Hybrid Time-Series Forecasting Models for Traffic Flow Prediction

Traffic flow forecast is critical in today’s transportation system since it is necessary to construct a traffic plan in order to determine a travel route. The goal of this research is to use time-series forecasting models to estimate future traffic in order to reduce traffic congestion on roadways. Minimising prediction error is the most difficult task in traffic prediction. In order to anticipate future traffic flow, the system also requires real-time data from vehicles and roadways. A hybrid autoregressive integrated moving av-erage with multilayer perceptron (ARIMA-MLP) model and a hybrid autoregressive integrated moving average with recurrent neural network (ARIMA-RNN) model are proposed in this paper to address these difficulties. The transportation data are used from the UK Highways data-set. The time-series data are preprocessed using a random walk model. The forecasting models autoregressive inte-grated moving average (ARIMA), recurrent neural net-work (RNN), and multilayer perceptron (MLP) are trained and tested. In the proposed hybrid ARIMA-MLP and ARI-MA-RNN models, the residuals from the ARIMA model are used to train the MLP and RNN models. Then the ef-ficacy of the hybrid system is assessed using the metrics MAE, MSE, RMSE and R2 (peak hour forecast-0.936763, non-peak hour forecast-0.87638 on ARIMA-MLP model and peak hour forecast-0.9416466, non-peak hour fore-cast-0.931917 on ARIMA-RNN model)

Studies on nucleotidases in plants: isolation and properties of the monomeric form of the crystalline and homogeneous mung bean nucleotide pyrophosphatase

Mung bean nucleotide pyrophosphatase isolated in a crystalline and homogeneous form as a dimer with a molecular weight of 65000 was converted by AMP into a tetramer. The tetramer was enzymatically active with altered kinetic properties. This conversion of the dimeric form by AMP to a tetrameric one was prevented by treating the dimer with p-hydroxymercuribenzoate. The molecular weight of the p-hydroxymercuribenzoate-treated enzyme was determined to be 32700 by a combination of Stokes' radius (2.4 nm) and sedimentation velocity (S20,w = 1.9 S), by thin-layer gel chromatography on superfine Sephadex G-200 and by sodium dodecylsulfate/polyacrylamide gel electrophoresis. The monomer obtained by treatment of the native enzyme with p-hydroxymercuribenzoate was isolated by passage of the dissociated enzyme through a column of Biogel P-200. The monomer was optimally active at 37°C, whereas the dimer and tetramer were active at 49°C. All the three enzyme forms were maximally active at pH 9.4. The Km and V (measured as rate of FAD hydrolysis per mg protein) for FAD of the three enzyme forms were for the monomer, 0.5mM and 7.0 μmolmin-1, for the dimer, 0.25mM and 3.3 μmolmin-1 and for the tetramer, 0.58mM and 2.5 μmolmin-1, respectively. The time course of the reaction of the monomer was linear and comparable to the initial fast rate of the dimer. The monomer was not converted to a tetramer or a dimer on the addition of AMP; and it was irreversibly inhibited by urea and EDTA. ATP and ADP were noncompetitive inhibitors of the monomer

Compound Conway-Maxwell Poisson Gamma Distribution: Properties and Estimation

The distribution of a random sum of random events is called a compound distribution. It involves a counting (discrete) distribution to model the number of occurrences of the random event in a fixed time period and a continuous distribution to model the outcome of the random event. It has applications in the fields of actuarial sciences, meteorology etc. For example, in modelling insurance loss amounts through compound distributions, the number of claims and the claim amounts are used to calculate the total claim amount of a portfolio. The number of claims is modelled through a discrete distribution and the claim amounts are modelled through continuous distributions. Generally, Poisson distribution is used in compound models as the discrete distribution and such models are known as compound Poisson models. However, the equi-dispersion property of the Poisson distribution hinders its application in scenarios where the underlying count data is either over- or under-dispersed. In this paper, a two-parameter Poisson distribution, namely, Conway-Maxwell Poisson (CMP) distribution, which handles both over- and under-dispersed data, is considered as the counting distribution, and the corresponding compound CMP distribution is developed. Some mathematical properties of the distribution are derived and a methodology to estimate the parameters using the likelihood approach is proposed. A numerical illustration of the proposed methodology is given through a simulation study. An application of the compound CMP model is illustrated through transportation security administration (TSA) insurance claim data. Also, the estimation of the risk measures associated with the TSA claim data is discussed

