Proceedings of the 16th International IEEE Annual Conference on Intelligent Transportation Systems, October 2013
A Bayesian Hierarchical Model is presented to
estimate route choice preferences between OD pairs... more A Bayesian Hierarchical Model is presented to
estimate route choice preferences between OD pairs. The
methodology adopted utilizes both Origin-Destination (OD)
information and traffic counts observed on some of the links
in the network to estimate route choice probabilities. Route
choice preferences are represented by multinomial distributions
and estimated via a Markov Chain Monte Carlo (MCMC)
algorithm. The proposed model takes into account measurement
errors in the link counts, the uncertanties present in OD data
and alternative routes choices both inside or outside the network
of study. The proposed method is validated on both a synthetic
example and the traffic network of Malta.
The modelling and analysis of spatiotemporal
behaviour is receiving wide-spread attention
due t... more The modelling and analysis of spatiotemporal
behaviour is receiving wide-spread attention
due to its applicability to various scientific fields
such as the mapping of the electrical activity in the
human brain, the spatial spread of pandemics and the
diffusion of hazardous pollutants. Nevertheless, due
to the complexity of the dynamics describing these
systems and the vast datasets of the measurements
involved, efficient computational methods are required
to obtain representative mathematical descriptions
of such behaviour. In this work, a computationally
efficient method for the estimation of heterogeneous
spatio-temporal autoregressive models is proposed
and tested on a dataset of air pollutants measured
over the Maltese islands. Results will highlight the
computation advantages of the proposed methodology
and the accuracy of the predictions obtained through
the estimated model.
Paper presented at the National Conference entitled Sustainable Mobility in Malta, organised by the Institute for Sustainable Development, University of Malta, 30th November 2012
A Bayesian Hierarchical Model is presented to estimate route choice preferences between OD pairs.... more A Bayesian Hierarchical Model is presented to estimate route choice preferences between OD pairs. The methodology adopted utilizes both Origin-Destination (OD) information and traffic counts observed on some of the links in the network to estimate route choice probabilities. Route choice preferences are represented by multinomial distributions and estimated via a Markov Chain Monte Carlo (MCMC) algorithm. The proposed model takes into account measurement errors in the link counts, the uncertanties present in OD data and alternative routes choices both inside or outside the network of study. The proposed method is validated on both a synthetic example and the traffic network of Malta.
Paper presented at 11th International Conference of GeoComputation 2011, organised by the GeoComputation Community, University College London, UK, 20th – 22nd July.
Air pollution measurements display patterns over space and time allowing for spatio-temporal
mod... more Air pollution measurements display patterns over space and time allowing for spatio-temporal
modelling, through which pollution concentrations and trends can be analysed. In Malta, the MEPA
(Malta Environment and Planning Authority) collects monthly averaged data for various pollutants
from a network of 123 diffusion tubes located around the Islands (Figure 1). This preliminary study
uses data associated with traffic, that is nitrogen dioxide (NO2) and benzene, collected monthly
between the period 2004 and 2010 with the objectives to i) develop a computationally efficient method
that best describes the data; ii) determine the level of dependency of each site on neighbouring ones
and iii) identify any factors that affect the behaviour and patterns of pollution. Results will show that
generally there is a low spatial dependency between close sites, thus implying that local sources, rather
than diffusion, have a predominant effect on the measurements. This analysis will prove valuable in
MEPA’s redistribution exercise of the diffusion tube network to determine which sites are necessary to
retain and which sites can be removed without significantly affecting the information gathered.
Proceedings of the 16th International IEEE Annual Conference on Intelligent Transportation Systems, October 2013
A Bayesian Hierarchical Model is presented to
estimate route choice preferences between OD pairs... more A Bayesian Hierarchical Model is presented to
estimate route choice preferences between OD pairs. The
methodology adopted utilizes both Origin-Destination (OD)
information and traffic counts observed on some of the links
in the network to estimate route choice probabilities. Route
choice preferences are represented by multinomial distributions
and estimated via a Markov Chain Monte Carlo (MCMC)
algorithm. The proposed model takes into account measurement
errors in the link counts, the uncertanties present in OD data
and alternative routes choices both inside or outside the network
of study. The proposed method is validated on both a synthetic
example and the traffic network of Malta.
The modelling and analysis of spatiotemporal
behaviour is receiving wide-spread attention
due t... more The modelling and analysis of spatiotemporal
behaviour is receiving wide-spread attention
due to its applicability to various scientific fields
such as the mapping of the electrical activity in the
human brain, the spatial spread of pandemics and the
diffusion of hazardous pollutants. Nevertheless, due
to the complexity of the dynamics describing these
systems and the vast datasets of the measurements
involved, efficient computational methods are required
to obtain representative mathematical descriptions
of such behaviour. In this work, a computationally
efficient method for the estimation of heterogeneous
spatio-temporal autoregressive models is proposed
and tested on a dataset of air pollutants measured
over the Maltese islands. Results will highlight the
computation advantages of the proposed methodology
and the accuracy of the predictions obtained through
the estimated model.
Paper presented at the National Conference entitled Sustainable Mobility in Malta, organised by the Institute for Sustainable Development, University of Malta, 30th November 2012
A Bayesian Hierarchical Model is presented to estimate route choice preferences between OD pairs.... more A Bayesian Hierarchical Model is presented to estimate route choice preferences between OD pairs. The methodology adopted utilizes both Origin-Destination (OD) information and traffic counts observed on some of the links in the network to estimate route choice probabilities. Route choice preferences are represented by multinomial distributions and estimated via a Markov Chain Monte Carlo (MCMC) algorithm. The proposed model takes into account measurement errors in the link counts, the uncertanties present in OD data and alternative routes choices both inside or outside the network of study. The proposed method is validated on both a synthetic example and the traffic network of Malta.
