Predicting real-time roadside CO and NO2 concentrations using neural networks

The main aim of this paper is to develop a model based on neural network (NN) theory to estimate real-time roadside CO and $hbox{NO}_{2}$ concentrations using traffic and meteorological condition data. The location of the study site is at a road intersection in Melton Mowbray, which is a town in Leicestershire, U.K. Several NNs, which can be classified into three types, namely, the multilayer perceptron, the radial basis function, and the modular network, were developed to model the nonlinear relationships that exist in the pollutant concentrations. Their performances are analyzed and compared. The transferability of the developed models is studied using data collected from a road intersection in another city. It was concluded that all NNs provide reliable estimates of pollutant concentrations using limited information and noisy data

Predicting real-time roadside CO and NO2 concentrations using neural networks

The main aim of this paper is to develop a model based on neural network (NN) theory to estimate real-time roadside CO and $hbox{NO}_{2}$ concentrations using traffic and meteorological condition data. The location of the study site is at a road intersection in Melton Mowbray, which is a town in Leicestershire, U.K. Several NNs, which can be classified into three types, namely, the multilayer perceptron, the radial basis function, and the modular network, were developed to model the nonlinear relationships that exist in the pollutant concentrations. Their performances are analyzed and compared. The transferability of the developed models is studied using data collected from a road intersection in another city. It was concluded that all NNs provide reliable estimates of pollutant concentrations using limited information and noisy data

Decoding the urban grid: or why cities are neither trees nor perfect grids

In a previous paper (Figueiredo and Amorim, 2005), we introduced the continuity lines, a compressed description that encapsulates topological and geometrical properties of urban grids. In this paper, we applied this technique to a large database of maps that included cities of 22 countries. We explore how this representation encodes into networks universal features of urban grids and, at the same time, retrieves differences that reflect classes of cities. Then, we propose an emergent taxonomy for urban grids

Spatial Complex Network Analysis and Accessibility Indicators: the Case of Municipal Commuting in Sardinia, Italy

In this paper a contribution is presented with respect to accessibility indicators modelling for commuters moving through the municipalities of Sardinia, in Italy. In this case, spatial complex network analysis is integrated into the construction of accessibility measures: one of the most relevant outcomes of the first tool –the detection of shortest road paths and distances- is adopted as an input for the second in modelling accessibility indicators. Instead of Euclidean distances often adopted in the literature, shortest road distances are chosen, as commuting implies movements that are usually repeated daily and very likely subjected, even unconsciously, to space and time minimization strategies. In particular, two commuter accessibility indicators are constructed according to approaches based on a travel cost and a spatial interaction model with impedance function calibrated in exponential and in power form. The accessibility indicators are confronted each other and with relevant socio-economic and infrastructure characteristics of Sardinia. In addition, they are described, with respect to their spatial distribution and their different implications, when adopted in decision-making and planning. The travel cost based accessibility indicator has a municipal spatial distribution strongly influenced by the main road infrastructure of the Island. By contrast, spatial interaction model based accessibility indicators are more reliable, with respect to their capacity to confirm a leading socio-economic role of the municipalities comprehended in the metropolitan area of the capital town Cagliari

A validated methodology for the prediction of heating and cooling energy demand for buildings within the Urban Heat Island: Case-study of London

This is the post-print version of the final paper published in Solar Energy. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2010 Elsevier B.V.This paper describes a method for predicting air temperatures within the Urban Heat Island at discreet locations based on input data from one meteorological station for the time the prediction is required and historic measured air temperatures within the city. It uses London as a case-study to describe the method and its applications. The prediction model is based on Artificial Neural Network (ANN) modelling and it is termed the London Site Specific Air Temperature (LSSAT) predictor. The temporal and spatial validity of the model was tested using data measured 8 years later from the original dataset; it was found that site specific hourly air temperature prediction provides acceptable accuracy and improves considerably for average monthly values. It thus is a very reliable tool for use as part of the process of predicting heating and cooling loads for urban buildings. This is illustrated by the computation of Heating Degree Days (HDD) and Cooling Degree Hours (CDH) for a West–East Transect within London. The described method could be used for any city for which historic hourly air temperatures are available for a number of locations; for example air pollution measuring sites, common in many cities, typically measure air temperature on an hourly basis.EPSR