FlightSense: An End-to-End MLOps Platform for Real-Time Flight Delay Prediction via Rotation-Chain Propagation Features and Agentic Conversational AI

• URL: http://arxiv.org/abs/2605.07364v1
• Date: Fri, 08 May 2026 07:21:00 GMT
• Title: FlightSense: An End-to-End MLOps Platform for Real-Time Flight Delay Prediction via Rotation-Chain Propagation Features and Agentic Conversational AI
• Abstract summary: This paper presents an end-to-end MLOps platform for real-time flight delay prediction built through a progressive three-version feature engineering framework.<n>FlightSense is deployed as a production AWS MLOps pipeline incorporating live weather ingestion via Lambda, real-time SageMaker inference, an interactive Streamlit dashboard, and an Amazon Bedrock Nova Micro conversational assistant answering natural-language delay queries via a tool-use architecture.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Flight delays impose cascading operational and financial burdens across the aviation network, costing the U.S. economy billions of dollars annually by disrupting interconnected aircraft rotation systems. While prior machine learning approaches have demonstrated strong predictive performance, most treat upstream delays as static input variables rather than explicitly modeling how delays propagate dynamically through aircraft rotation chains, and none have deployed such systems alongside a live weather-aware conversational AI interface for end-user interaction. This paper presents FlightSense, an end-to-end MLOps platform for real-time flight delay prediction built through a progressive three-version feature engineering framework. Version 1 trains an XGBoost classifier on 11 schedule-based features establishing a baseline ROC AUC of 0.732 on 7.07 million BTS 2018 On-Time Performance records. Version 2 introduces 11 delay propagation features derived from aircraft rotation chains via tail-number tracking, yielding the dominant performance gain (AUC 0.732 to 0.875) and surpassing the single-stage XGBoost baseline reported by Zhou (2025). Version 3 integrates five NOAA meteorological features across 10 major U.S. airports, achieving a final test set AUC of 0.879. FlightSense is deployed as a production AWS MLOps pipeline incorporating live weather ingestion via Lambda, real-time SageMaker inference, an interactive Streamlit dashboard, and an Amazon Bedrock Nova Micro conversational assistant answering natural-language delay queries via a tool-use architecture.

Related papers

• Predicting Train Delays in Finland Using Machine Learning and Weather Data [2.4788097726838667]
  Adverse weather, particularly in Arctic regions with extreme temperatures and heavy precipitation, remains a leading cause of train delays.<n>This paper investigates machine learning for train delay prediction using the Finland Integrated Train-Weather dataset.
  arXiv  Detail & Related papers  (2026-09-10T09:08:43Z)
• Aero-World: Action-Conditioned Aerial Video Generation from Inertial Controls [57.166020875486474]
  We propose textbfAero-World, a method for converting a pretrained image-to-video diffusion model into a controllable aerial video generator.<n>A frozen latent-space Physics Probe, trained independently on real video--IMU pairs, provides differentiable inertial-consistency supervision during LoRA finetuning.<n>On AeroBench, Aero-World improves mean AAS from 57.7 to 63.6 over action-only finetuning and gives a stronger quality-control trade-off than AirScape.
  arXiv  Detail & Related papers  (2026-05-19T12:02:17Z)
• ASCENT: Transformer-Based Aircraft Trajectory Prediction in Non-Towered Terminal Airspace [54.94692733670454]
  ASCENT is a lightweight transformer-based model for multi-modal 3D aircraft trajectory forecasting.<n>It integrates domain-aware 3D coordinate normalization and parameterized predictions.
  arXiv  Detail & Related papers  (2026-03-17T14:16:10Z)
• When RL Meets Adaptive Speculative Training: A Unified Training-Serving System [71.98182665273575]
  We present Aurora, a unified training-serving system that closes the loop by continuously learning a speculator directly from live inference traces.<n>Our design integrates an SGLang-based inference server with an asynchronous training server, enabling hot-swapped speculator updates without service interruption.
  arXiv  Detail & Related papers  (2026-02-06T18:28:54Z)
• LiQSS: Post-Transformer Linear Quantum-Inspired State-Space Tensor Networks for Real-Time 6G [85.58816960936069]
  Proactive and agentic control in Sixth-Generation (6G) Open Radio Access Networks (O-RAN) requires control-grade prediction under stringent Near-Time (Near-RT) latency and computational constraints.<n>This paper investigates a post-Transformer paradigm for efficient radio telemetry forecasting.<n>We propose a quantum-inspired state-space tensor network that replaces self-attention with stable structured state-space dynamics kernels.
  arXiv  Detail & Related papers  (2026-01-18T12:08:38Z)
• Aeolus: A Multi-structural Flight Delay Dataset [41.17078926798434]
  Aeolus is a large-scale Multi-modal Flight Delay dataset.<n>It is designed to advance research on flight delay prediction.<n>Aeolus supports a broad range of tasks, including regression, classification, temporal structure modeling, and graph learning.
  arXiv  Detail & Related papers  (2025-10-30T15:41:43Z)
• Flight Delay Prediction via Cross-Modality Adaptation of Large Language Models and Aircraft Trajectory Representation [1.7434507809930746]
  This paper presents a lightweight large language model-based multimodal flight delay prediction.<n>The approach integrates trajectory representations with textual aeronautical information, including flight information, weather reports, and aerodrome notices.<n>Experiments show that the model consistently achieves sub-minute prediction error.<n>The framework demonstrates that linguistic understanding, when combined with cross-modality adaptation of trajectory data, enhances delay prediction.
  arXiv  Detail & Related papers  (2025-10-24T12:21:27Z)
• Efficient Real-Time Aircraft ETA Prediction via Feature Tokenization Transformer [17.277512318874457]
  Estimated time of arrival (ETA) for airborne aircraft in real-time is crucial for arrival management in aviation.<n>In this study, we utilize a feature tokenization-based Transformer model to efficiently predict aircraft ETA.<n>With a data sampling rate of 1HZ, the ETA prediction is updated every second.
  arXiv  Detail & Related papers  (2025-07-31T03:56:30Z)
• FengWu: Pushing the Skillful Global Medium-range Weather Forecast beyond 10 Days Lead [93.67314652898547]
  We present FengWu, an advanced data-driven global medium-range weather forecast system based on Artificial Intelligence (AI) FengWu is able to accurately reproduce the atmospheric dynamics and predict the future land and atmosphere states at 37 vertical levels on a 0.25deg latitude-longitude resolution. The results suggest that FengWu can significantly improve the forecast skill and extend the skillful global medium-range weather forecast out to 10.75 days lead.
  arXiv  Detail & Related papers  (2023-04-06T09:16:39Z)
• Neural-Fly Enables Rapid Learning for Agile Flight in Strong Winds [96.74836678572582]
  We present a learning-based approach that allows rapid online adaptation by incorporating pretrained representations through deep learning. Neural-Fly achieves precise flight control with substantially smaller tracking error than state-of-the-art nonlinear and adaptive controllers.
  arXiv  Detail & Related papers  (2022-05-13T21:55:28Z)
• Spatio-Temporal Data Mining for Aviation Delay Prediction [15.621546618044173]
  We present a novel aviation delay prediction system based on stacked Long Short-Term Memory (LSTM) networks for commercial flights. The system learns from historical trajectories from automatic dependent surveillance-broadcast (ADS-B) messages. Compared with previous schemes, our approach is demonstrated to be more robust and accurate for large hub airports.
  arXiv  Detail & Related papers  (2021-03-20T18:37:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.