Forecasting short-term air passenger demand using big data from search engine queries

Kim, Seongdo; Shin, Do Hyoung

doi:10.1016/j.autcon.2016.06.009

Cited by 65 publications

(22 citation statements)

References 23 publications

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“…In real estate it has been used to develop early warnings and forecasts [71], providing multimedia-based real estate services over the internet [72], and other applications. It comes in different forms but the current study looks at big data [73] and AI and robotics [74,75].…”

Section: Data-mining Technologiesmentioning

confidence: 99%

A Systematic Review of Smart Real Estate Technology: Drivers of, and Barriers to, the Use of Digital Disruptive Technologies and Online Platforms

2018

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Real estate needs to improve its adoption of disruptive technologies to move from traditional to smart real estate (SRE). This study reviews the adoption of disruptive technologies in real estate. It covers the applications of nine such technologies, hereby referred to as the Big9. These are: drones, the internet of things (IoT), clouds, software as a service (SaaS), big data, 3D scanning, wearable technologies, virtual and augmented realities (VR and AR), and artificial intelligence (AI) and robotics. The Big9 are examined in terms of their application to real estate and how they can furnish consumers with the kind of information that can avert regrets. The review is based on 213 published articles. The compiled results show the state of each technology’s practice and usage in real estate. This review also surveys dissemination mechanisms, including smartphone technology, websites and social media-based online platforms, as well as the core components of SRE: sustainability, innovative technology and user centredness. It identifies four key real estate stakeholders—consumers, agents and associations, government and regulatory authorities, and complementary industries—and their needs, such as buying or selling property, profits, taxes, business and/or other factors. Interactions between these stakeholders are highlighted, and the specific needs that various technologies address are tabulated in the form of a what, who and how analysis to highlight the impact that the technologies have on key stakeholders. Finally, stakeholder needs as identified in the previous steps are matched theoretically with six extensions of the traditionally accepted technology adoption model (TAM), paving the way for a smoother transition to technology-based benefits for consumers. The findings pertinent to the Big9 technologies in the form of opportunities, potential losses and exploitation levels (OPLEL) analyses highlight the potential utilisation of each technology for addressing consumers’ needs and minimizing their regrets. Additionally, the tabulated findings in the form of what, how and who links the Big9 technologies to core consumers’ needs and provides a list of resources needed to ensure proper information dissemination to the stakeholders. Such high-quality information can bridge the gap between real estate consumers and other stakeholders and raise the state of the industry to a level where its consumers have fewer or no regrets. The study, being the first to explore real estate technologies, is limited by the number of research publications on the SRE technologies that has been compensated through incorporation of online reports.

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Section: Data-mining Technologiesmentioning

confidence: 99%

A Systematic Review of Smart Real Estate Technology: Drivers of, and Barriers to, the Use of Digital Disruptive Technologies and Online Platforms

2018

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“…For example, Big Data analytics have been used to predict air passenger demands in airports (Kim and Shin, 2016), to model commuting patterns (Wan et al, 2018), and to infer transport mode using data from mobile devices (Semanjski et al, 2017). Also, Jeong et al (2017;2019) presented a cloud-based Big Data management and analytics framework that handles the massive and diverse datasets used for bridge monitoring.…”

Section: Big Data In Architecture Engineering and Construction (Aec)mentioning

confidence: 99%

Big Data Analytics System for Costing Power Transmission Projects

Delgado

Bilal

Ajayi

et al. 2020

J. Constr. Eng. Manage.

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Inaccurate cost estimates have significant impacts on the final cost of power transmission projects and erode profits. Methods for cost estimation have been investigated thoroughly, but they are not used widely in practice. The purpose of this study is to leverage a Big Data architecture, to manage the large and diverse data required for predictive analytics. This paper presents a Predictive Analytics and Modelling System (PAMS) that facilitates the use of different data-driven cost prediction methods. A 2.75 million-point dataset of power transmission projects has been used as a case study. The proposed Big Data architecture is fit for purpose. It can handle the diverse datasets used in the construction sector. The three most prevalent cost estimation models were implemented (linear regression, support vector regression, and artificial neural networks). All models performed better than the estimated human-level performance. The primary contribution of this study to the Body of Knowledge is an empirical indication that data-driven methods analysed in this study are on average 13.5% better than manual methods for cost estimation of power transmission projects. Additionally, the paper presents a Big Data architecture that can manage and process large varied datasets and seamless scalability.

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“…For safety critical systems as nuclear power plants, highspeed trains and aerospace shuttles, large amounts of data have been collected and stored through integrated sensors, video inspections, hand-held field tables and other sources [2], [39]. From such data, proper data-driven approaches can extract complex relations among variables for accurate fault detection, as shown in [18], [24], [26], [34]. The previous works are more like for offline processing of the faults, as the demand on the hardware, e.g.…”

Section: Introductionmentioning

confidence: 99%

KNN-FSVM for Fault Detection in High-Speed Trains

Liu

Zio

2018

2018 IEEE International Conference on Prognostics and Health Management (ICPHM)

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Prognostics and health management can improve the reliability and safety of transportation systems. Data collected from diverse sources provide a chance and at the same time a challenge for data-driven PHM methods and models. The data often exhibit challenging characteristics like imbalanced data on normal and faulty conditions, noise and outliers, data points of different importance for the data-driven model, etc. In this paper, a k nearest neighbors-based fuzzy support vector machine is proposed for reducing the computational burden and tackling the issue of imbalance and outlier data, in fault detection. Fault detection is mathematically a classification problem. In this paper, the reverse nearest neighbors technique is adopted for detecting outliers and the k nearest neighbors technique is used to identify the borderline points for defining the classification hyperplane in support vector machines. Considering the position of each data point and the distribution of its nearest neighbors, a new method is proposed for calculating their estimation error costs. A real case study concerning fault detection in a braking system of a highspeed train is considered.

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Forecasting short-term air passenger demand using big data from search engine queries

Cited by 65 publications

References 23 publications

A Systematic Review of Smart Real Estate Technology: Drivers of, and Barriers to, the Use of Digital Disruptive Technologies and Online Platforms

A Systematic Review of Smart Real Estate Technology: Drivers of, and Barriers to, the Use of Digital Disruptive Technologies and Online Platforms

Big Data Analytics System for Costing Power Transmission Projects

KNN-FSVM for Fault Detection in High-Speed Trains

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