Urban Air Mobility (UAM): Airport Shuttles or City-Taxis?
AAM & Mobility Systems Paper analysis
A clear explanation of why Milan survey evidence suggests airport shuttle routes may be a stronger early Urban Air Mobility market than city t...
Research notes & perspectives
Research Notes & Perspectives
Research notes on how data-driven and neurosymbolic AI can support mobility demand modeling, infrastructure planning, and operational decisions. Browse by research area or by article format.
Posts are organized by research area and article format. A post may appear in more than one research area when it connects methods with mobility applications.
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Urban Air Mobility Demand Forecasting: Modeling Evidence from the Case Study of Milan (Italy)
AAM & Mobility Systems Machine Learning & Modeling Paper analysis
Explore Milan UAM demand forecasts for airport shuttles, intercity services, air taxis, vertiport scenarios, fares, and traveler preferences.
Enhancing AI's Reasoning with Active Retrieval and Monte Carlo Tree Search
Neurosymbolic & Trustworthy AI Paper analysis
A clear explanation of AR-MCTS, a framework that improves multimodal reasoning by combining active retrieval, Monte Carlo Tree Search, and pro...
Demystifying the Difference Between Explainable AI and Neurosymbolic AI
Neurosymbolic & Trustworthy AI Paper analysis
A practical explanation of how Explainable AI and Neurosymbolic AI differ, where they overlap, and why both matter for trustworthy systems.
Improving Pre-Disaster Air Mobility Planning with Neural Network-Accelerated Genetic Algorithms
AAM & Mobility Systems Machine Learning & Modeling Paper analysis
A practical explanation of how neural networks can accelerate genetic algorithms for pre-disaster air mobility evacuation planning.
Why Gravity Models Struggle to Capture Temporal Migration Dynamics
AAM & Mobility Systems Paper analysis
A clear explanation of why gravity models can fit spatial migration patterns well but fail to predict how migration changes over time.
Stochastic Origin-Destination Matrix Forecasting Using Dual-Stage Graph Convolutional, Recurrent Neural Networks
AAM & Mobility Systems Machine Learning & Modeling Paper analysis
A clear explanation of stochastic OD matrix forecasting with matrix factorization, graph convolutional networks, and recurrent neural networks.
A Demand Forecasting Model for Urban Air Mobility in Chengdu, China
AAM & Mobility Systems Machine Learning & Modeling Paper analysis
A readable walkthrough of a four-step UAM demand forecasting model for Chengdu, China, with a focus on modal split, shared operations, and 203...
A Data-Driven Method for OD Matrix Estimation
AAM & Mobility Systems Paper analysis
Explore data-driven origin-destination matrix estimation using traffic speed, flow, production-attraction patterns, routing, and PCA.
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27 articles
AAM & Mobility Systems
Demand modeling, regional and urban air mobility, infrastructure planning, and operational systems.
23 articles Neurosymbolic & Trustworthy AINeural-symbolic learning, interpretable reasoning, knowledge distillation, and explainable AI.
22 articles Machine Learning & ModelingForecasting, deep learning, decision models, graph methods, and data-driven analysis.
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