026.

Collaborative Decision-Making and Optimal Utilization of Pathfinding Flights during Convective Weather

Authors: Jimin Choi, Husni R. Idris, Huy T. Tran, Max Z. Li

Abstract: Air traffic operations are strongly influenced by convective weather, and one common response is pathfinder operations, in which a designated aircraft tests the viability of weather-impacted airspace and routes. Despite relatively routine use in practice, how pathfinder operations evolve under uncertainty and how the pathfinder decision-making process unfolds are largely treated as exogenous. Addr… ▽ More Air traffic operations are strongly influenced by convective weather, and one common response is pathfinder operations, in which a designated aircraft tests the viability of weather-impacted airspace and routes. Despite relatively routine use in practice, how pathfinder operations evolve under uncertainty and how the pathfinder decision-making process unfolds are largely treated as exogenous. Addressing this gap requires jointly modeling weather-driven system accessibility, flight responses to pathfinder offers, and the sequencing of those offers to improve outcomes. We develop a unified analytical framework that connects weather-driven system state transitions, flight acceptance decisions, and the sequencing of pathfinder offers. We first construct a four-state Markov chain to model stochastic closure and reopening of exit points, or fixes, out of the terminal departure airspace surrounding a major airport, pathfinder selection, and pathfinding execution, and analyze its steady-state behavior to characterize long-term capacity and delay implications. We introduce utility-based decision models for flights, air traffic control (ATC), and dispatchers, and analyze worst-case collective rejection to quantify system vulnerability under selfless behavior and uncertainty. Finally, we formulate optimization problems that model ATC-initiated and dispatcher-initated pathfinder offers, with the goal of optimizing the sequence of pathfinder offers. Using a discrete event simulation for a major US airport, we show that ATC- and dispatcher-driven objectives lead to distinct, near real-time sequencing strategies, providing the first formal decision models for pathfinder operations under weather uncertainty. △ Less

Submitted 26 January, 2026; originally announced January 2026.

Bayesian Ambiguity Contraction-based Adaptive Robust Markov Decision Processes for Adversarial Surveillance Missions

Authors: Jimin Choi, Max Z. Li

Abstract: Collaborative Combat Aircraft (CCAs) are envisioned to enable autonomous Intelligence, Surveillance, and Reconnaissance (ISR) missions in contested environments, where adversaries may act strategically to deceive or evade detection. These missions pose challenges due to model uncertainty and the need for safe, real-time decision-making. Robust Markov Decision Processes (RMDPs) provide worst-case g… ▽ More Collaborative Combat Aircraft (CCAs) are envisioned to enable autonomous Intelligence, Surveillance, and Reconnaissance (ISR) missions in contested environments, where adversaries may act strategically to deceive or evade detection. These missions pose challenges due to model uncertainty and the need for safe, real-time decision-making. Robust Markov Decision Processes (RMDPs) provide worst-case guarantees but are limited by static ambiguity sets that capture initial uncertainty without adapting to new observations. This paper presents an adaptive RMDP framework tailored to ISR missions with CCAs. We introduce a mission-specific formulation in which aircraft alternate between movement and sensing states. Adversarial tactics are modeled as a finite set of transition kernels, each capturing assumptions about how adversarial sensing or environmental conditions affect rewards. Our approach incrementally refines policies by eliminating inconsistent threat models, allowing agents to shift from conservative to aggressive behaviors while maintaining robustness. We provide theoretical guarantees showing that the adaptive planner converges as credible sets contract to the true threat and maintains safety under uncertainty. Experiments under Gaussian and non-Gaussian threat models across diverse network topologies show higher mission rewards and fewer exposure events compared to nominal and static robust planners. △ Less

Submitted 1 December, 2025; originally announced December 2025.

Tracing the Representation Geometry of Language Models from Pretraining to Post-training

Authors: Melody Zixuan Li, Kumar Krishna Agrawal, Arna Ghosh, Komal Kumar Teru, Adam Santoro, Guillaume Lajoie, Blake A. Richards

Abstract: Standard training metrics like loss fail to explain the emergence of complex capabilities in large language models. We take a spectral approach to investigate the geometry of learned representations across pretraining and post-training, measuring effective rank (RankMe) and eigenspectrum decay ($α$-ReQ). With OLMo (1B-7B) and Pythia (160M-12B) models, we uncover a consistent non-monotonic sequence… ▽ More Standard training metrics like loss fail to explain the emergence of complex capabilities in large language models. We take a spectral approach to investigate the geometry of learned representations across pretraining and post-training, measuring effective rank (RankMe) and eigenspectrum decay ($α$-ReQ). With OLMo (1B-7B) and Pythia (160M-12B) models, we uncover a consistent non-monotonic sequence of three geometric phases during autoregressive pretraining. The initial "warmup" phase exhibits rapid representational collapse. This is followed by an "entropy-seeking" phase, where the manifold's dimensionality expands substantially, coinciding with peak n-gram memorization. Subsequently, a "compression-seeking" phase imposes anisotropic consolidation, selectively preserving variance along dominant eigendirections while contracting others, a transition marked with significant improvement in downstream task performance. We show these phases can emerge from a fundamental interplay of cross-entropy optimization under skewed token frequencies and representational bottlenecks ($d \ll |V|$). Post-training further transforms geometry: SFT and DPO drive "entropy-seeking" dynamics to integrate specific instructional or preferential data, improving in-distribution performance while degrading out-of-distribution robustness. Conversely, RLVR induces "compression-seeking", enhancing reward alignment but reducing generation diversity. △ Less

Submitted 26 September, 2025; originally announced September 2025.

