Estimating an Origin-Destination Table Under Repeated Counts of In-Out Volumes at Highway Ramps: Use of Artificial Neural Networks

2000 ◽  
Vol 1739 (1) ◽  
pp. 59-66 ◽  
Author(s):  
Shinya Kikuchi ◽  
Mitsuru Tanaka
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Traditional Method ◽  
Freeway Traffic ◽  
Hand Column ◽  
The Neural Network ◽  
Artificial Neural ◽  
Trip Production ◽  
The Right

A method is proposed that applies an artificial neural network model to estimate an origin-destination (O-D) matrix for a freeway network for which the data on inflow and outflow at the ramps are gathered regularly. This problem is the same as estimating the elements of an O-D table, given that many sets of data about the right-hand column total (trip production) and the bottom row total (trip attraction) are available. A neural network model is developed to emulate the stimulusresponse process on the freeway traffic, in which the stimulus is the inflow at the entrance ramps and the response the outflow at the exit ramps. After the neural network of a particular structure is trained by many sets of data (e.g., sets of daily volumes), the weights of the neural network are found to represent the ramp-to-ramp volume expressed in the proportion of the in-flow at the corresponding ramps. The model is applied to estimate a ramp-to-ramp O-D table for the Tokyo expressway network. The result is compared with the actual O-D table obtained from a survey. The model is found to be useful not only for estimating the O-D volume with much less data than for the traditional method, but also for verifying the existence of a pattern in the traffic flow.

scholarly journals PERANCANGAN MODEL UNTUK PERAMALAN TINGKAT PERTUMBUHAN PEMBIAYAAN MELALUI METODE JARINGAN SARAF TIRUAN DI BNI SYARIAH

2020 ◽  
Vol 6 (2) ◽  
Author(s):  
Orfyanny S Themba ◽  
Susianah Mokhtar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Data Bank ◽  
Forecasting Accuracy ◽  
The Neural Network ◽  
Forecasting Method ◽  
Artificial Neural

ABSTRAKTren perkembangan pembiayaan di Indonesia mulai meningkat namun cenderung melambat dari tahun ke tahun. Peramalan pertumbuhan pembiayaan pada bank syariah menjadi hal yang menarik karena naik turunnya pembiayaan akan berdampak pada perekonomian Indonesia. Tujuan dari penelitian ini melakukan peramalan pertumbuhan pembiayaan dalam jangka waktu setahun melalui metode Jaringan Saraf Tiruan pada data Bank BNI Syariah dari tahun 2015 sampai dengan 2019. Hasil dari peramalan diharapkan memberi informasi bagi bank untuk menunjang pengambilan keputusan dan menyiapkan strategi meningkatkan pembiayaan sehingga semakin besar laba yang akan diperoleh. Model peramalan dibuat berdasarkan metode peramalan dan ditujukan untuk digunakan pada aplikasi peramalan pembiayaan. Model Jaringan Saraf Tiruan memiliki nilai akurasi peramalan yang tinggi karena memiliki nilai error RMSE, MAPE yang minimum. Dari hasil peramalan menggunakan model Jaringan Saraf Tiruan menunjukkan terjadi peningkatan pembiayaan pada setiap bulannya untuk akad murabahah, mudharabah, musyarakah dan qardh. Hanya pembiayaan yang menggunakan ijarah yang mengalami penurunan drastis dibanding tahun-tahun sebelumnya. Pembiayaan murabahah masih tetap mendominasi dibanding akad mudharabah, musyarakah, qardh dan ijarah selama tahun 2020 Kata Kunci: Jaringan Saraf Tiruan ;PembiayaanABSTRACT Trend of financing development in Indonesia is starting to increase but tends to slow down from year to year. It is interesting to forecast the growth of financing in Islamic banks because the up and down of financing will have an impact on the Indonesian economy. The purpose of this study to forecast financing growth within a year through the Neural Network method on BNI Syariah Bank data from 2015 to 2019. The results of the forecast are expected to provide information for banks to support decision making and prepare strategies to increase financing so that greater profits that will be obtained. The forecasting model is made based on the forecasting method and is intended for use in financing forecasting applications. The Artificial Neural Network Model has a high value of forecasting accuracy because it has a minimum error value of RMSE, MAPE. The results of forecasting using the Artificial Neural Network model show an increase in financing every month for murabahah, mudharabah, musyarakah and qardh contracts. Only financing using ijarah has experienced a drastic decline compared to previous years. Murabahah financing still dominates over the mudharabah, musyarakah, qardh and ijarah contracts during 2020Keyword: Arificial Neural Network ;Financing

scholarly journals Modeling of a Novel Coaxial Ducted Fan Aerial Robot Combined with Corner Environment by Using Artificial Neural Network

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5805
Author(s):  
Tianfu Ai ◽  
Bin Xu ◽  
Changle Xiang ◽  
Wei Fan ◽  
Yibo Zhang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Simulation Software ◽  
Ducted Fan ◽  
The Neural Network ◽  
Aerial Robot ◽  
Artificial Neural ◽  
Comprehensive Modeling

