A traffic engineering model for air taxi services

Lee, Douglas W.; Bass, Ellen J.; Patek, Stephen D.; Boyd, J.

doi:10.1016/j.tre.2007.10.004

Cited by 9 publications

(7 citation statements)

References 9 publications

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“…In addition to the above-mentioned articles that have investigated the air taxi design, prior works have also focused on operational aspects of this aviation service. The weekly flow of passengers for a single provider was evaluated through a network formulation created by Lee et al. (2008) .…”

Section: Literature Reviewmentioning

confidence: 99%

Recommendations for emerging air taxi network operations based on online review analysis of helicopter services

Rajendran

Pagel

2020

Heliyon

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The effects of traffic congestion are adverse, primarily including air pollution, commuter stress, an increase in vehicle operating costs, and accidents on road. In efforts to alleviate these problems in metropolitan cities, logistics companies plan to introduce a new Urban Air Mobility (UAM) service called air taxis. These are electric-powered vehicles that would be tested and operated in the forthcoming years by international transportation companies like Airbus, Uber, and Kitty Hawk. Since these flying taxis are an emerging mode of transportation, it is necessary to provide recommendations for initial design, implementation, and operation. This study proposes managerial insights for these upcoming UAM services by analyzing online customer reviews and conducting an internal assessment of helicopter operations. Helicopters are similar to air taxis in regards to their vertical takeoff and landing (VTOL) operations, and therefore, customer reviews pertaining to the former can enable us to obtain insights into the strengths and weaknesses of the short-distance aviation service, in general. A four-stage sequential approach is used in this research, wherein online reviews are mined in Stage 1 , analyzed using bigram and trigram models in Stage 2 , 7S internal assessment is conducted for helicopter services in Stage 3 , and managerial recommendations for air taxis are proposed in Stage 4 . The insights obtained in this paper could assist any air taxi company in providing better customer service when they venture into the market.

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Section: Literature Reviewmentioning

confidence: 99%

Recommendations for emerging air taxi network operations based on online review analysis of helicopter services

Rajendran

Pagel

2020

Heliyon

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“…Yuan [9] constructed a taxi revenue function in the form of neuron s-characteristic function, introduced market parameters such as taxi market scale, waiting time, and price, discussed the supply-demand balance mechanism of taxi market, and constructed the taxi market optimization model with the goal of maximizing social welfare. Lee [10] used a set of hypothetical scenarios for air taxi network operation and evaluated the accuracy of the flow model against the outputs of the discrete-event model and illustrated how the flow model can be applied towards the optimal pricing of passenger fares for a single airport taxi service provider. Chen [11] proposed a multiview learning algorithm to extract a highly coupled and robust ship descriptor from multiple distinct ship feature sets and proposed a framework for integrating a multiview learning algorithm and a sparse representation method to track ships efficiently and effectively.…”

Section: Literature Reviewmentioning

confidence: 99%

“…us, the driver's decision-making judgment criterion can be obtained by comparing W A and W B in equation (6) and equation (10) given as follows:…”

Section: Construction Of the Multiple Decision Model Based On Multipl...mentioning

confidence: 99%

The Optimization of Airport Management Based on Collaborative Optimization of Flights and Taxis

Ding

Liu

Shi

et al. 2022

Discrete Dynamics in Nature and Society

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How to efficiently guide passengers and ensure the order of airport operation is an urgent transport problem for airport management. Based on the analysis of the factors that affect the driver’s decision-making, this paper deeply explores the collaborative association of the core factors, such as the number of flight arrivals in different periods and the average seeking distance of taxis. Firstly, according to the GPS data of taxis, the paper uses clustering algorithm to get the average passenger-seeking time from the airport and makes matching interaction between the number of flights based on time distribution and the average passenger-carrying capacity of vehicles in the parking garage, so as to build a decision-making model based on the number of taxis N; secondly, it takes passenger safety and traffic order as the priority and uses M/M/S queuing model to integrate the two factors. Taking the maintenance cost and passenger evacuation time as constraints, the judgment condition of minimum cost Zmin and the optimal number of boarding points Sm are solved. Finally, taking the flight and taxi data of Shanghai Hongqiao Airport as an example, the driver’s decision-making standard is simulated, and the accuracy of the model is verified by the deviation rate. It can provide decision-making support for taxi management of urban transportation hub and rapid evacuation of airport passengers, so as to realize the collaborative optimization of airport flight arrival and taxi carrying order.

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“…It uses a heuristic for routing and pilot assignment with an objective function that covers revenue, the empty to revenue mileage ratio, idle time, and location of the aircraft at the end of the day. Lee et al ( 23 ) model the “air taxi” operations from two perspectives: discrete-event simulation and an aggregate traffic flow model. In the discrete-event simulation, the passengers wait for a maximum of W max before an aircraft is assigned to them based on the shortest-distance policy.…”

Section: Introductionmentioning

confidence: 99%

“…The fractional ownership programs typically address the operational costs from the operator’s perspective and do not include the notion of users’ inconvenience, such as wait time, in the objective function. The focus on the “air taxi” models ensued from developments in VLJ technology, and the air taxi was envisioned as an on-demand per-seat service to transfer passengers on a regional level using a fleet of small aircraft ( 22 , 23 ). The main difference between “air taxis,” in this sense, and air charter and fractional ownership programs is in the per-seat versus per-aircraft operation.…”

Section: Introductionmentioning

confidence: 99%

Capacitated Location-Allocation-Routing Problem with Time Windows for On-Demand Urban Air Taxi Operation

Ale-Ahmad

Mahmassani

2021

Transportation Research Record: Journal of the Transportation R

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Urban air taxi (UAT) operation has gained traction with the advancements in distributed electric propulsion and the emergence of electric vertical take-off and landing aircraft. Start-up companies and aircraft manufacturers are pursuing the possibility of operating UAT at scale in urban and suburban areas and at an affordable price. However, considerable uncertainties remain about several strategic, tactical, and operational aspects that affect UAT adoption. We envision a mature state of UAT operation in which the UAT operator offers door-to-door, multimodal, on-demand, and per-seat service. We propose the concept of flexible meeting points for UAT operation where passengers are flexible about the location of the UAT pads for boarding and deboarding, and could therefore be pooled together to share an aircraft. Consequently, we model UAT fleet operation as a capacitated location-allocation-routing problem with time windows and present a mixed integer programming formulation. The formulation addresses decisions on request acceptance and rejection, allocation of requests to flights, and aircraft routing and scheduling. Additionally, it allows for consolidating the demand to increase the aircraft’s utilization and service rate. The numerical results indicate that the demand consolidation scheme could significantly decrease the number of rejected requests and the aerial mileage. Depending on the operator’s business model, the proposed formulation could be used offline in a static and deterministic setting when all requests are known in advance, or it could be employed online by sequentially solving the static and deterministic snapshot problems with no knowledge about future requests.

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A traffic engineering model for air taxi services

Cited by 9 publications

References 9 publications

Recommendations for emerging air taxi network operations based on online review analysis of helicopter services

Recommendations for emerging air taxi network operations based on online review analysis of helicopter services

The Optimization of Airport Management Based on Collaborative Optimization of Flights and Taxis

Capacitated Location-Allocation-Routing Problem with Time Windows for On-Demand Urban Air Taxi Operation

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