Comments: 33 pages, 14 figures, 9 tables

Optimization-Guided Exploration of Advanced Air Mobility Congestion Management Strategies with Stochastic Demands

Authors: Haochen Wu, Lesley A. Weitz, Jeffrey M. Henderson, Max Z. Li

Abstract: Advanced Air Mobility (AAM) represents an evolution of the air transportation system by introducing low-altitude, potentially high-traffic environments. AAM operations will be enabled by both new aircraft, as well as new safety- and efficiency-critical supporting infrastructure. Published concepts of operations from both public and private sector entities establish notions such as federated manage… ▽ More Advanced Air Mobility (AAM) represents an evolution of the air transportation system by introducing low-altitude, potentially high-traffic environments. AAM operations will be enabled by both new aircraft, as well as new safety- and efficiency-critical supporting infrastructure. Published concepts of operations from both public and private sector entities establish notions such as federated management of the airspace and public-private partnerships for AAM air traffic, but there is a gap in the literature in terms of integrated tools that consider all three critical elements: AAM fleet operators (\emph{lower} layer), airspace service providers (\emph{middle} layer), and overall system governance from the legacy air navigation service provider (\emph{upper} layer). In this work, we explore modeling congestion management within the AAM setting using a bi-level optimization approach, focusing on (1) time-varying, stochastic AAM demand, (2) differing congestion management strategies, and (3) the impact of unscheduled, \enquote{pop-up} demand. We show that our bi-level formulation can be tractably solved using a Neural Network-based surrogate which returns solution qualities within 0.1-5.2\% of the optimal solution. Additionally, we show that our congestion management strategies can reduce congestion by 25.7-39.8\% when compared to the scenario of no strategies being applied. Finally, we also show that while pop-up demand degrades congestion conditions, our congestion management strategies fare better against pop-up demand than the no strategy scenario. The work herein contributes a rigorous modeling and simulation tool to help evaluate future AAM traffic management concepts and strategies. △ Less

Submitted 22 September, 2025; originally announced September 2025.

Comments: First US-Europe Air Transportation Research and Development Symposium (ATRDS2025)

Integrated Prediction and Distributionally Robust Optimization for Air Traffic Management

Authors: Haochen Wu, Xinting Zhu, Lishuai Li, Max Z. Li

Abstract: Strategic Traffic Management Initiatives (TMIs) such as Ground Delay Programs (GDPs) play a crucial role in mitigating operational costs associated with air traffic demand-capacity imbalances. However, GDPs can only be planned (e.g., duration, delay assignments) with confidence if the future capacities at constrained resources (i.e., airports) are predictable. In reality, such future capacities ar… ▽ More Strategic Traffic Management Initiatives (TMIs) such as Ground Delay Programs (GDPs) play a crucial role in mitigating operational costs associated with air traffic demand-capacity imbalances. However, GDPs can only be planned (e.g., duration, delay assignments) with confidence if the future capacities at constrained resources (i.e., airports) are predictable. In reality, such future capacities are uncertain, and predictive models may provide predictions that are vulnerable to errors and distribution shifts. Motivated by the goal of planning optimal GDPs that are distributionally robust against airport capacity prediction errors, we study a fully integrated learning-driven optimization framework. We design a deep learning-based prediction model capable of forecasting arrival and departure capacity distributions across a network of airports. We incorporate the predictions into a distributionally robust formulation of the multi-airport ground holding program (DR-MAGHP). Our results demonstrate that DR-MAGHP can achieve up to a 15.6% improvement over the stochastic programming formulation (SP-MAGHP) under airport capacity distribution shifts. We conclude by outlining future research directions aimed at enhancing both the learning and optimization components of the framework. △ Less

Submitted 22 September, 2025; originally announced September 2025.

Autonomous Air-Ground Vehicle Operations Optimization in Hazardous Environments: A Multi-Armed Bandit Approach

Authors: Jimin Choi, Max Z. Li

Abstract: Hazardous environments such as chemical spills, radiological zones, and bio-contaminated sites pose significant threats to human safety and public infrastructure. Rapid and reliable hazard mitigation in these settings often unsafe for humans, calling for autonomous systems that can adaptively sense and respond to evolving risks. This paper presents a decision-making framework for autonomous vehicl… ▽ More Hazardous environments such as chemical spills, radiological zones, and bio-contaminated sites pose significant threats to human safety and public infrastructure. Rapid and reliable hazard mitigation in these settings often unsafe for humans, calling for autonomous systems that can adaptively sense and respond to evolving risks. This paper presents a decision-making framework for autonomous vehicle dispatch in hazardous environments with uncertain and evolving risk levels. The system integrates a Bayesian Upper Confidence Bound (BUCB) sensing strategy with task-specific vehicle routing problems with profits (VRPP), enabling adaptive coordination of unmanned aerial vehicles (UAVs) for hazard sensing and unmanned ground vehicles (UGVs) for cleaning. Using VRPP allows selective site visits under resource constraints by assigning each site a visit value that reflects sensing or cleaning priorities. Site-level hazard beliefs are maintained through a time-weighted Bayesian update. BUCB scores guide UAV routing to balance exploration and exploitation under uncertainty, while UGV routes are optimized to maximize expected hazard reduction under resource constraints. Simulation results demonstrate that our framework reduces the number of dispatch cycles to resolve hazards by around 30% on average compared to uninformed baseline dispatch strategies, underscoring the value of uncertainty-aware vehicle dispatch for reliable hazard mitigation. △ Less

Submitted 8 June, 2026; v1 submitted 11 August, 2025; originally announced August 2025.

