Air taxi service for urban mobility: A critical review of recent developments, future challenges, and opportunities
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Abstract (arXiv)
Expected to operate in the imminent future, air taxi service (ATS) is an aerial on-demand transport for a single passenger or a small group of riders, which seeks to transform the method of everyday commute. This uncharted territory in the emerging transportation world is anticipated to enable consumers bypass traffic congestion in urban road networks. By adopting an electric vertical takeoff and landing concept (eVTOL), air taxis could be operational from skyports retrofitted on building rooftops, thus gaining advantage from an implementation standpoint. Motivated by the potential impact of ATS, this study provides a review of air taxi systems and associated operations. We first discuss the current developments in the ATS (demand prediction, air taxi network design, and vehicle configuration). Next, we anticipate potential future challenges of ATS from an operations management perspective, and review the existing literature that could be leveraged to tackle these problems (ride-matching, pricing strategies, vehicle maintenance scheduling, and pilot training and recruitment). Finally, we detail future research opportunities in the air taxi domain.
Key facts. Cited by 7 papers in the map (data through 13 Oct 2026). First posted on arXiv 24 Feb 2021.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 2; 2024: 2; 2025: 1; 2026: 1.
7 papers.
- 2023Reaching the Limit in Autonomous Racing: Optimal Control versus Reinforcement Learningcited by 72
- 2021Predicting Demand for Air Taxi Urban Aviation Services using Machine Learning Algorithmscited by 3
- 2024Self-organized free-flight arrival for urban air mobilitycited by 2
- 2023Simulating the Integration of Urban Air Mobility into Existing Transportation Systems: A Surveycited by 1
- 2025Real-Time Communication-Aware Ride-Sharing Route Planning for Urban Air Mobility: A Multi-Source Hybrid Attention Reinforcement Learning Approachcited by 1
- 2024Generative Artificial Intelligence-Guided User Studies: An Application for Air Taxi Servicescited by 0
- 2026Heterogeneous Vertiport Selection Optimization for On-Demand Air Taxi Services: A Deep Reinforcement Learning Approachcited by 0
- 2026Heterogeneous Vertiport Selection Optimization for On-Demand Air Taxi Services: A Deep Reinforcement Learning Approach29 Jan 2026
- 2025Real-Time Communication-Aware Ride-Sharing Route Planning for Urban Air Mobility: A Multi-Source Hybrid Attention Reinforcement Learning Approach18 Jul 2025
- 2024Generative Artificial Intelligence-Guided User Studies: An Application for Air Taxi Services18 Jun 2024
- 2024Self-organized free-flight arrival for urban air mobility4 Apr 2024
- 2023Reaching the Limit in Autonomous Racing: Optimal Control versus Reinforcement Learning17 Oct 2023
- 2023Simulating the Integration of Urban Air Mobility into Existing Transportation Systems: A Survey25 Jan 2023
- 2021Predicting Demand for Air Taxi Urban Aviation Services using Machine Learning Algorithms26 Mar 2021