Estimation of Annual Average Daily Traffic on Low-Volume Roads: Factor Approach Versus Neural Networks

2000 ◽  
Vol 1719 (1) ◽  
pp. 103-111 ◽  
Author(s):  
Satish C. Sharma ◽  
Pawan Lingras ◽  
Guo X. Liu ◽  
Fei Xu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Traffic Monitoring ◽  
Network Approach ◽  
Annual Average ◽  
Neural Network Approach ◽  
The Neural Network ◽  
Low Volume Roads ◽  
Annual Average Daily Traffic ◽  
Low Volume

Estimation of the annual average daily traffic (AADT) for low-volume roads is investigated. Artificial neural networks are compared with the traditional factor approach for estimating AADT from short-period traffic counts. Fifty-five automatic traffic recorder (ATR) sites located on low-volume rural roads in Alberta, Canada, are used as study samples. The results of this study indicate that, when a single 48-h count is used for AADT estimation, the factor approach can yield better results than the neural networks if the ATR sites are grouped appropriately and the sample sites are correctly assigned to various ATR groups. Unfortunately, the current recommended practice offers little guidance on how to achieve the assignment accuracy that may be necessary to obtain reliable AADT estimates from a single 48-h count. The neural network approach can be particularly suitable for estimating AADT from two 48-h counts taken at different times during the counting season. In fact, the 95th percentile error values of about 25 percent as obtained in this study for the neural network models compare favorably with the values reported in the literature for low-volume roads using the traditional factor approach. The advantage of the neural network approach is that classification of ATR sites and sample site assignments to ATR groups are not required. The analysis of various groups of low-volume roads presented also leads to a conclusion that, when defining low-volume roads from a traffic monitoring point of view, it is not likely to matter much whether the AADT on the facility is less than 500 vehicles, less than 750 vehicles, or less than 1,000 vehicles.

Individualizing Displays through the Use of Neural Networks

1997 ◽  
Vol 41 (1) ◽  
pp. 269-273
Author(s):  
Kai-Chun Cheng ◽  
Ray E. Eberts
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Intelligent Vehicle ◽  
Network Approach ◽  
Neural Network Approach ◽  
Traveler Information ◽  
Gas Stations ◽  
The Neural Network ◽  
Advanced Traveler Information System ◽  
Near Future

An Advanced Traveler Information System (ATIS), a key component of Intelligent Vehicle highway Systems (IVHS) in the near future, will help travelers find locations of restaurants, lodging, gas stations, and rest stops. On typical ATIS displays, which are now being incorporated in some advanced vehicles, the choices for these traveler services are presented to the vehicle occupants alphabetically. An experiment was conducted to determine whether individualizing the display through the use of neural networks enhanced performance when choosing restaurants. The neural network ATIS was compared to an ATIS that displayed the most frequently chosen restaurants at the top, one that alphabetized the list of restaurants, and one that randomly displayed the restaurant choices. The time to choose a restaurant was significantly faster for the individualized displays (neural network and frequency) when compared to the nonindividualized displays (alphabetical and random). When the two individualized displays were compared, choice time was significantly faster for the neural network approach.

scholarly journals ESTIMATION OF THE LYAPUNOV SPECTRUM FROM ONE-DIMENSIONAL OBSERVATIONS USING NEURAL NETWORKS

2014 ◽  
pp. 30-34
Author(s):  
Vladimir Golovko
Keyword(s):  
Neural Network ◽  
Dynamical System ◽  
Neural Networks ◽  
Time Series ◽  
Lyapunov Spectrum ◽  
Network Approach ◽  
Neural Network Approach ◽  
One Dimensional ◽  
The Neural Network ◽  
Attractor Dynamics

This paper discusses the neural network approach for computing of Lyapunov spectrum using one dimensional time series from unknown dynamical system. Such an approach is based on the reconstruction of attractor dynamics and applying of multilayer perceptron (MLP) for forecasting the next state of dynamical system from the previous one. It allows for evaluating the Lyapunov spectrum of unknown dynamical system accurately and efficiently only by using one observation. The results of experiments are discussed.

scholarly journals A neural network approach for the simultaneous retrieval of volcanic ash parameters and SO<sub>2</sub> using MODIS data

2014 ◽  
Vol 7 (12) ◽  
pp. 4023-4047 ◽  
Author(s):  
A. Piscini ◽  
M. Picchiani ◽  
M. Chini ◽  
S. Corradini ◽  
L. Merucci ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Volcanic Eruption ◽  
Volcanic Ash ◽  
Thermal Infrared ◽  
Complete Characterization ◽  
Network Approach ◽  
Neural Network Approach ◽  
The Neural Network ◽  
The Neural Networks