Journal ref: Journal of Aerospace Information Systems, 2026

Pathfinders in the Sky: Formal Decision-Making Models for Collaborative Air Traffic Control in Convective Weather

Authors: Jimin Choi, Kartikeya Anand, Husni R. Idris, Huy T. Tran, Max Z. Li

Abstract: Air traffic can be significantly disrupted by weather. Pathfinder operations involve assigning a designated aircraft to assess whether airspace that was previously impacted by weather can be safely traversed through. Despite relatively routine use in air traffic control, there is little research on the underlying multi-agent decision-making problem. We seek to address this gap herein by formulatin… ▽ More Air traffic can be significantly disrupted by weather. Pathfinder operations involve assigning a designated aircraft to assess whether airspace that was previously impacted by weather can be safely traversed through. Despite relatively routine use in air traffic control, there is little research on the underlying multi-agent decision-making problem. We seek to address this gap herein by formulating decision models to capture the operational dynamics and implications of pathfinders. Specifically, we construct a Markov chain to represent the stochastic transitions between key operational states (e.g., pathfinder selection). We then analyze its steady-state behavior to understand long-term system dynamics. We also propose models to characterize flight-specific acceptance behaviors (based on utility trade-offs) and pathfinder selection strategies (based on sequential offer allocations). We then conduct a worst-case scenario analysis that highlights risks from collective rejection and explores how selfless behavior and uncertainty affect system resilience. Empirical analysis of data from the US Federal Aviation Administration demonstrates the real-world significance of pathfinder operations and informs future model calibration. △ Less

Submitted 8 June, 2026; v1 submitted 3 May, 2025; originally announced May 2025.

Journal ref: 2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC)

Bi-Level Route Optimization and Path Planning with Hazard Exploration

Authors: Jimin Choi, Grant Stagg, Cameron K. Peterson, Max Z. Li

Abstract: Effective risk monitoring in dynamic environments such as disaster zones requires an adaptive exploration strategy to detect hidden threats. We propose a bi-level unmanned aerial vehicle (UAV) monitoring strategy that efficiently integrates high-level route optimization with low-level path planning for known and unknown hazards. At the high level, we formulate the route optimization as a vehicle r… ▽ More Effective risk monitoring in dynamic environments such as disaster zones requires an adaptive exploration strategy to detect hidden threats. We propose a bi-level unmanned aerial vehicle (UAV) monitoring strategy that efficiently integrates high-level route optimization with low-level path planning for known and unknown hazards. At the high level, we formulate the route optimization as a vehicle routing problem (VRP) to determine the optimal sequence for visiting known hazard locations. To strategically incorporate exploration efficiency, we introduce an edge-based centroidal Voronoi tessellation (CVT), which refines baseline routes using pseudo-nodes and allocates path budgets based on the UAV's battery capacity using a line segment Voronoi diagram. At the low level, path planning maximizes information gain within the allocated path budget by generating kinematically feasible B-spline trajectories. Bayesian inference is applied to dynamically update hazard probabilities, enabling the UAVs to prioritize unexplored regions. Simulation results demonstrate that edge-based CVT improves spatial coverage and route uniformity compared to the node-based method. Additionally, our optimized path planning consistently outperforms baselines in hazard discovery rates across a diverse set of scenarios. △ Less

Submitted 8 June, 2026; v1 submitted 31 March, 2025; originally announced March 2025.

Journal ref: 2025 IEEE 64th Conference on Decision and Control (CDC)

A Convex Formulation of Game-theoretic Hierarchical Routing

Authors: Dong Ho Lee, Kaitlyn Donnel, Max Z. Li, David Fridovich-Keil

Abstract: Hierarchical decision-making is a natural paradigm for coordinating multi-agent systems in complex environments such as air traffic management. In this paper, we present a bilevel framework for game-theoretic hierarchical routing, where a high-level router assigns discrete routes to multiple vehicles who seek to optimize potentially noncooperative objectives that depend upon the assigned routes. T… ▽ More Hierarchical decision-making is a natural paradigm for coordinating multi-agent systems in complex environments such as air traffic management. In this paper, we present a bilevel framework for game-theoretic hierarchical routing, where a high-level router assigns discrete routes to multiple vehicles who seek to optimize potentially noncooperative objectives that depend upon the assigned routes. To address computational challenges, we propose a reformulation that preserves the convexity of each agent's feasible set. This convex reformulation enables a solution to be identified efficiently via a customized branch-and-bound algorithm. Our approach ensures global optimality while capturing strategic interactions between agents at the lower level. We demonstrate the solution concept of our framework in two-vehicle and three-vehicle routing scenarios. △ Less

Submitted 17 March, 2025; originally announced March 2025.

