Air taxi skyport location problem with single-allocation choice-constrained elastic demand for airport access

Rath, Srushti; Chow, Joseph Y.J.

doi:10.1016/j.jairtraman.2022.102294

Cited by 22 publications

(10 citation statements)

References 56 publications

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“…Mathematical model A mathematical optimization model has been developed to tackle the challenges outlined above and determine the optimal locations for UAM airports. This model is widely used to identify the best locations for various facilities, including airports (see Hammad et al (2017), Rath and Chow (2022)) and taxi skyports.…”

Section: System Constraintsmentioning

confidence: 99%

“…(2018) used commute data but employed a mixed-integer linear programming approach. Alternatively, Rath and Chow (2019) and Willey and Salomon (2021) used heuristic methods and treated the problem as a modified single-allocation p-hub median location problem.…”

Section: Related Studiesmentioning

confidence: 99%

“…This model is widely used to identify the best locations for various facilities, including airports (see Hammad et al. (2017), Rath and Chow (2022)) and taxi skyports.…”

Section: Mathematical Modelmentioning

confidence: 99%

“…Airport design and planning, demand forecasting, routing and revenue management are critical components of UAM service operation (Rath and Chow, 2022; Wu and Zhang, 2021; Rimjha et al ., 2021; Thu et al ., 2022). The quantitative research on UAM services in their operational aspects is lacking.…”

Section: Introductionmentioning

confidence: 99%

“…Airport locating is a critical factor to the performance of a UAM system. Therefore, a quantitative approach considering various factors such as stakeholder expectations (Merkisz-Guranowska et al ., 2016), trade-offs between trip length and cost (Rath and Chow, 2022) and commuting data for different travel paths (Lim and Hwang, 2019) is essential in determining the optimal locations. To support this decision-making process, the current study developed a multi-objective and multi-period (MOMP) location optimization model.…”

Section: Introductionmentioning

confidence: 99%

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The UAM service network: multi-objective and multi-period design for UAM airports

Boo,

Lee,

Song

2023

APJML

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PurposeThe next generation of mobility is arising, and various challenging mobilities have entered the limelight. One of the most exciting of these is urban air mobility (UAM), and one of its challenges is constructing effective and efficient UAM service network. This study took a quantitative approach to the problem in an effort to support and facilitate the UAM service industry.Design/methodology/approachThis study derived a multi-objective and multi-period (MOMP) location optimization model to support strategic UAM service network design. The model, based on its long-term service plan, determines where and when to open UAM airports. In addition, this study applied a modified e-constraint algorithm to derive managerial decisions on the Pareto relationship in consideration of multiple objectives and multiple periods.FindingsEach Pareto solution represents a different UAM service network configuration. Thus, the model can analyze the trade-offs between Pareto decisions for the UAM service network. A case study of UAM service network design in South Korea demonstrates the validity of the proposed mathematical model and algorithm.Practical implicationsThe design of a UAM service network should consider various aspects. Its construction and operation would require significant investments of time, capital and people, which would redound to society over a significant span of time. The results of this study provide quantitative guidelines for derivation and analysis of various UAM service network configurations in consideration of multiple objectives and multiple periods.Originality/valueThis paper proposes MOMP optimization, which approach is suitable to the fundamental characteristics of expanding UAM service networks and their design. It is expected that the present study will make significant contributions to the efforts of those deriving and analyzing future UAM service networks.

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Section: System Constraintsmentioning

confidence: 99%

Section: Related Studiesmentioning

confidence: 99%

“…This model is widely used to identify the best locations for various facilities, including airports (see Hammad et al. (2017), Rath and Chow (2022)) and taxi skyports.…”

Section: Mathematical Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The UAM service network: multi-objective and multi-period design for UAM airports

Boo,

Lee,

Song

2023

APJML

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On a fair and risk‐averse urban air mobility resource allocation problem under demand and capacity uncertainties

Sun,

Deng,

Wei

et al. 2024

Naval Research Logistics

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Urban air mobility (UAM) is an emerging air transportation mode to alleviate the ground traffic burden and achieve zero direct aviation emissions. Due to the potential economic scaling effects, the UAM traffic flow is expected to increase dramatically once implemented, and its market can be substantially large. To be prepared for the era of UAM, we study the fair and risk‐averse urban air mobility resource allocation model (FairUAM) under passenger demand and airspace capacity uncertainties for fair, safe, and efficient aircraft operations. FairUAM is a two‐stage model, where the first stage is the aircraft resource allocation, and the second stage is to fairly and efficiently assign the ground and airspace delays to each aircraft provided the realization of random airspace capacities and passenger demand. We show that FairUAM is NP‐hard even when there is no delay assignment decision or no aircraft allocation decision. Thus, we recast FairUAM as a mixed‐integer linear program (MILP) and explore model properties and strengthen the model formulation by developing multiple families of valid inequalities. The stronger formulation allows us to develop a customized exact decomposition algorithm with both benders and L‐shaped cuts, which significantly outperforms the off‐the‐shelf solvers. Finally, we numerically demonstrate the effectiveness of the proposed method and draw managerial insights when applying FairUAM to a real‐world network.

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Demand analysis in urban air mobility: A literature review

Long

Jiang

et al. 2023

Journal of Air Transport Management

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Air taxi skyport location problem with single-allocation choice-constrained elastic demand for airport access

Cited by 22 publications

References 56 publications

The UAM service network: multi-objective and multi-period design for UAM airports

The UAM service network: multi-objective and multi-period design for UAM airports

On a fair and risk‐averse urban air mobility resource allocation problem under demand and capacity uncertainties

Demand analysis in urban air mobility: A literature review

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