A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2023; you can also visit the original URL.
The file type is application/pdf.
Filters
Deep Learning Models for Airport Demand Forecasting With Google Trends
2023
International Journal of Cyber Behavior Psychology and Learning
Preserved Fulltext
To fill this gap, the current study predicts the demand of Madrid airport demand with Google search query data using H2O deep learning method. ...
Besides, search queries "fly to madrid," and "flights to madrid spain" were found to be the cause of the actual domestic air passenger demand in Madrid. ...
In this respect, ANN (Artificial Neural Network) is a widely used deep network architecture that predicts consumer demand in various industries, such as tourism (eg., Law et al., 2019; Sun et al., 2019 ...
doi:10.4018/ijcbpl.324086
fatcat:s7xn3ylt75cq5fn3olr5mpq2ba
Chinese User Service Intention Classification Based on Hybrid Neural Network
2019
Journal of Physics, Conference Series
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Therefore, a hybrid neural network classification model based on BiLSTM and CNN is proposed to recognize users service intentions. ...
In order to satisfy the consumers' increasing personalized service demand, the Intelligent service has arisen. ...
Chit-chat I want to book an air ticket to Beijing. Task-oriented dialogue (Booking air tickets) I want to book a neat and low-priced inn near Harvard. ...
doi:10.1088/1742-6596/1229/1/012054
fatcat:ouwphoquzncivkyeitqxcheptu
Deep Learning XAI for Bus Passenger Forecasting: A Use Case in Spain
2022
Mathematics
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Our goal is to develop a predictive and linguistically interpretable model, useful for decision making using large volumes of data from different sources. ...
We obtained an interpretable model from the LSTM neural network using a surrogate model and the 2-tuple fuzzy linguistic model, which improves the linguistic interpretability of the generated Explainable ...
For this, we first proposed to improve the prediction of passenger demand through historical data and external sources, such as calendar, through the use of deep learning techniques using deep neural LSTM ...
doi:10.3390/math10091428
fatcat:5nwdxwfm5jg7df2wfu2v37e4jy
Flight Fare Prediction Using Machine Learning Approach
2023
International Journal for Research in Applied Science and Engineering Technology
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2023; you can also visit the original URL.
The file type is application/pdf.
Abstract: In the airline industry, ticket pricing is a complex process that is influenced by various factors, including demand, availability, and competition. ...
By analysing historical data, machine learning algorithms can identify a minimum airfare, data for a specific air route has been collected including the features like arrival time, departure time and airways ...
In 2019, another study by Singh et al. used a Deep Learning approach to predict flight ticket prices. ...
doi:10.22214/ijraset.2023.49224
fatcat:wtohx5aytrhkrmuxmc6qsysp4y
Large-Scale Data-Driven Airline Market Influence Maximization
[article]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
At the lower level, our neural networks consider a wide variety of features, such as classical air carrier performance features and transportation network features, to predict the market influence. ...
We present a prediction-driven optimization framework to maximize the market influence in the US domestic air passenger transportation market by adjusting flight frequencies. ...
Neural Network-based Prediction Whereas many existing methods rely on classical machine learning approaches, we use the following neural network to predict: h (1) , = (f , W (0) + b (0) ), for initial ...
arXiv:2105.15012v1
fatcat:vax6kxnhpfcz7pxizs6xohqkfi
Processing, mining and visualizing massive urban data
2017
The European Symposium on Artificial Neural Networks
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
For example, a large amount of urban data is collected by various sensors, such as smart meters, or provided by GSM, Wi-Fi or Bluetooth records, ticketing data, geotagged posts on social networks, etc. ...
If we take the case of public transport, long-term prediction might be valuable for planning the transport network, while short-term demand prediction could be used for transit operation purposes to match ...
Due to the availability of sizeable historical datasets, deep networks are obvious candidates for performing this type of task. ...
dblp:conf/esann/BorgnatCO17
fatcat:xuo477ozqjd6fhlfavgkplcmxi
Autonomous Airline Revenue Management: A Deep Reinforcement Learning Approach to Seat Inventory Control and Overbooking
[article]
2019
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Note that this fulltext copy is not of the "primary" version of this work. The version it corresponds to is:
2019 pre-print arXiv:1902.06824v1 fatcat:n4gf4owtrbentfztmai6spho3m
We have addressed this problem using Deep Q-Learning with the goal of maximizing the reward for each flight departure. ...
products targeted at each of these demand segments. ...
Deep Q-Learning Like perceptron Q-learning, DQL also combines the idea of using an approximator and Q-learning. But, instead of using a perceptron, a deep neural network is used. ...
arXiv:1902.06824v2
fatcat:kz3h7h7mincynmh57pkukr5vru
Deep Choice Model Using Pointer Networks for Airline Itinerary Prediction
2017
Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '17
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
Note that this fulltext copy is not of the "primary" version of this work. The version it corresponds to is:
2018 pre-print arXiv:1803.05976v1 fatcat:h53ll5dlhnaodn3ehoherwnjbi
Given an input sequence, this type of deep neural architecture combines Recurrent Neural Networks with the Attention Mechanism to learn the conditional probability of an output whose values correspond ...