Examining the Dynamics of Local and Transfer Passenger Share Patterns in Air Transportation

Authors: Xufang Zheng, Qilei Zhang, Victoria Cobb, Max Z. Li

Abstract: The air transportation local share, defined as the proportion of local passengers relative to total passengers, serves as a critical metric reflecting how economic growth, carrier strategies, and market forces jointly influence demand composition. This metric is particularly useful for examining industry structure changes and large-scale disruptive events such as the COVID-19 pandemic. This resear… ▽ More The air transportation local share, defined as the proportion of local passengers relative to total passengers, serves as a critical metric reflecting how economic growth, carrier strategies, and market forces jointly influence demand composition. This metric is particularly useful for examining industry structure changes and large-scale disruptive events such as the COVID-19 pandemic. This research offers an in-depth analysis of local share patterns on more than 3900 Origin and Destination (O&D) pairs across the U.S. air transportation system, revealing how economic expansion, the emergence of low-cost carriers (LCCs), and strategic shifts by legacy carriers have collectively elevated local share. To efficiently identify the local share characteristics of thousands of O&Ds and to categorize the O&Ds that have the same behavior, a range of time series clustering methods were used. Evaluation using visualization, performance metrics, and case-based examination highlighted distinct patterns and trends, from magnitude-based stratification to trend-based groupings. The analysis also identified pattern commonalities within O&D pairs, suggesting that macro-level forces (e.g., economic cycles, changing demographics, or disruptions such as COVID-19) can synchronize changes between disparate markets. These insights set the stage for predictive modeling of local share, guiding airline network planning and infrastructure investments. This study combines quantitative analysis with flexible clustering to help stakeholders anticipate market shifts, optimize resource allocation strategies, and strengthen the air transportation system's resilience and competitiveness. △ Less

Submitted 21 February, 2025; originally announced March 2025.

Comments: 30 pages, 14 figures, 1 table

Rare event modeling with self-regularized normalizing flows: what can we learn from a single failure?

Authors: Charles Dawson, Van Tran, Max Z. Li, Chuchu Fan

Abstract: Increased deployment of autonomous systems in fields like transportation and robotics have seen a corresponding increase in safety-critical failures. These failures can be difficult to model and debug due to the relative lack of data: compared to tens of thousands of examples from normal operations, we may have only seconds of data leading up to the failure. This scarcity makes it challenging to t… ▽ More Increased deployment of autonomous systems in fields like transportation and robotics have seen a corresponding increase in safety-critical failures. These failures can be difficult to model and debug due to the relative lack of data: compared to tens of thousands of examples from normal operations, we may have only seconds of data leading up to the failure. This scarcity makes it challenging to train generative models of rare failure events, as existing methods risk either overfitting to noise in the limited failure dataset or underfitting due to an overly strong prior. We address this challenge with CalNF, or calibrated normalizing flows, a self-regularized framework for posterior learning from limited data. CalNF achieves state-of-the-art performance on data-limited failure modeling and inverse problems and enables a first-of-a-kind case study into the root causes of the 2022 Southwest Airlines scheduling crisis. △ Less

Submitted 28 February, 2025; originally announced February 2025.

Comments: Published at ICLR 2025

CHATATC: Large Language Model-Driven Conversational Agents for Supporting Strategic Air Traffic Flow Management

Authors: Sinan Abdulhak, Wayne Hubbard, Karthik Gopalakrishnan, Max Z. Li

Abstract: Generative artificial intelligence (AI) and large language models (LLMs) have gained rapid popularity through publicly available tools such as ChatGPT. The adoption of LLMs for personal and professional use is fueled by the natural interactions between human users and computer applications such as ChatGPT, along with powerful summarization and text generation capabilities. Given the widespread use… ▽ More Generative artificial intelligence (AI) and large language models (LLMs) have gained rapid popularity through publicly available tools such as ChatGPT. The adoption of LLMs for personal and professional use is fueled by the natural interactions between human users and computer applications such as ChatGPT, along with powerful summarization and text generation capabilities. Given the widespread use of such generative AI tools, in this work we investigate how these tools can be deployed in a non-safety critical, strategic traffic flow management setting. Specifically, we train an LLM, CHATATC, based on a large historical data set of Ground Delay Program (GDP) issuances, spanning 2000-2023 and consisting of over 80,000 GDP implementations, revisions, and cancellations. We test the query and response capabilities of CHATATC, documenting successes (e.g., providing correct GDP rates, durations, and reason) and shortcomings (e.g,. superlative questions). We also detail the design of a graphical user interface for future users to interact and collaborate with the CHATATC conversational agent. △ Less

Submitted 23 July, 2024; v1 submitted 19 February, 2024; originally announced February 2024.

Comments: 8 pages, 5 figures; minor revisions to address reviewer feedback for final submission to the 11th International Conference on Research in Air Transportation (ICRAT)

User Feedback-Informed Interface Design for Flow Management Data and Services (FMDS)

Authors: Sinan Abdulhak, Anthony Carvette, Kate Shen, Robert Goldman, Bill Tuck, Max Z. Li

Abstract: The transition to a microservices-based Flow Management Data and Services (FMDS) architecture from the existing Traffic Flow Management System (TFMS) is a critical enabler of the vision for an Information-Centric National Airspace System (NAS). The need to design a user-centric interface for FMDS is a key technical gap, as this interface connects NAS data and services to the traffic management spe… ▽ More The transition to a microservices-based Flow Management Data and Services (FMDS) architecture from the existing Traffic Flow Management System (TFMS) is a critical enabler of the vision for an Information-Centric National Airspace System (NAS). The need to design a user-centric interface for FMDS is a key technical gap, as this interface connects NAS data and services to the traffic management specialists within all stakeholder groups (e.g., FAA, airlines). We provide a research-driven approach towards designing such a graphical user interface (GUI) for FMDS. Major goals include unifying the more than 50 disparate traffic management services currently hosted on TFMS, as well as streamlining the process of evaluating, modeling, and monitoring Traffic Management Initiatives (TMIs). Motivated by this, we iteratively designed a GUI leveraging human factors engineering and user experience design principles, as well as user interviews. Through user testing and interviews, we identify workflow benefits of our GUI (e.g., reduction in task completion time), along with next steps for developing a live prototype. △ Less

Submitted 19 February, 2024; originally announced February 2024.