Abstract. In this work neural networks (NNs) have been used for the retrieval of volcanic ash and sulfur dioxide (SO2) parameters based on Moderate Resolution Imaging Spectroradiometer (MODIS) multispectral measurements. Different neural networks were built in order for each parameter to be retrieved, for experimenting with different topologies and evaluating their performances. The neural networks' capabilities to process a large amount of new data in a very fast way have been exploited to propose a novel applicative scheme aimed at providing a complete characterization of eruptive products. As a test case, the May 2010 Eyjafjallajókull eruption has been considered. A set of seven MODIS images have been used for the training and validation phases. In order to estimate the parameters associated to the volcanic eruption, such as ash mass, effective radius, aerosol optical depth and SO2 columnar abundance, the neural networks have been trained using the retrievals from well-known algorithms. These are based on simulated radiances at the top of the atmosphere and are estimated by radiative transfer models. Three neural network topologies with a different number of inputs have been compared: (a) three thermal infrared MODIS channels, (b) all multispectral MODIS channels and (c) the channels selected by a pruning procedure applied to all MODIS channels. Results show that the neural network approach is able to estimate the volcanic eruption parameters very well, showing a root mean square error (RMSE) below the target data standard deviation (SD). The network built considering all the MODIS channels gives a better performance in terms of specialization, mainly on images close in time to the training ones, while the networks with less inputs reveal a better generalization performance when applied to independent data sets. In order to increase the network's generalization capability and to select the most significant MODIS channels, a pruning algorithm has been implemented. The pruning outcomes revealed that channel sensitive to ash parameters correspond to the thermal infrared, visible and mid-infrared spectral ranges. The neural network approach has been proven to be effective when addressing the inversion problem for the estimation of volcanic ash and SO2 cloud parameters, providing fast and reliable retrievals, important requirements during volcanic crises.

Investigation of the possibility of reconstructing strain diagrams using the neural network approach

2011 ◽  
Vol 47 (15) ◽  
pp. 1689-1695
Author(s):  
M. B. Bakirov ◽  
O. A. Mishulina ◽  
I. A. Kiselev ◽  
I. A. Kruglov
Keyword(s):  
Neural Network ◽  
Network Approach ◽  
Neural Network Approach ◽  
The Neural Network

An artificial neural network approach to discrete-event simulation

1995 ◽  
Vol 9 (1) ◽  
pp. 37-49 ◽  
Author(s):  
Ian Flood ◽  
Kenneth Worley
Keyword(s):  
Neural Network ◽  
Discrete Event Simulation ◽  
Network Architecture ◽  
Discrete Event ◽  
Network Approach ◽  
Neural Network Approach ◽  
Event Simulation ◽  
The Neural Network ◽  
Component Processes ◽  
And Performance

AbstractThis paper proposes and evaluates a neural network-based method for simulating manufacturing processes that exhibit both noncontinuous and stochastic behavior processes more conventionally modeled, using discrete-event simulation algorithms. The incentive for developing the technique is its potential for rapid execution of a simulation through parallel processing, and facilitation of the development and improvement of models particularly where there is limited theory describing the dependence between component processes. A brief introduction is provided to a radial-Gaussian neural network architecture and training process, the system adopted for the work presented in this paper. A description of the basic approach proposed for applying this technology to simulation is then described. This involves the use of a modularized neural network approach to model construction and the prediction of the occurrence of events using information retained from several previous states of the simulation. A class of earth-moving systems, comprising a push-dozer and a fleet of scrapers, is used as the basis for assessing the viability and performance of the proposed approach. A series of experiments show the neural network to be capable of both capturing the characteristic behavior and making an accurate prediction of production rates of scraper-based earth-moving systems. The paper concludes with an indication of some areas for further development and evaluation of the technique.

Modeling of Uncertain Nonlinear System With Z-Numbers

2021 ◽  
pp. 290-314
Author(s):  
Raheleh Jafari ◽  
Sina Razvarz ◽  
Alexander Gegov ◽  
Satyam Paul
Keyword(s):  
Neural Network ◽  
Nonlinear System ◽  
Nonlinear Systems ◽  
Network Approach ◽  
Uncertain Nonlinear System ◽  
Neural Network Approach ◽  
The Neural Network ◽  
Simulation Results ◽  
Fuzzy Equations

In order to model the fuzzy nonlinear systems, fuzzy equations with Z-number coefficients are used in this chapter. The modeling of fuzzy nonlinear systems is to obtain the Z-number coefficients of fuzzy equations. In this work, the neural network approach is used for finding the coefficients of fuzzy equations. Some examples with applications in mechanics are given. The simulation results demonstrate that the proposed neural network is effective for obtaining the Z-number coefficients of fuzzy equations.

scholarly journals The Behavior of VLF/LF Variations Associated with Geomagnetic Activity, Earthquakes, and the Quiet Condition Using a Neural Network Approach

Entropy ◽  
2018 ◽  
Vol 20 (9) ◽  
pp. 691 ◽  
Author(s):  
Irina Popova ◽  
Alexandr Rozhnoi ◽  
Maria Solovieva ◽  
Danila Chebrov ◽  
Masashi Hayakawa
Keyword(s):  
Neural Network ◽  
Radio Wave ◽  
Seismic Activity ◽  
Low Frequency ◽  
Magnetic Storms ◽  
Network Approach ◽  
Neural Network Approach ◽  
The Neural Network ◽  
Different Types ◽  
Parameters Of Seismicity

The neural network approach is proposed for studying very-low- and low-frequency (VLF and LF) subionospheric radio wave variations in the time vicinities of magnetic storms and earthquakes, with the purpose of recognizing anomalies of different types. We also examined the days with quiet geomagnetic conditions in the absence of seismic activity, in order to distinguish between the disturbed signals and the quiet ones. To this end, we trained the neural network (NN) on the examples of the representative database. The database included both the VLF/LF data that was measured during four-year monitoring at the station in Petropavlovsk-Kamchatsky, and the parameters of seismicity in the Kuril-Kamchatka and Japan regions. It was shown that the neural network can distinguish between the disturbed and undisturbed signals. Furthermore, the prognostic behavior of the VLF/LF variations indicative of magnetic and seismic activity has a different appearance in the time vicinity of the earthquakes and magnetic storms.

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