Predicting demand for air taxi urban aviation services using machine learning algorithms

Rajendran, Suchithra; Srinivas, Sharan; Grimshaw, Trenton

doi:10.1016/j.jairtraman.2021.102043

Cited by 51 publications

(16 citation statements)

References 45 publications

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“…In contrast to efforts on predicting the demand and delays in a data-driven manner [ 35 , 36 ], as well as to the rich literature and various formulations of demand-capacity balance problem in the context of the Air Traffic Flow Management problem (among which those mentioned above) at the tactical phase (i.e. during operation), our work considers solving the DCB problem at the pre-tactical phase.…”

Section: Related Workmentioning

confidence: 99%

Explaining deep reinforcement learning decisions in complex multiagent settings: towards enabling automation in air traffic flow management

et al. 2022

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Section: Related Workmentioning

confidence: 99%

Explaining deep reinforcement learning decisions in complex multiagent settings: towards enabling automation in air traffic flow management

et al. 2022

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“…Guo et al (2021) proposed a regression tree combined with copula-based simulations employing passenger level data to generate real-time distributional estimates of travels in an airport. Rajendran et al (2021) developed a logistic regression (LR), artificial neural networks (ANN), RF, and gradient boosting (GB) for assessing air taxi demand considering various factors such as temperature, weather conditions and visibility.…”

Section: Literature Reviewmentioning

confidence: 99%

Multi-layer perceptron based transfer passenger flow prediction in Istanbul transportation system

Utku

Kaya

2022

Decis. Mak. Appl. Manag. Eng.

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Estimating passenger movement in transportation networks is a critical aspect of public transportation systems. It allows for a greater understanding of traffic patterns, as well as efficient system evaluation and monitoring. It could also help with precise timing to emergencies or important events, as well as the improvement of urban transport system weaknesses and service quality. The number of transfer passengers demand in Istanbul, Turkey's biggest and most developed metropolis, was used to construct a real-world forecasting model in this study. The number of transfer passengers has been forecasted using popular machine learning methods such as kNN (k-Nearest Neighbours), LR (Linear Regression), RF (Random Forest), SVM (Support Vector Machine), XGBoost and MLP. The dataset utilized is made up of hourly passenger transfer counts gathered at two public transportation transfer stations in Istanbul in January 2020. Using MSE, RMSE, MAE and R2 parameters, each model's experimental data have been thoroughly evaluated. MLP has more successfully other machine learning algorithms in the majority of transportation lines, according to the experimental results.

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“…It is possible to find literature review works on some air taxi segments, as can be seen in Table 1, such as for urban air mobility [18], review of services [19], demand for services [20], and others. However, despite the great dangers of a simple safety or security failure, as far as we are aware, there is no other work reviewing air vehicle safety and security.…”

Section: Related Studiesmentioning

confidence: 99%

Security and Safety Concerns in Air Taxis: A Systematic Literature Review

Ferrão

Espes

Dezan

et al. 2022

Sensors

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Different from traditional transport systems, such as cars or trains, which are limited by land transit space, flying cars (such as UAS, drones, and air taxis) do not occupy space with traffic. They have a degree of freedom in space and time, smaller displacement, and consequently, less stress for their users. Large companies and researchers around the world are working with different architectures, algorithms, and techniques to test air taxi transport to serve a significant proportion of people safely and autonomously. One of the main issues surrounding the diffusion of air taxis is safety and security, since a simple failure can lead to the loss of high-value assets, loss of the vehicle, and/or injuries to human lives, including fatalities. In this sense, despite significant efforts, the literature is still specific and limited regarding air taxi safety and security. Therefore, this study aimed to carry out an extensive systematic literature review of the main modern advances in techniques, architectures, and research carried out around the world focused on these types of vehicles. More than 210 articles from between 2015 and January 2022 were individually reviewed. In addition, this study also presents gaps that could serve as a direction for future research. As far as the authors are aware, no other study performs this type of review focused on air taxi safety.

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Predicting demand for air taxi urban aviation services using machine learning algorithms

Cited by 51 publications

References 45 publications

Explaining deep reinforcement learning decisions in complex multiagent settings: towards enabling automation in air traffic flow management

Explaining deep reinforcement learning decisions in complex multiagent settings: towards enabling automation in air traffic flow management

Multi-layer perceptron based transfer passenger flow prediction in Istanbul transportation system

Security and Safety Concerns in Air Taxis: A Systematic Literature Review

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