Comments: 8 pages, 8 figures

Distributionally Robust Ground Delay Programs with Learning-Driven Airport Capacity Predictions

Authors: Haochen Wu, Xinting Zhu, Shuchang Li, Ying Zhou, Lishuai Li, Max Z. Li

Abstract: Strategic Traffic Management Initiatives (TMIs) such as Ground Delay Programs (GDPs) play a crucial role in mitigating operational costs associated with demand-capacity imbalances. However, GDPs can only be planned (e.g., duration, delay assignments) with confidence if the future capacities at constrained resources (i.e., airports) are predictable. In reality, such future capacities are uncertain,… ▽ More Strategic Traffic Management Initiatives (TMIs) such as Ground Delay Programs (GDPs) play a crucial role in mitigating operational costs associated with demand-capacity imbalances. However, GDPs can only be planned (e.g., duration, delay assignments) with confidence if the future capacities at constrained resources (i.e., airports) are predictable. In reality, such future capacities are uncertain, and predictive models may provide forecasts that are vulnerable to errors and distribution shifts. Motivated by the goal of planning optimal GDPs that are \emph{distributionally robust} against airport capacity prediction errors, we study a fully integrated learning-driven optimization framework. We design a deep learning-based prediction model capable of forecasting arrival and departure capacity distributions across a network of airports. We then integrate the forecasts into a distributionally robust formulation of the multi-airport ground holding problem (\textsc{dr-MAGHP}). We show how \textsc{dr-MAGHP} can outperform stochastic optimization when distribution shifts occur, and conclude with future research directions to improve both the learning and optimization stages. △ Less

Submitted 17 February, 2024; originally announced February 2024.

Comments: 8 pages, 6 figures

Spaceport Facility Location Planning within the US National Airspace System

Authors: Haochen Wu, Kevin R. Sun, Jackson A. Miller, Oliver Jia-Richards, Max Z. Li

Abstract: The burgeoning commercial space transportation industry necessitates an expansion of launch infrastructure to meet rising demands. However, future operations from these large-scale infrastructures can result in new impacts, particularly to air traffic operations. To rigorously reason about where such future spaceports might be located and what their impacts might be, we introduce a facility locati… ▽ More The burgeoning commercial space transportation industry necessitates an expansion of launch infrastructure to meet rising demands. However, future operations from these large-scale infrastructures can result in new impacts, particularly to air traffic operations. To rigorously reason about where such future spaceports might be located and what their impacts might be, we introduce a facility location planning model for future US spaceports (SPFLP). Central considerations for the SPFLP include population density, space launch trajectories, and potential impacts to air traffic within the US National Airspace System (NAS). The SPFLP outputs a cost-optimal set of candidate locations for future spaceports while satisfying a range of operational constraints. By conducting sensitivity analyses on the SPFLP, we are able to examine differences in flight rerouting costs and optimal launch mission allocations. Our model and numerical experiments offer valuable insights for future spaceport site selection, contributing to the strategic development of commercial space transportation while keeping in mind the need to integrate these operations within the NAS. △ Less

Submitted 17 February, 2024; originally announced February 2024.

Comments: 10 pages, 5 figures

Spatial Correlation at the Boson Peak Frequency in Amorphous Materials

Authors: X. Y. Li, H. P. Zhang, S. Lan, D. L. Abernathy, C. H. Hu, L. R. Fan, M. Z. Li, X. -L. Wang

Abstract: The Boson peak (BP), an excess of vibrational density of states, is ubiquitous for amorphous materials and is believed to hold the key to understanding the dynamics of glass and glass transition. Previous studies have established an energy scale for the BP, which is ~1-10 meV or ~THz in frequency. However, so far, little is known about the momentum dependence or spatial correlation of the BP. Here… ▽ More The Boson peak (BP), an excess of vibrational density of states, is ubiquitous for amorphous materials and is believed to hold the key to understanding the dynamics of glass and glass transition. Previous studies have established an energy scale for the BP, which is ~1-10 meV or ~THz in frequency. However, so far, little is known about the momentum dependence or spatial correlation of the BP. Here, we report the observation of the BP in model Zr-Cu-Al metallic glasses over a wide range of momentum transfer, using inelastic neutron scattering, heat capacity, Raman scattering measurements, and molecular dynamics (MD) simulations. The BP energy is largely dispersionless; however, the BP intensity was found to scale with the static structure factor. Additional MD simulations with a generic Lennard-Jones potential confirmed the same. Based on these results, an analytical expression for the dynamic structure factor was formulated for the BP excitation. Further analysis of the simulated disordered structures suggests that the BP is related to local structure fluctuations (e.g., in shear strain). Our results offered insights into the nature of the BP and provide guidance for the development of theories of amorphous materials. △ Less

Submitted 6 November, 2023; originally announced November 2023.