Low temperature deformation of the R-phase in a NiTiFe shape memory alloy

Deformation in the P3 phase (R-phase) of NiTiFe was investigated by in situ neutron diffraction during compressive loading at cryogenic temperatures. At 216 K, upon loading the R-phase detwinned and subsequently underwent a reversible stress-induced transformation to the B19\u27 phase (martensite). At 92 K on the other hand, detwinning was suppressed and the stress-induced martensite formed did not transform back upon unloading. The experiments also directly observed a hitherto theoretically predicted B33 phase. Rietveld refinement of the neutron diffraction spectra were used to determine lattice parameters of the B33 and R-phases. Plane-specific elastic moduli were also determined for the R-phase

An Eight-Term Novel Four-Scroll Chaotic System with Cubic Nonlinearity and its Circuit Simulation

This research work proposes an eight-term novel four-scroll chaotic system with cubic nonlinearity and analyses its fundamental properties such as dissipativity, equilibria, symmetry and invariance, Lyapunov exponents and KaplanYorke dimension. The phase portraits of the novel chaotic system, which are obtained in this work by using MATLAB, depict the four-scroll attractor of the system. For the parameter values and initial conditions chosen in this work, the Lyapunov exponents of the novel four-scroll chaotic system are obtained as L1 = 0.75335, L2 = 0 and L3 = −22.43304. Also, the Kaplan-Yorke dimension of the novel four-scroll chaotic system is obtained as DKY = 2.0336. Finally, an electronic circuit realization of the novel four-scroll chaotic system is presented by using SPICE to confirm the feasibility of the theoretical model

Applications of quadrivariate exponential distribution to a three-unit warm standby system with dependent structure

Two-unit warm standby systems have been elaborately dealt within the literature. However, the study of standby systems with more than two units, though very relevant in state-of-the-art practical situations, has received little attention because of mathematical intricacies involved in analyzing them. Also, such systems have been studied assuming: (i) the lifetime or repair time of the units to be exponential, or (ii) the life-time and repair time to be independent. The present contribution is an improvement in the state-of-the-art in the sense that three-unit warm standby system with dependent structure is shown to be capable of comprehensive analysis.http://www.tandfonline.com/loi/lsta202018-03-20hj2018Industrial and Systems Engineerin

On the reaction of keto acids with amino acids

This article does not have an abstract

Minimizing Acquisition Maximizing Inference -- A demonstration on print error detection

Is it possible to detect a feature in an image without ever looking at it? Images are known to have sparser representation in Wavelets and other similar transforms. Compressed Sensing is a technique which proposes simultaneous acquisition and compression of any signal by taking very few random linear measurements (M). The quality of reconstruction directly relates with M, which should be above a certain threshold for a reliable recovery. Since these measurements can non-adaptively reconstruct the signal to a faithful extent using purely analytical methods like Basis Pursuit, Matching Pursuit, Iterative thresholding, etc., we can be assured that these compressed samples contain enough information about any relevant macro-level feature contained in the (image) signal. Thus if we choose to deliberately acquire an even lower number of measurements - in order to thwart the possibility of a comprehensible reconstruction, but high enough to infer whether a relevant feature exists in an image - we can achieve accurate image classification while preserving its privacy. Through the print error detection problem, it is demonstrated that such a novel system can be implemented in practise

Tunka Advanced Instrument for cosmic rays and Gamma Astronomy

The paper is a script of a lecture given at the ISAPP-Baikal summer school in 2018. The lecture gives an overview of the Tunka Advanced Instrument for cosmic rays and Gamma Astronomy (TAIGA) facility including historical introduction, description of existing and future setups, and outreach and open data activities.Comment: Lectures given at the ISAPP-Baikal Summer School 2018: Exploring the Universe through multiple messengers, 12-21 July 2018, Bol'shie Koty, Russi

Hidden attractors in fundamental problems and engineering models

Recently a concept of self-excited and hidden attractors was suggested: an attractor is called a self-excited attractor if its basin of attraction overlaps with neighborhood of an equilibrium, otherwise it is called a hidden attractor. For example, hidden attractors are attractors in systems with no equilibria or with only one stable equilibrium (a special case of multistability and coexistence of attractors). While coexisting self-excited attractors can be found using the standard computational procedure, there is no standard way of predicting the existence or coexistence of hidden attractors in a system. In this plenary survey lecture the concept of self-excited and hidden attractors is discussed, and various corresponding examples of self-excited and hidden attractors are considered