Spatio-Temporal Population Density and Spatial Dynamic Spectrum Allocation

Introduction The scarcity of available radio spectrum limits theextension of the modern wireless communication. Oneapproach to alleviate this limitation is the dynamicspectrum allocation (DSA) at network level, which isable to adapt to the spatial and temporal variation intraffic load[1]. The concept of DSA was first presentedin the DARPA (Defense Advanced Research ProjectsAgency) XG Program[2], with the aim to increase thespectral efficiency by spectrum sharing among radios.Due to the…     

An appropriate unidirectional wave spectrum model for the Strait of Hormuz

The objective of this paper is to introduce an appropriate unidirectional wave spectrum model for the Strait of Hormuz. The research is focused on assessing performance of standard wave spectrum models in the region. By evaluating such models based on valuable measurement data recently published, the calibration procedure has been conducted on such standard models to reach a better concordance between a modified standard spectral model and observed field spectra. The calibration is performed initially with respect to four distinct directions related to four available measurement stations. So, it results in four sets of coefficients for a nominated model. Next, it is continued to reach just one model insensitive to directions. Results clearly showed that the International Towing Tank Conference (ITTC) model is more appropriate than Joint North Sea Wave Project (JONSWAP) and Pierson and Moskowitz (PM) models in this area, even without any calibration. However, modifications have been successful on improving the conformity of the model.     

Survey and empirical evaluation of nonhomogeneous arrival process models with taxi data

Arrival processes are important inputs to many transportation system functions, such as vehicle prepositioning, taxi dispatch, bus holding strategies, and dynamic pricing. We conduct a comprehensive survey of the literature which shows that many transport systems employ basic homogeneous arrival process models or static nonhomogeneous processes. We conduct an empirical experiment to compare five state of the art arrival process short term prediction models using a common transportation system data set: New York taxi passenger pickups in 2013. Pickup data is split between 672 observations for model estimation and 96 observations for validation. From our experiment, we obtain evidence to support a recent model called FM‐IntGARCH, which is able to combine the benefits of both time series models and discrete count processes. Using a set of seven performance metrics from the literature, FM‐IntGARCH is shown to outperform the offline models—seasonal factor method, piecewise linear model—as well as the online models—ARIMA, Gaussian Cox process. Implications for operating data‐driven “smart” transit systems and urban informatics are discussed. Copyright © 2016 John Wiley & Sons, Ltd.