Paper presented at 11th International Conference of GeoComputation 2011, organised by the GeoComputation Community, University College London, UK, 20th – 22nd July.
Air pollution measurements display patterns over space and time allowing for spatio-temporal
mod... more Air pollution measurements display patterns over space and time allowing for spatio-temporal
modelling, through which pollution concentrations and trends can be analysed. In Malta, the MEPA
(Malta Environment and Planning Authority) collects monthly averaged data for various pollutants
from a network of 123 diffusion tubes located around the Islands (Figure 1). This preliminary study
uses data associated with traffic, that is nitrogen dioxide (NO2) and benzene, collected monthly
between the period 2004 and 2010 with the objectives to i) develop a computationally efficient method
that best describes the data; ii) determine the level of dependency of each site on neighbouring ones
and iii) identify any factors that affect the behaviour and patterns of pollution. Results will show that
generally there is a low spatial dependency between close sites, thus implying that local sources, rather
than diffusion, have a predominant effect on the measurements. This analysis will prove valuable in
MEPA’s redistribution exercise of the diffusion tube network to determine which sites are necessary to
retain and which sites can be removed without significantly affecting the information gathered.
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estimate route choice preferences between OD pairs. The
methodology adopted utilizes both Origin-Destination (OD)
information and traffic counts observed on some of the links
in the network to estimate route choice probabilities. Route
choice preferences are represented by multinomial distributions
and estimated via a Markov Chain Monte Carlo (MCMC)
algorithm. The proposed model takes into account measurement
errors in the link counts, the uncertanties present in OD data
and alternative routes choices both inside or outside the network
of study. The proposed method is validated on both a synthetic
example and the traffic network of Malta.
behaviour is receiving wide-spread attention
due to its applicability to various scientific fields
such as the mapping of the electrical activity in the
human brain, the spatial spread of pandemics and the
diffusion of hazardous pollutants. Nevertheless, due
to the complexity of the dynamics describing these
systems and the vast datasets of the measurements
involved, efficient computational methods are required
to obtain representative mathematical descriptions
of such behaviour. In this work, a computationally
efficient method for the estimation of heterogeneous
spatio-temporal autoregressive models is proposed
and tested on a dataset of air pollutants measured
over the Maltese islands. Results will highlight the
computation advantages of the proposed methodology
and the accuracy of the predictions obtained through
the estimated model.
modelling, through which pollution concentrations and trends can be analysed. In Malta, the MEPA
(Malta Environment and Planning Authority) collects monthly averaged data for various pollutants
from a network of 123 diffusion tubes located around the Islands (Figure 1). This preliminary study
uses data associated with traffic, that is nitrogen dioxide (NO2) and benzene, collected monthly
between the period 2004 and 2010 with the objectives to i) develop a computationally efficient method
that best describes the data; ii) determine the level of dependency of each site on neighbouring ones
and iii) identify any factors that affect the behaviour and patterns of pollution. Results will show that
generally there is a low spatial dependency between close sites, thus implying that local sources, rather
than diffusion, have a predominant effect on the measurements. This analysis will prove valuable in
MEPA’s redistribution exercise of the diffusion tube network to determine which sites are necessary to
retain and which sites can be removed without significantly affecting the information gathered.
estimate route choice preferences between OD pairs. The
methodology adopted utilizes both Origin-Destination (OD)
information and traffic counts observed on some of the links
in the network to estimate route choice probabilities. Route
choice preferences are represented by multinomial distributions
and estimated via a Markov Chain Monte Carlo (MCMC)
algorithm. The proposed model takes into account measurement
errors in the link counts, the uncertanties present in OD data
and alternative routes choices both inside or outside the network
of study. The proposed method is validated on both a synthetic
example and the traffic network of Malta.
behaviour is receiving wide-spread attention
due to its applicability to various scientific fields
such as the mapping of the electrical activity in the
human brain, the spatial spread of pandemics and the
diffusion of hazardous pollutants. Nevertheless, due
to the complexity of the dynamics describing these
systems and the vast datasets of the measurements
involved, efficient computational methods are required
to obtain representative mathematical descriptions
of such behaviour. In this work, a computationally
efficient method for the estimation of heterogeneous
spatio-temporal autoregressive models is proposed
and tested on a dataset of air pollutants measured
over the Maltese islands. Results will highlight the
computation advantages of the proposed methodology
and the accuracy of the predictions obtained through
the estimated model.
modelling, through which pollution concentrations and trends can be analysed. In Malta, the MEPA
(Malta Environment and Planning Authority) collects monthly averaged data for various pollutants
from a network of 123 diffusion tubes located around the Islands (Figure 1). This preliminary study
uses data associated with traffic, that is nitrogen dioxide (NO2) and benzene, collected monthly
between the period 2004 and 2010 with the objectives to i) develop a computationally efficient method
that best describes the data; ii) determine the level of dependency of each site on neighbouring ones
and iii) identify any factors that affect the behaviour and patterns of pollution. Results will show that
generally there is a low spatial dependency between close sites, thus implying that local sources, rather
than diffusion, have a predominant effect on the measurements. This analysis will prove valuable in
MEPA’s redistribution exercise of the diffusion tube network to determine which sites are necessary to
retain and which sites can be removed without significantly affecting the information gathered.