Urban Air Mobility (UAM): Airport Shuttles or City-Taxis?
A clear explanation of why Milan survey evidence suggests airport shuttle routes may be a stronger early Urban Air Mobility market than city taxi services.
Ph.D. Candidate in Information Systems, UMBC
Blog Topic
Articles on Advanced Air Mobility, regional air mobility, air taxi demand, OD estimation, infrastructure planning, and transportation forecasting.
A clear explanation of why Milan survey evidence suggests airport shuttle routes may be a stronger early Urban Air Mobility market than city taxi services.
A clear look at Milan's UAM demand forecasting study, including airport shuttles, intercity services, air taxis, vertiport scenarios, fares, and traveler preferences.
A practical explanation of how neural networks can accelerate genetic algorithms for pre-disaster air mobility evacuation planning.
A clear explanation of why gravity models can fit spatial migration patterns well but fail to predict how migration changes over time.
A clear explanation of stochastic OD matrix forecasting with matrix factorization, graph convolutional networks, and recurrent neural networks.
A readable walkthrough of a four-step UAM demand forecasting model for Chengdu, China, with a focus on modal split, shared operations, and 2030 demand.
A clear introduction to the gravity model in transportation planning, including trip distribution, productions, attractions, impedance, calibration, uses, and limitations.
A clear explanation of why traffic origin-destination flow estimation is difficult, especially in large congested networks with limited observations.
A practical look at a Jakarta UAM case study that estimates when airport travelers may prefer air mobility over ground transportation.
A practical explanation of a San Francisco Bay Area UAM demand method that compares multimodal air trips with road commutes under different congestion and vertiport scenarios.
A clear look at how researchers evaluated UAM landing-site feasibility, commuter demand, and fare levels in the Greater Northern California region.
A clear walkthrough of how researchers estimated the demand and market potential for AAM airport shuttle and air taxi services in the United States.
A clear look at how business travel demand can be modeled as a potential early market for Advanced Air Mobility in Hamburg.
A practical walkthrough of a New York City AAM simulation framework that connects demand modeling, scheduling, fleet size, vertiport capacity, and passenger delay.
A practical introduction to Advanced Air Mobility, including urban air taxis, regional mobility, cargo, public services, infrastructure, autonomy, and deployment challenges.
A practical walkthrough of how researchers estimate future demand for advanced regional air mobility in the U.S. Northeast Corridor.
A clear guide to Tennessee's metropolitan, micropolitan, and combined statistical areas, and why these county-based regions matter for planning and mobility analysis.
A practical comparison of Urban Air Mobility and Regional Air Mobility, including aircraft, infrastructure, markets, costs, and how the two systems may work together.
A clear introduction to Regional Air Mobility, why it matters for 50-500 mile trips, and how local airports could become useful transportation hubs again.
A practical look at how machine learning can classify air taxi demand across locations and time periods using ride and weather data.
A readable look at how NYC taxi data and constrained clustering can help planners decide where early air taxi vertistops should go.
A readable explanation of using support vector regression to forecast final airline bookings from early pre-sale booking patterns.
A readable explanation of using LSTM, GRU, and Transformer models to estimate future Urban Air Mobility demand from taxi-trip data.
A practical explanation of using Temporal Fusion Transformers to improve multi-horizon airport departure demand forecasting.
A readable explanation of using seq2seq and attention-based deep learning models to forecast airport departure demand for strategic planning.