Showing papers by "Haibo Chen published in 2009"

Field Deployments of the MESSAGE System for Environmental Monitoring

Abstract: This paper describes how the technologies developed in the MESSAGE project have been deployed in a series of real-world experiments to examine the relationship between transport and air pollution. Three different sensor systems have been developed to allow deployment on infrastructure, people, and vehicles for both short- and long-term studies. This paper describes the field trials conducted using each of these systems in turn. The initial conclusions regarding the coordinated use of these sensors in the management of transport and air pollution are presented.

Examining the Effect of Attribute Representation on Preference Uncertainty

Abstract: The representation of attributes in choice experiments has significant implications for valuation as well as forecasting as attributes represented in a manner not easily understood by the respondents can affect their decision making. This paper examines the effect of different methods of attribute representation on preference uncertainty. A Stated Preference (SP) survey was conducted to evaluate the importance of apartment characteristics in residential choice in the Telheiras area of Lisbon. Different levels of noise, sunlight, view and housing service charge were offered to the respondents to evaluate the impact of these on residential choice. The SP survey was classified into two independent experiments with different representations of noise, sunlight and view. In the first experiment, these attributes were represented using the location method while linguistic representation was used in the second experiment. For the two experiments, binary choices as well as five point Likert scale was offered to capture preference uncertainty. Nested logit and Error Components model are used to analyse the responses gathered from the five point Likert scale while binary logit model is used for the binary choices. The results obtained from these models are compared across the two experiments to analyse the effect of different attribute representation techniques on respondent preference uncertainty.

Multivariate Extreme Value Modeling of Air Pollution Concentrations and Analysis of Traffic and Meteorological Impacts

Abstract: Road traffic emission is accounted for high proportions of many harmful pollutants. Exact prediction of pollution episodes occurrence, strength and duration is a formidable problem due to the combination of many complex physical and chemical processes involved. This underpins the need for the development of sophisticated statistical methods in order to facilitate prediction of high pollution concentrations and to understand better their cause. In this paper, joint extremes of ozone (O3), nitrogen dioxide (NO2) and nitrogen oxide (NO) have been investigated using the peaks-over-threshold (POT) approach extended by treating the parameters of the generalized Pareto distribution as certain functions of traffic and meteorological factors (such as traffic flow, speed, air temperature, relative humidity, wind speed and wind direction). The impact of these factors on the joint distribution of extreme values has been estimated, and in particular combinations of traffic and meteorological conditions that determine persistent interdependence between extremes of different pollutants have been identified using the copula method. Due to the complexity of the model, the Markov Chain Monte Carlo (MCMC) method has been employed for the parameter estimation. Appropriate goodness-of-fit tests confirm that the model proposed in this study provides accurate estimation of joint extremes in the air pollution concentrations.