A novel coaxial ducted fan aerial robot with a manipulator is proposed which can achieve some hover operation tasks in a corner environment, such as switching on and off a wall-attached button on the corner. In order to study the aerodynamic interference between the prototype and the environment when the aerial robot is hovering in the corner environment, a method for the comprehensive modeling of the prototype and corner environment based on the artificial neural network is presented. By using the CFD simulation software, the flow field of the prototype at different positions with the corner effect is analyzed. After determining the input, output and structure of the neural network model, the Adam and gradient descent algorithms are selected as the neural network training algorithms, respectively. In addition, to optimize the initial weights and biases of the neural network model, the genetic algorithm is precisely used. The three-dimensional prediction surfaces generated by the three methods of the neural network, kriging surface and the polynomial fitting are compared. The results show that the neural network has high prediction accuracy, and can be applied to the comprehensive modeling of the prototype and the corner environment.

scholarly journals Artificial Neural Network-Based Model for Prediction of Frost Heave Behavior of Silty Soil Specimen

2021 ◽  
Vol 11 (22) ◽  
pp. 10834
Author(s):  
Seok Yoon ◽  
Dinh-Viet Le ◽  
Gyu-Hyun Go
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Parametric Study ◽  
Mechanical Analysis ◽  
Frozen Soil ◽  
Frost Heave ◽  
The Neural Network ◽  
Artificial Neural

Frost heave action is a major issue in permafrost regions that can give rise to various geotechnical engineering problems. To analyze and predict this phenomenon at a specimen scale, this study conducted a fully coupled thermal-hydro-mechanical analysis and evaluated the frost heave behavior of frozen soil considering geotechnical parameters. Furthermore, a parametric study was performed to quantitatively analyze the effects of major geotechnical properties on frost heave behavior. According to the results of the parametric study, the amount of heave tended to decrease as the particle thermal conductivity increased, whereas the frost heave ratio tended to increase as the initial hydraulic conductivity increased. After evaluating the sensitivity of each parameter to frost heave behavior through statistical analyses, an artificial neural network model was developed to practically predict frost heave behavior. According to the verification results of the neural network model, the trained network model demonstrated a reliable accuracy (R2 = 0.893) in predicting frost heave ratio, even when the model used test datasets that were not part of the training datasets.

scholarly journals Determination of daily solar ultraviolet radiation using statistical models and artificial neural networks

2006 ◽  
Vol 24 (8) ◽  
pp. 2105-2114 ◽  
Author(s):  
F. J. Barbero ◽  
G. López ◽  
F. J. Batlles
Keyword(s):  
Neural Network ◽  
Statistical Model ◽  
Network Model ◽  
Neural Network Model ◽  
Weather Conditions ◽  
Neural Network Models ◽  
Solar Ultraviolet Radiation ◽  
Air Mass ◽  
The Neural Network ◽  
Artificial Neural

Abstract. In this study, two different methodologies are used to develop two models for estimating daily solar UV radiation. The first is based on traditional statistical techniques whereas the second is based on artificial neural network methods. Both models use daily solar global broadband radiation as the only measured input. The statistical model is derived from a relationship between the daily UV and the global clearness indices but modulated by the relative optical air mass. The inputs to the neural network model were determined from a large number of radiometric and atmospheric parameters using the automatic relevance determination method, although only the daily solar global irradiation, daily global clearness index and relative optical air mass were shown to be the optimal input variables. Both statistical and neural network models were developed using data measured at Almería (Spain), a semiarid and coastal climate, and tested against data from Table Mountain (Golden, CO, USA), a mountainous and dry environment. Results show that the statistical model performs adequately in both sites for all weather conditions, especially when only snow-free days at Golden were considered (RMSE=4.6%, MBE= –0.1%). The neural network based model provides the best overall estimates in the site where it has been trained, but presents an inadequate performance for the Golden site when snow-covered days are included (RMSE=6.5%, MBE= –3.0%). This result confirms that the neural network model does not adequately respond on those ranges of the input parameters which were not used for its development.

scholarly journals Burst Pressure Prediction of API 5L X-Grade Dented Pipelines Using Deep Neural Network

2020 ◽  
Vol 8 (10) ◽  
pp. 766
Author(s):  
Dohan Oh ◽  
Julia Race ◽  
Selda Oterkus ◽  
Bonguk Koo
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Deep Neural Network ◽  
Machine Learning Techniques ◽  
Burst Pressure ◽  
Comparison Results ◽  
Artificial Neural

Mechanical damage is recognized as a problem that reduces the performance of oil and gas pipelines and has been the subject of continuous research. The artificial neural network in the spotlight recently is expected to be another solution to solve the problems relating to the pipelines. The deep neural network, which is on the basis of artificial neural network algorithm and is a method amongst various machine learning methods, is applied in this study. The applicability of machine learning techniques such as deep neural network for the prediction of burst pressure has been investigated for dented API 5L X-grade pipelines. To this end, supervised learning is employed, and the deep neural network model has four layers with three hidden layers, and the neural network uses the fully connected layer. The burst pressure computed by deep neural network model has been compared with the results of finite element analysis based parametric study, and the burst pressure calculated by the experimental results. According to the comparison results, it showed good agreement. Therefore, it is concluded that deep neural networks can be another solution for predicting the burst pressure of API 5L X-grade dented pipelines.

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