In this paper, we concentrate with the problem of modeling air passenger choices of flight itineraries. ...
CONCLUSIONS AND FUTURE WORK In this work we propose a new deep choice model based on Pointer Networks, a recent neural architecture that combines Recurrent Neural Networks with the A ention Mechanism ...
doi:10.1145/3097983.3098005
dblp:conf/kdd/MottiniA17
fatcat:y7kc3kb4sjekvl4em6ipvs5zuu
Predicting the Skies: A Novel Model for Flight-Level Passenger Traffic Forecasting
[article]
2024
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2024; you can also visit the original URL.
The file type is application/pdf.
Note that this fulltext copy is not of the "primary" version of this work. The version it corresponds to is:
2024 pre-print arXiv:2401.03397v1 fatcat:6rtqaqzq4jeyplsdidjz4apyli
Our proposed neural network integrates the strengths of Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN), exploiting the temporal patterns and spatial relationships within the data ...
This study, therefore, highlights the significant potential of deep learning techniques and meticulous data processing in advancing the field of flight traffic prediction. ...
In parallel, the emergence of neural network has marked a new era in forecasting. ...
arXiv:2401.03397v2
fatcat:dv7dor2kfrfmlhwuqemwss5c7a
A review of demand forecasting models and methodological developments within tourism and passenger transportation industry
2019
Journal of Tourism Futures
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
Purpose -The purpose of this paper is to review the current literature in the field of tourism demand forecasting. ...
Design/methodology/approach -Published papers in the high quality journals are studied and categorized based their used forecasting method. ...
(2009)
Predicting short-term railway passenger
demand
Daily and monthly
passenger demand
Ticket sales data
Multiple temporal units neural
network/Parallel ensemble neural
network
Celebi et ...
doi:10.1108/jtf-10-2018-0061
fatcat:yrh57fdybbfnjn3byl23rwnn3e
Using the Generative Adversarial Network to Generate Recommendations
[chapter]
2020
Frontiers in Artificial Intelligence and Applications
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
In our work, we compare recommendations from the Generative Adversarial Network with recommendation from the Deep Semantic Similarity Model (DSSM) on real-world case of airflight tickets. ...
We found a way to train the GAN so that users receive appropriate recommendations, and during A/B testing, we noted that the GAN-based recommendation system can successfully compete with other neural networks ...
networks (restricted Boltzmann machine, deep autoencoders and etc.). ...
doi:10.3233/faia200680
fatcat:s4p3xxn5svasblj7kueembppmm
Deep IV: A Flexible Approach for Counterfactual Prediction
2017
International Conference on Machine Learning
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Our IV specification resolves into two prediction tasks that can be solved with deep neural nets: a first-stage network for treatment prediction and a second-stage network whose loss function involves ...
This Deep IV framework 1 allows us to take advantage of off-the-shelf supervised learning techniques to estimate causal effects by adapting the loss function. ...
We would also like to thank Holger Hoos for the use of the Ada cluster, without which the experiments would not have been possible. ...
dblp:conf/icml/HartfordLLT17
fatcat:v6o2wpnnojfs3c2fkdigcedxqq
Emerging Technologies for Smart Cities' Transportation: Geo-Information, Data Analytics and Machine Learning Approaches
2022
ISPRS International Journal of Geo-Information
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
integrated deep learning towards SC transportation. ...
This survey paper aimed to give useful insights to researchers regarding the roles that data-driven approaches can be utilized for in smart cities (SCs) and transportation. ...
Figure 24 . 24 Figure 24.Neural network approach for public transportation prediction [154] .
Figure 25 . 25 Figure 25. Deep neural network travel prediction framework [168]. ...
doi:10.3390/ijgi11020085
fatcat:bjkv6cu7zbfqbl7q7ezfhai5ya
Space Physical Sensor Protection and Control System Based on Neural Network Prediction: Application in Princess Elizabeth Area of Antarctica
2020
Sensors
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Based on the analysis of local meteorological data, various neural network models are compared, and the training model with the smallest error is used to predict the future ambient temperature. ...
This technology solves the demand for unmanned high-altitude physical observation or astronomical observation stations in inland areas. ...
Although many methods use long-term and short-term memory, we use three latest methods of LSTM. We also analyzed the prediction results of the BP neural network and the ELM neural network. ...
doi:10.3390/s20174662
pmid:32824950
fatcat:lz2p4s3vd5cyraxhsdegl4x62a
Alexa, Predict My Flight Delay
[article]
2022
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Recent research has focused on using artificial intelligence algorithms to predict the possibility of flight delays. Earlier prediction algorithms were designed for a specific air route or airfield. ...
Therefore, precise flight delay prediction is beneficial for the aviation industry and passenger travel. ...
[23] demonstrates that neural networks and multilayer perceptron predict delayed flights efficiently. ...
arXiv:2208.09921v1
fatcat:5mcv73agpnainpisrjrueiqn2u
« Previous
Showing results 1 — 15 out of 1,069 results