Analysis of artificial neural networks training models for airfare price prediction

2020 ◽  
Vol 25 (3) ◽  
pp. 45-50
Author(s):  
Kuptsova E.A. ◽  
◽  
Ramazanov S.K. ◽  
Keyword(s):  
Dynamic Pricing ◽  
Technological Development ◽  
Regression Tree ◽  
The United States ◽  
Civil Aviation ◽  
Aviation Industry ◽  
Training Methods ◽  
Tree Model ◽  
Artificial Neural ◽  
Regression Tree Model

Air transport is playing an increasing role in the world economy every year. This is facilitated by technological development and the latest developments in the aviation industry, globalization. This paper provides an overview of artificial neural network training methods for airfare predicting. The articles for 2017-2019 were analyzed in order to determine the model with the most accurate prediction. The researchers conducted research on open data collected by themselves and set themselves the goal of creating a model that would advise a user the best time to buy a ticket when the price would be the lowest. The review of the papers by similar themes revealed that the Bagging Regression Tree model has the highest results with an accuracy of 88% and the random forest method has an accuracy of 87%. Civil aviation plays an important role in the economy of each country. Aviation is the best way to cover long distances in comfort in the shortest time. Airlines offer customers a variety of opportunities to travel both within the country and abroad. The main problem of interaction between airlines and customers is the airfare: the former want to sell more at the higher price, and the latter want to buy cheaper. Therefore, companies use their own private algorithms for dynamic pricing and constantly monitor the market situation, responsive to changes in demand and the actions of competitors. This behavior allows them to achieve a balance between the desires of airlines and customers. Scientists are trying to invent a way to predict airfare so that air travelers can buy them at the lowest price. The results of the work in this area provide general rules for the best purchase. For example, according to the article (Udachny, 2016) thebest day to buy a ticket by expedia.com for a domestic flight on the United States is Sunday, and the best period is 57 days before departure. This article provides an overview of the works, the authors of which compared the models of machine learning. Achievements in this area are limited to direct flights of a certain domestic market (USA, India) and 88% accuracy of the forecast (Tziridis et al., 2017). The Bagging Regression Tree model described in the article (Tziridis et al., 2017) can be considered the best result. This trained model can make predictions based only on two parameters: the number of free cargo and the number of days left before departure and has an accuracy of 88%.

scholarly journals Managing raptors to reduce wildlife strikes at Chicago’s O’Hare International Airport

2016 ◽  
Author(s):  
Craig K. Pullins ◽  
Travis L. Guerrant ◽  
Scott F. Beckerman ◽  
Brian E. Washburn
Keyword(s):  
Significant Risk ◽  
Federal Aviation Administration ◽  
The United States ◽  
Civil Aviation ◽  
Aviation Industry ◽  
International Airport ◽  
The Past ◽  
Wildlife Damage Management ◽  
Wildlife Populations ◽  
The Cost

Nationally, wildlife-aircraft collisions (wildlife strikes) have been increasing over the past 25 years; denoted in the National Wildlife Strike Database that has been maintained by the Federal Aviation Administration (FAA) since 1990. Increasing wildlife populations and air traffic coupled with quieter, faster aircraft create a significant risk to aviation safety; the cost to the civil aviation industry is an estimated $937 million dollars annually. USDA/APHIS/Wildlife Services (WS) provides technical and direct assistance to over 850 airports and airbases around the United States, including Chicago’s O’Hare International Airport (ORD). At ORD, raptors are one of the most commonly struck bird guild and accounted for at least 25% of damaging strikes from 2010-2013. An Integrated Wildlife Damage Management (IWDM) program is implemented at ORD to reduce the presence of wildlife on the airfield, consequently lowering the risk of wildlife strikes. Professional airport wildlife biologists at ORD concentrate much of their efforts on raptor management due to the high strike risk these birds pose to aircraft on the airfield itself. A variety of techniques are currently used to manage raptor populations at ORD. Concurrently, research is being conducted to evaluate the efficacy of the Red-tailed Hawk relocation program at the airport, as well as to assess their movements within the airfield environment.

scholarly journals A global bibliometric perspective on soil erosion modelling

2020 ◽  
Author(s):  
Nejc Bezak ◽  
Keyword(s):  
Conference Proceedings ◽  
Soil Erosion ◽  
Regression Tree ◽  
Tree Model ◽  
Boosted Regression Tree ◽  
Field Activity ◽  
Erosion Modelling ◽  
Meta Information ◽  
Number Of Citations ◽  
Regression Tree Model

<p>Systematic bibliometric investigations are useful to evaluate and compare the scientific impact of journal papers, book chapters and conference proceedings. Such studies allow the detection of emerging research topics, the analyses of cooperation networks, and the collection of in-depth insights into a specific research topic. In the presented work, we carried out a bibliometric study in order to obtain an in-depth knowledge on soil erosion modelling applications worldwide.</p><p>As a starting point, we used the soil erosion modelling meta-analysis data collection generated by the authors of this abstract in a joint community effort. This database contains meta-information of more than 3,000 documents published between 1994 and 2018 that are indexed in the SCOPUS database. The documents were reviewed and database entries verified. The database contains various types of meta-information about the modelling studies (e.g., model used, study area, input data, calibration, etc.). The bibliometric information was also included in the database (e.g., number of citations, type of publication, Scopus category, etc.). We investigated differences among publication types and differences between papers published in journals that are part of various Scopus categories. Moreover, relationships between publication CiteScore, number of authors, and number of citations were analyzed. A boosted regression tree model was used to detect the relative impact of the selected meta-information such as erosion model used, spatial modelling scale, study period, field activity on the total number of citations. Detailed investigation of the most cited papers was also conducted. The VOSviewer software was used to analyze citations, co-citations, bibliographic coupling, and co-authorship networks of the database entries.  </p><p>Our bibliometric investigations demonstrated that journal publications, on average, receive more citations than book series or conference proceedings. There were differences among the erosion models used, and some specific models such as the WaTEM/SEDEM model, on average, receive more citations than other models (e.g., USLE). It should also be noted that self-citation rates in case of most frequently used models were similar. Global studies, on average, receive more citations than studies dealing with plot, regional, or national scales. According to the boosted regression tree model, model calibration, validation, or field activity do not have significant impact on the obtained publication citations. Co-citation investigation revealed some interesting patterns. Our results also indicate that papers about soil erosion modeling also attract citations from different fields and better international cooperation is needed to advance this field of research with regard to its visibility and impact on human societies.    </p>