Comments: 19 pages, 4 figures,

Distributionally Robust Airport Ground Holding Problem under Wasserstein Ambiguity Sets

Authors: Haochen Wu, Alexander S. Estes, Max Z. Li

Abstract: Ground Delay Programs (GDPs) mitigate demand-capacity imbalances by holding flights on the ground when an airport's arrival capacity is reduced, thereby reducing costly airborne holding. A central challenge is that day-to-day demand-capacity balancing relies on accurate predictions of airport capacities. However, these predictions are deeply uncertain: forecast errors, operational disruptions, and… ▽ More Ground Delay Programs (GDPs) mitigate demand-capacity imbalances by holding flights on the ground when an airport's arrival capacity is reduced, thereby reducing costly airborne holding. A central challenge is that day-to-day demand-capacity balancing relies on accurate predictions of airport capacities. However, these predictions are deeply uncertain: forecast errors, operational disruptions, and climate change-driven shifts in weather severity can induce distribution shifts in capacity outcomes. Thus, policies optimized for a single predicted distribution may be brittle out of sample. We address this challenge by developing a \emph{distributionally robust} framework for the single airport ground holding problem (dr-SAGHP). We also propose a method integrates Kelly's cutting plane method with the integer L-shaped method, and that is applicable more broadly to two-stage distributionally robust integer programs with relatively complete recourse and continuous second-stage decision variables. Our method includes a novel dual bisection and primal recovery algorithm that makes use of the structure of the distributionally robust integer program in order to quickly generate subgradients required by Kelly's cutting plane method. In computational experiments, our proposed algorithm delivers up to two orders-of-magnitude speedups compared to solving the convex reformulation directly, while maintaining negligible optimality gaps. We generate capacity scenarios via Gaussian process regression and evaluate out-of-sample performance by perturbing the posterior mean and variance. The numerical experiment results show that dr-SAGHP delivers significant out-of-sample gains under moderate-to-severe shifts, improving the resilience and effectiveness of GDP decision-making under capacity uncertainty. △ Less

Submitted 5 March, 2026; v1 submitted 16 June, 2023; originally announced June 2023.

Comments: 31 pages, 13 figures

Remote Identification Trajectory Coverage in Urban Air Mobility Applications

Authors: Hejun Huang, Billy Mazotti, Joseph Kim, Max Z. Li

Abstract: As Urban Air Mobility (UAM) and Advanced Air Mobility (AAM) continue to mature, a safety-critical system that will need to be implemented in tandem is Remote Identification (Remote ID) for uncrewed aircraft systems (UAS). To ensure successful and efficient deployment (e.g., maximal surveillance of UAS trajectories), as well as to better understand secondary impacts (e.g., consumer privacy risks in… ▽ More As Urban Air Mobility (UAM) and Advanced Air Mobility (AAM) continue to mature, a safety-critical system that will need to be implemented in tandem is Remote Identification (Remote ID) for uncrewed aircraft systems (UAS). To ensure successful and efficient deployment (e.g., maximal surveillance of UAS trajectories), as well as to better understand secondary impacts (e.g., consumer privacy risks in collecting real-time UAS trajectory information), the coverage of broadcast-receive Remote ID architectures needs to be characterized. Motivated by this need, we examine theoretical and empirical trajectory coverage of several common Remote ID technologies (e.g., Bluetooth, Wi-Fi) deployed for urban package delivery missions, a commonly-cited use case for UAM and AAM. We derive methods to explicitly compute expected coverage proportions under idealized geometries, as well as conduct case studies with realistic city geographies and UAS path planning algorithms. An example of results include approximate magnitudes of Remote ID receivers needed (approximately 500-5000 receivers needed to achieve 50-95\% coverage for Bluetooth Legacy, and approximately 10-40 receivers needed for the same coverage range for Wi-Fi NAN/Beacon, assuming a cruise altitude of 200 feet) to achieve specific trajectory coverage proportions for San Francisco, California. Our analyses, combined with complementary works related to Remote ID bandwidth and deployment topologies, can help guide municipal authorities and AAM stakeholders in future Remote ID system deployments and upkeep. △ Less

Submitted 19 February, 2023; originally announced February 2023.

Comments: 10 pages, 10 figures, conference paper for 2023-ATM-Seminar

Atomic structural characteristics and dynamical properties in monatomic metallic liquids via molecular dynamics simulations

Authors: X. Qin, J. Q. Wu, M. Z. Li

Abstract: Molecular dynamics simulations were performed for five monatomic metallic liquids and the atomic structural characteristics and dynamical properties were systematically investigated and compared for understanding the underlying structural basis of liquid properties, such as glass-forming ability. All simulated monatomic liquids exhibit similar structural characteristics and temperature evolution.… ▽ More Molecular dynamics simulations were performed for five monatomic metallic liquids and the atomic structural characteristics and dynamical properties were systematically investigated and compared for understanding the underlying structural basis of liquid properties, such as glass-forming ability. All simulated monatomic liquids exhibit similar structural characteristics and temperature evolution. However, the degree varies significantly in different liquids. It is found that the atomic structures in liquid Cu, Ta, and Fe are quite similar. However, liquid Ta exhibits more ordered and more densely packed structure features, with more populated icosahedral-like atomic ordering and five-fold HA indexes, less Voronoi entropy, and more regular tetrahedral configurations. Moreover, the increase of crystal-like clusters in liquid Ta slows down with decreasing temperature, and the icosahedral-like clusters increase more quickly, exceeding crystal-like ones, in contrast to other liquids. On the contrary, the atomic structures of liquid Al and Zr are more similar, much more disordered and more loosely packed. Furthermore, liquid Ta exhibits more slow and heterogeneous dynamics, which could be facilitated by the particular atomic structures of liquid Ta. Both the atomic structure and dynamics features in liquid Ta favor its glass formation. Our findings provide comprehensive structural and dynamical information for better understanding GFA and crystallization in these typical monatomic liquids. △ Less

Submitted 4 April, 2022; originally announced April 2022.

