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Discrete choice models are widely used to explain transportation behaviors, including a household's decision to own a car. They show how some distinct choice of human behavior or preference influences a decision. They are also used to project future demand estimates to support policy exploration. This latter use for prediction is indirectly aligned with and conditional to the model's estimation which...
This paper presents a data-driven model to estimate the traversal speed of public transport on urban arterials under non-recurring congestion. We group unidirectional links of an arterial and use the concept of Macroscopic Fundamental Diagram to achieve a smooth speed-density relationship along the arterial. The methodology comprises two main steps: first, developing the model and estimating fit parameters...
Per-flow counting for big network data streams is a fundamental problem in various network applications such as traffic monitoring, load balancing, capacity planning, etc. Traditional research focused on designing compact data structures to estimate flow sizes from the beginning of the data stream (i.e., landmark window model). However, for many applications, the most recent elements of a stream are...
As an energy source of mobile system including robot, Li battery is extensively applied. To increase durability and efficient usage of battery, it is to require the rigorous management to battery controller, BMS,. Of them, the exact estimation of SOC is very important for correct usage. In this paper, introducing the design of BMS simply and, as a estimation method of SOC, dual extended Kalman filter...
The optimization of a model that expresses time series data for a given period is a problem associated with the development of a regression model that estimates future data on the extension of the past data time series. This is a two-step optimization problem where the order of past data used in the regression model (number of orders of the solution space) is decided, and weighted coefficients for...
This paper uses high-frequency intraday electricity prices from the EPEX market to estimate and forecast realised volatility. Variation is broken down into jump and continuous components using quadratic variation theory. Then several heterogeneous autoregressive models are estimated for the logarithmic and standard deviation transformations. GARCH structures are included in the error terms of the...
This paper provides a method to estimate Thévenin equivalent models for voltage stability analysis of a wind hub with about 600 MW of rated generation. The estimation uses 24-hour SCADA data on two medium voltage transmission lines. The estimation is based on a recently developed method whose objective is to find, from long data records, a fixed Thévenin reactance value that yields a mostly fixed...
A robust estimation of road course and traffic lanes is an essential part of environment perception for next generations of Advanced Driver Assistance Systems and development of self-driving vehicles. In this paper, a flexible method for modeling multiple lanes in a vehicle in real time is presented. Information about traffic lanes, derived by cameras and other environmental sensors, that is represented...
Recent empirical evidence from research on temporal networks has shown that the time constraints imposed on individual interactions are crucial for understanding the generic structure and dynamics of networks. A desirable but challenging task is sampling the waiting time distribution (WTD) associated with their internal interactions, a defining feature that reflects the added time dimension of temporal...
Condition monitoring data have been widely used to evaluate the health state and reliability, as well as estimate the remaining useful life (RUL) for degrading systems. Among various degradation modeling and RUL estimating methods, Wiener process based models is recognized by both scholars and engineers as the one of the most effect tools, and thus becomes very popular nowadays. In this paper, a prognostic...
The categorical data that have natural ordering between categories are termed ordinal data, which are pervasive in numerous areas, including discrete sensor readings, metering data or preference options. Though aggregating such ordinal data from the population is facilitating plenty of crowdsourcing applications, contributing such data is privacy risky and may reveal sensitive information (e.g. locations,...
Photoplethysmographic signals are synthesized using a Fourier representation with a fundamental and 2 harmonic components, and an accurate method for parameter estimation using this synthesis model is discussed. The estimated parameters enable to determine physiologically meaningful features related to arterial stiffness, heart rate variability, blood pressure variations, etc. The method allows signal...
The paper considers the problem of decision support systems constructing for solving the problems of modeling and estimating selected types of risks with the possibility for application of alternative data processing techniques, modeling and estimation of parameters and states for the processes under study. The system proposed has a modular architecture that provides a possibility for easy extension...
Active assistive mobility systems are largely limited to a-priori mapped environments, whereas their reactive assistive counterparts are in general location independent and focus on the provision of collision avoidance in the immediate space surrounding the platform. This paper presents a framework capable of providing active short-term navigation, combining the intelligence of active assistance with...
In Synthetic Biology, the idea of using feedback control for the mitigation of perturbations to gene regulatory networks due to disease and environmental disturbances is gaining popularity. To facilitate the design of such synthetic control circuits, a suitable model that captures the relevant dynamics of the gene regulatory network is essential. Traditionally, Michaelis-Menten models with Hill-type...
In the paper are observed methods for synthesis of the information model of the systems' evolution for the variation properties of their parameters. It is proposed to carry out the variations description based on entropy potentials values. As a result, it is possible to obtain compact and representative models in various situations with the original data. The synthesis of these models requires the...
In the present study we conduct analysis of methods and models of origin destination matrices reconstruction with regard to urban transport processes, we point out their advantages and disadvantages. As a result of the analysis the relational method of OD matrices reconstruction is chosen and the trilinear model (EVA) is considered as the most applicable for OD matrices reconstruction. To show the...
Location Model is a classification model that capable to deal with mixtures of binary and continuous variables simultaneously. The binary variables create segmentation in the groups called cells whilst the continuous variables measure the differences between groups based on information inside the cells. It is important to note that location model is biased and even impossible to be constructed when...
Driving a process to optimal conditions under various uncertainties is a key issue for meeting objectives of productivity and quality of batch or fed-batch product. To overcome a limitation of two-step approaches unable to cope with nonparametric or large uncertainty, several gradient based iterative optimization methods have been proposed. Among these, latent variable model based approaches have...
Statistical modeling methods have been used for estimating difficult-to-measure quality variables by using easy-to-measure process variables. The estimation performance of a statistical model depends on the quality and quantity of data. To build a better model when the amount of the data is limited, joint-Y partial least squares (JY-PLS) was proposed. JY-PLS can concurrently use the data from the...
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