scholarly journals A Dynamic Adjustment Model of Cruising Taxicab Fleet Size Combined the Operating and Flied Survey Data

2020 ◽  
Vol 12 (7) ◽  
pp. 2776 ◽  
Author(s):  
Xiaofei Ye ◽  
Min Li ◽  
Zhongzhen Yang ◽  
Xingchen Yan ◽  
Jun Chen
Keyword(s):  
Waiting Time ◽  
Layer Structure ◽  
Operating Time ◽  
Regression Tree ◽  
Threshold Value ◽  
Dynamic Adjustment ◽  
Tree Model ◽  
Fleet Size ◽  
Average Waiting Time ◽  
Regression Tree Model

Due to the lack of adjustment index systems for taxi fleet sizes in China, this paper used the taxi operating datasets from Ningbo City and established a regression tree model to consider the endogenous indicators that affect taxi fleet sizes. Then, a dynamic adjustment mechanism of taxi fleet sizes was proposed by combining the exogenous and endogenous indicators. The importance of the exogenous and endogenous indicators was sorted using the Delphi method. The threshold value of each indicator was also given. The results indicated that (1) in the three-layer structure of the regression tree model, the mileage utilization had the strongest effect on the fleet size of taxis, and the F statistic was 63.73; followed by the average daily revenue of a single taxi, the average waiting time to catch a single taxi, the average operating time of a single taxi, and the revenue per 100 km. The overall accuracy of the model was found to be valid. (2) When the mileage utilization was less than 0.6179 and the average daily revenue of a single taxi was less than 798.38 Yuan, the fleet size of cruising taxis was surplus and should be reduced by 362 vehicles. (3) When the mileage utilization was more than 0.6774 and the average waiting time to catch a single taxi was more than 259.09 s, the fleet size of cruising taxis was insufficient, and we suggest an increase of 463 taxis.

scholarly journals Research on the Relationship Between Teachers’ Professional Skills and Students’ Expectations for Improving the Study Environment

2018 ◽  
Vol 4 (8) ◽  
pp. 109
Author(s):  
Aida Stikliene
Keyword(s):  
Regression Tree ◽  
Point Of View ◽  
Learning Problems ◽  
Data Collection Tool ◽  
Tree Model ◽  
Professional Skills ◽  
Working Students ◽  
Variate Analysis ◽  
The Relationship ◽  
Regression Tree Model

The teacher's attitude towards the teaching process and communication skills is of particular importance and plays a crucial role in today’s rapidly changing world. It has to go together, raising consciousness and awareness of individuals on study environment issues and ensuring that they contribute to solutions of learning problems. The research was conducted with 405 prospective professionals from the Faculty of Forest Sciences and Ecology, Aleksandras Stulginskis University. An interactive questionnaire ‘Study subject in student’s eyes’ (SSSE) developed at Aleksandras Stulginskis University (2014–2017) was used as the data collection tool. This article analyses the teachers’ pedagogical work from the students’ point of view. The multi-variate analysis and regression tree model were used in the interpretation of results. The results confirmed the hypothesis that hard working students better evaluate teachers’ professional skills. It seems that elder course students with age have higher expectations from the teaching environment. Keywords:

scholarly journals Polityka surowcowa w wymiarze metali ziem rzadkich. Rwanda – studium przypadku

2021 ◽  
Vol 19 (1) ◽  
pp. 25-38
Author(s):  
Rafał Kamprowski ◽  
Keyword(s):  
Rare Earth ◽  
Democratic Republic Of Congo ◽  
Technological Development ◽  
Dominant Role ◽  
Rare Earth Metals ◽  
Health And Safety ◽  
The United States ◽  
Raw Material ◽  
Aviation Industry ◽  
Material Policy

The primary goal of a state's raw material policy is to ensure its raw material security. Due to the progressing technological development, rare earth metals play an increasingly important role. For several years, they have become the subject of a political game between the countries that play a dominant role in their market, i.e., the People's Republic of China and the United States. The other countries where the discussed groups of metals are mined were left on the sidelines of the discussion. The aim of the research undertaken in this article is to show the role of rare earth metals in creating raw material security on the example of Rwanda. It is home to some of the largest deposits of niobium and tantalum, key elements used in electronics, in aviation industry, and in the manufacturing of medical equipment. The main result of the research carried out is as follows: extraction of rare earth metals constitutes one of the foundations of the Rwandan economy. In recent years, there has been a significant professionalization of mining practices, bearing in mind the environment, health, and safety. It was also established that the factors that pose the most important threat to Rwanda's raw material policy include the current, uneasy situation on the border with the Democratic Republic of Congo, where the largest deposits of the metals in question are located, and the negative effects of the global Sars-Cov-2 pandemic.

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