Routing with Privacy for Drone Package Delivery Systems

Authors: Geoffrey Ding, Alex Berke, Karthik Gopalakrishnan, Kwassi H. Degue, Hamsa Balakrishnan, Max Z. Li

Abstract: Unmanned aerial vehicles (UAVs), or drones, are increasingly being used to deliver goods from vendors to customers. To safely conduct these operations at scale, drones are required to broadcast position information as codified in remote identification (remote ID) regulations. However, location broadcast of package delivery drones introduces a privacy risk for customers using these delivery service… ▽ More Unmanned aerial vehicles (UAVs), or drones, are increasingly being used to deliver goods from vendors to customers. To safely conduct these operations at scale, drones are required to broadcast position information as codified in remote identification (remote ID) regulations. However, location broadcast of package delivery drones introduces a privacy risk for customers using these delivery services: Third-party observers may leverage broadcast drone trajectories to link customers with their purchases, potentially resulting in a wide range of privacy risks. We propose a probabilistic definition of privacy risk based on the likelihood of associating a customer to a vendor given a package delivery route. Next, we quantify these risks, enabling drone operators to assess privacy risks when planning delivery routes. We then evaluate the impacts of various factors (e.g., drone capacity) on privacy and consider the trade-offs between privacy and delivery wait times. Finally, we propose heuristics for generating routes with privacy guarantees to avoid exhaustive enumeration of all possible routes and evaluate their performance on several realistic delivery scenarios. △ Less

Submitted 29 June, 2022; v1 submitted 4 March, 2022; originally announced March 2022.

Journal ref: International Conference on Research in Air Transportation (ICRAT) 2022

Automatic Clustering for Unsupervised Risk Diagnosis of Vehicle Driving for Smart Road

Authors: Xiupeng Shi, Yiik Diew Wong, Chen Chai, Michael Zhi-Feng Li, Tianyi Chen, Zeng Zeng

Abstract: Early risk diagnosis and driving anomaly detection from vehicle stream are of great benefits in a range of advanced solutions towards Smart Road and crash prevention, although there are intrinsic challenges, especially lack of ground truth, definition of multiple risk exposures. This study proposes a domain-specific automatic clustering (termed Autocluster) to self-learn the optimal models for uns… ▽ More Early risk diagnosis and driving anomaly detection from vehicle stream are of great benefits in a range of advanced solutions towards Smart Road and crash prevention, although there are intrinsic challenges, especially lack of ground truth, definition of multiple risk exposures. This study proposes a domain-specific automatic clustering (termed Autocluster) to self-learn the optimal models for unsupervised risk assessment, which integrates key steps of risk clustering into an auto-optimisable pipeline, including feature and algorithm selection, hyperparameter auto-tuning. Firstly, based on surrogate conflict measures, indicator-guided feature extraction is conducted to construct temporal-spatial and kinematical risk features. Then we develop an elimination-based model reliance importance (EMRI) method to unsupervised-select the useful features. Secondly, we propose balanced Silhouette Index (bSI) to evaluate the internal quality of imbalanced clustering. A loss function is designed that considers the clustering performance in terms of internal quality, inter-cluster variation, and model stability. Thirdly, based on Bayesian optimisation, the algorithm selection and hyperparameter auto-tuning are self-learned to generate the best clustering partitions. Various algorithms are comprehensively investigated. Herein, NGSIM vehicle trajectory data is used for test-bedding. Findings show that Autocluster is reliable and promising to diagnose multiple distinct risk exposures inherent to generalised driving behaviour. Besides, we also delve into risk clustering, such as, algorithms heterogeneity, Silhouette analysis, hierarchical clustering flows, etc. Meanwhile, the Autocluster is also a method for unsupervised multi-risk data labelling and indicator threshold calibration. Furthermore, Autocluster is useful to tackle the challenges in imbalanced clustering without ground truth or priori knowledge △ Less

Submitted 28 September, 2024; v1 submitted 24 November, 2020; originally announced November 2020.

Orbital competition of Mn3+ and V3+ ions in Mn1+xV2-xO4

Authors: J. L. Jiao, H. P. Zhang, Q. Huang, W. Wang, G. Wang, Q. Ren, R. Sinclair, G. T. Lin, A. Huq, H. Cao, H. D. Zhou, M. Z. Li, J. Ma

Abstract: The structure and magnetic properties of Mn1+xV2-xO4 (0<x<=1), a complex frustrated system, were investigated by heat capacity, magnetization, x-ray diffraction, and neutron diffraction measurements. For x<=0.3, a cubic-to-tetragonal (c > a) phase transition was observed. For x > 0.3, the system maintained the tetragonal lattice. The collinear and noncollinear magnetic transition was also observed… ▽ More The structure and magnetic properties of Mn1+xV2-xO4 (0<x<=1), a complex frustrated system, were investigated by heat capacity, magnetization, x-ray diffraction, and neutron diffraction measurements. For x<=0.3, a cubic-to-tetragonal (c > a) phase transition was observed. For x > 0.3, the system maintained the tetragonal lattice. The collinear and noncollinear magnetic transition was also observed for all compositions. To reveal the dynamics of the ground state, first principle simulation was applied to not only analyze the orbital effects of Mn2+, Mn3+, and V3+ ions, but also the related exchange energies. △ Less

Submitted 6 October, 2020; originally announced October 2020.

