Neurosymbolic AI combines neural network adaptability with symbolic reasoning, promising an approach to address the complex regulatory, operational, and safety challenges in Advanced Air Mobility (AAM). This survey reviews its applications across key AAM domains such as demand forecasting, aircraft design, and real-time air traffic management. Our analysis reveals a fragmented research landscape where methodologies, including Neurosymbolic Reinforcement Learning, have shown potential for dynamic optimization but still face hurdles in scalability, robustness, and compliance with aviation standards. We classify current advancements, present relevant case studies, and outline future research directions aimed at integrating these approaches into reliable, transparent AAM systems. By linking advanced AI techniques with AAM’s operational demands, this work provides a concise roadmap for researchers and practitioners developing next-generation air mobility solutions.

Accurate travel demand prediction is essential for effective transportation planning, infrastructure development, and resource optimization. Traditional models often lack both interpretability and the ability to capture complex, nonlinear relationships across geospatial and socioeconomic variables. To address this, we propose a Neurosymbolic AI framework that integrates decision tree (DT) based symbolic reasoning with neural network (NN) learning for travel demand forecasting. The model leverages multisource data including geospatial, economic, and mobility datasets and embeds interpretable if-then rules extracted from DTs as additional features in the NN. Our experiments demonstrate that this hybrid approach improves predictive accuracy across key metrics, achieving up to 24.19% reduction in Mean Absolute Error (MAE), 1.79% improvement in R2, and 6.49% in Common Part of Commuters (CPC) compared to NN models. Rules extracted at finer variance thresholds (e.g., 0.0001) capture nuanced patterns and enhance model alignment with observed commuter behavior. By combining symbolic interpretability with neural generalization, the proposed method advances both the transparency and performance of travel demand modeling. The data and code can be accessed on GitHub.1

In recent years, the rapid pace of urbanization has posed profound challenges globally, exacerbating environmental concerns and escalating traffic congestion in metropolitan areas. To mitigate these issues, Advanced Air Mobility (AAM) has emerged as a promising transportation alternative. However, the effective implementation of AAM requires robust demand modeling. This study delves into the demand dynamics of AAM by analyzing employment based trip data across Tennessee’s census tracts, employing statistical techniques and machine learning models to enhance accuracy in demand forecasting. Drawing on datasets from the Bureau of Transportation Statistics (BTS), the Internal Revenue Service (IRS), the Federal Aviation Administration (FAA), and additional sources, we perform cost, time, and risk assessments to compute the Generalized Cost of Trip (GCT). Our findings indicate that trips are more likely to be viable for AAM if air transportation accounts for over 70% of the GCT and the journey spans more than 250 miles. The study not only refines the understanding of AAM demand but also guides strategic planning and policy formulation for sustainable urban mobility solutions. The data and code can be accessed on GitHub.1

Accurately predicting flight demand is essential for optimizing air travel operations and resource allocation. In our research, we explore the relationship between temporal patterns and flight demand, leveraging hourly data rather than traditional meteorological factors. Through analysis, we discovered significant correlations between hour of the day and flight demand, prompting the creation of features such as peak hours and time segments (morning, afternoon, evening). By utilizing these temporal features, we develop predictive models employing various machine learning algorithms, including LSTM, linear regression, and gradient boosting models. We aim to identify the most effective approach for accurately forecasting flight demand, with implications extending to the optimization of Advanced Air Mobility (AAM) solutions, where understanding temporal patterns is crucial for efficient resource allocation and urban air transportation network design.

In this article, we present a novel approach, namely distributed matching-by-clone hungarian-based algorithm (DMCHBA), to multiagent task-allocation problems, in which the number of agents is smaller than the number of tasks. The proposed DMCHBA assumes that agents employ an implicit coordination mechanism and consists of two iterative phases, i.e., the communication phase and the assignment phase. In the communication phase, agents communicate with their connected neighbors and exchange their local knowledge base until they converge on the global knowledge base. In the assignment phase, each agent builds a squared cost matrix by cloning agents and adding pseudotasks when necessary, and applying the Hungarian method for task allocation. A local planning algorithm is then applied to identify the order of task execution for an agent. The proposed DMCHBA is proven to produce conflict-free assignments among agents in finite time. We compare the performance of DMCHBA with the consensus-based bundle algorithm, the distributed recursive Hungarian-based algorithms, and the cluster-based Hungarian algorithm (CBHA) in Monte-Carlo simulations with different numbers of agents and tasks. The numerical results reveal the superior convergence and optimality of DMCHBA over all other selected algorithms.

This paper reports a novel optical localization method, including both the hardware design and algorithm design, to track mobile Unmanned Aerial Vehicles (UAVs). The method relies on a circle-shaped blinking LED marker installed on the UAV and uses a single Dynamic Vision Sensing (DVS) camera to sense the temporal difference of the video streams. A temporal-filtering algorithm processes the video stream and detects the target marker by filtering out the background image. The triangulation-based spatial localization algorithm captures the trace of the target with the help of the prior knowledge of the physical size of the marker. The proposed system was evaluated in flight tests and compared with ground truth data provided by a motion capture system. The proposed system provides a simple and accurate localization solution for UAV tracking with a low computing overhead.

In this paper, we present a cooperative target-centric formation control strategy that maintains the dynamic graph connectivity for a system of unmanned aerial vehicles. The connectivity of a graph plays a critical role in dynamic networks of multiple agents since it represents a level of information sharing capability of a system. The connectivity of a network of unmanned systems changes as the state dependent graph evolves over time, revealing the risk of the system being uncontrollable during null connectivity. We show that the existing formation controller fails to make the formation when the network connectivity is lost. We propose a novel formation control strategy which keeps the multi-agent graph connected throughout the system dynamic process. The convergence of the formation controller is shown with the help of the Lyapunov theory. Simulation results validate the effectiveness of the proposed control law.