Optimization of the JUNO liquid scintillator composition using a Daya Bay antineutrino detector

Authors: Daya Bay, JUNO collaborations, :, A. Abusleme, T. Adam, S. Ahmad, S. Aiello, M. Akram, N. Ali, F. P. An, G. P. An, Q. An, G. Andronico, N. Anfimov, V. Antonelli, T. Antoshkina, B. Asavapibhop, J. P. A. M. de André, A. Babic, A. B. Balantekin, W. Baldini, M. Baldoncini, H. R. Band, A. Barresi, E. Baussan , et al. (642 additional authors not shown)

Abstract: To maximize the light yield of the liquid scintillator (LS) for the Jiangmen Underground Neutrino Observatory (JUNO), a 20 t LS sample was produced in a pilot plant at Daya Bay. The optical properties of the new LS in various compositions were studied by replacing the gadolinium-loaded LS in one antineutrino detector. The concentrations of the fluor, PPO, and the wavelength shifter, bis-MSB, were… ▽ More To maximize the light yield of the liquid scintillator (LS) for the Jiangmen Underground Neutrino Observatory (JUNO), a 20 t LS sample was produced in a pilot plant at Daya Bay. The optical properties of the new LS in various compositions were studied by replacing the gadolinium-loaded LS in one antineutrino detector. The concentrations of the fluor, PPO, and the wavelength shifter, bis-MSB, were increased in 12 steps from 0.5 g/L and <0.01 mg/L to 4 g/L and 13 mg/L, respectively. The numbers of total detected photoelectrons suggest that, with the optically purified solvent, the bis-MSB concentration does not need to be more than 4 mg/L. To bridge the one order of magnitude in the detector size difference between Daya Bay and JUNO, the Daya Bay data were used to tune the parameters of a newly developed optical model. Then, the model and tuned parameters were used in the JUNO simulation. This enabled to determine the optimal composition for the JUNO LS: purified solvent LAB with 2.5 g/L PPO, and 1 to 4 mg/L bis-MSB. △ Less

Submitted 1 July, 2020; originally announced July 2020.

Comments: 13 pages, 8 figures

Intrinsic structural features in jammed disordered packing of monodisperse spheres

Authors: S. Q. Jiang, M. Z. Li

Abstract: A new tetrahedral structure model was developed and the geometrical structure of jammed disordered packings of monodisperse spheres with different friction coefficients was systematically characterized. An intrinsic structure feature is revealed for all jammed disordered packings, which is determined only by the packing fraction, independent of interparticle friction. Moreover, the structural conf… ▽ More A new tetrahedral structure model was developed and the geometrical structure of jammed disordered packings of monodisperse spheres with different friction coefficients was systematically characterized. An intrinsic structure feature is revealed for all jammed disordered packings, which is determined only by the packing fraction, independent of interparticle friction. Moreover, the structural configurations associated with crystal phases are more prevalent than the geometric frustrated ones. In addition, irregular simplexes are found to play a key role in an increase in density, rather than regular simplexes, in contrast to previous expectation. Our model provides an intrinsic structural basis for the random close packing. △ Less

Submitted 4 October, 2020; v1 submitted 2 March, 2019; originally announced March 2019.

Comments: 25 pages, 10 figures

Critical scaling of icosahedral medium-range order in CuZr metallic glass-forming liquids

Authors: Z. W. Wu, F. X. Li, C. W. Huo, M. Z. Li, W. H. Wang, K. X. Liu

Abstract: The temperature evolution of icosahedral medium-range order formed by interpenetrating icosahedra in CuZr metallic glass-forming liquids was investigated via molecular dynamics simulations. Scaling analysis based on percolation theory was employed, and it is found that the size distribution of clusters formed by the central atoms of icosahedra at various temperatures follows a very good scaling la… ▽ More The temperature evolution of icosahedral medium-range order formed by interpenetrating icosahedra in CuZr metallic glass-forming liquids was investigated via molecular dynamics simulations. Scaling analysis based on percolation theory was employed, and it is found that the size distribution of clusters formed by the central atoms of icosahedra at various temperatures follows a very good scaling law with the cluster number density scaled by $S^{-τ}$ and the cluster size $S$ scaled by $|1-T_c/T|^{-1/σ}$, respectively. Here $T_c$ is scaling crossover-temperature. $τ$ and $σ$ are scaling exponents. The critical scaling behaviour suggests that there would be a structural phase transition manifested by percolation of locally favoured structures underlying the glass transition, if the liquid could be cooled slowly enough but without crystallization intervening. Furthermore, it is revealed that when icosahedral short-range order (ISRO) extends to medium-range length scale by connection, the atomic configurations of ISROs will be optimized from distorted ones towards more regular ones gradually, which significantly lowers the energies of ISROs and introduces geometric frustration simultaneously. Both factors make key impacts on the drastic dynamic slowdown of supercooled liquids. Our findings provide direct structure-property relationship for understanding the nature of glass transition. △ Less

Submitted 19 October, 2016; v1 submitted 18 May, 2015; originally announced May 2015.

Comments: 8 pages, 4 figures; Renamed, Massive Revisions, Scientific Reports Accepted