In this work, we take a closer look at the Vogt-Bailey (VB) index, proposed in Ref. [1] as a tool... more In this work, we take a closer look at the Vogt-Bailey (VB) index, proposed in Ref. [1] as a tool for studying local functional homogeneity in the human cortex. We interpret the VB index in terms of the minimum ratio cut, a weighted graph cut that indicates whether a network can easily be disconnected into two parts having a comparable number of nodes. In our case, the nodes of the network consist of a brain vertex/voxel and its nearest neighbours, and a given edge is weighted according to the affinity of the nodes it connects (as reflected by the modified Pearson correlation between their fMRI time series). Consequently, the minimum ratio cut quantifies the degree of similarity in local brain activity. We compare the performance of the VB index with that of the Regional Homogeneity (ReHo) algorithm, commonly used to assess whether voxels in close proximity have synchronised fMRI signals, and find that the VB index is uniquely placed to detect sharp changes in the (local) functional...
Traffic signal timing plays an important role in ensuring efficient flow and reduction of traffic... more Traffic signal timing plays an important role in ensuring efficient flow and reduction of traffic congestion. Fixed signal times work well when traffic conditions are consistent. However performance degrades when traffic conditions are subject to high demands or during unusual occurrences such as traffic incidents or unanticipated network obstructions, causing significant changes to the normal traffic conditions. To address these situations, several traffic-responsive systems were proposed. However in these cases, the controller parameters are typically set at installation and not adaptively tuned to changing traffic behaviour. Hence, in this work, a novel adaptive control system which can self-tune to respond to changing traffic conditions is developed, leading towards an autonomic system. This system makes use of a MPC based approach which can autonomously tune the controller to ensure good performance despite changing traffic conditions. Different norms were tested as objective functions for the optimization problem. Results highlight the effectiveness of the proposed traffic light timing controller.
A novel algorithm is presented for macro model estimation of the dynamics of traffic flow in a ju... more A novel algorithm is presented for macro model estimation of the dynamics of traffic flow in a junction having multiple input lanes for each turning direction. The proposed algorithm jointly estimates the states describing the traffic flow under different traffic conditions, together with model parameters and their uncertainties of the measurement and process noise. Use is made of the Expectation-Maximization methodology with a sliding window over time in order to obtain quasi real-time estimation.
16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013), 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.
Spatio-temporal models have the potential to represent a wide variety of dynamic behaviour such a... more Spatio-temporal models have the potential to represent a wide variety of dynamic behaviour such as the growth of bacteria, the dispersion of a pollutant or the changing spatial patterns in house prices. Classical methods for the simulation of such behaviours suffer from large computational demands due to their high dimensionality. Recent advances in spatio-temporal modelling have proposed a method based on a state-space representation of the spatio-temporal integro-difference equation. Although the dimension reduction obtained when using this model is significant, it is frequently not sufficient for online computation or rapid simulation. Thus this model is revisited in this work and a method for further dimension reduction based on a balanced realization of the state-space model is developed. The results will show that the computational cost reduction obtained is significant at the expense of a minor loss in accuracy.
2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), 2017
An online multidimensional self-estimation algorithm is developed to jointly estimate the paramet... more An online multidimensional self-estimation algorithm is developed to jointly estimate the parameters of a macroscopic model describing the traffic dynamics in a signalized junction under different traffic conditions, together with the state variables characterising traffic flow. The proposed novel methodology is based on the Expectation-Maximization algorithm and multidimensional Robbins-Monro stochastic approximation. The algorithm is validated on the geometry of a signalized 3-arm junction within the traffic network of Malta resulting in a mean percentage error of −0.965% on the parameter estimates. This is aimed to form part of an adaptive control loop for traffic light systems that is able to autonomously adjust to changing traffic conditions.
Due to the complexity of the interactions involved in various dynamic systems, known physical, bi... more Due to the complexity of the interactions involved in various dynamic systems, known physical, biological or chemical laws cannot adequately describe the dynamics behind these processes. The study of these systems thus depends on measurements often taken at various discrete spatial locations through time by noisy sensors. For this reason, scientists often necessitate interpolative, visualisation and analytical tools to deal with the large volumes of data common to these systems. The starting point of this study is the seminal research by C. Shannon on sampling and reconstruction theory and its various extensions. Based on recent work on the reconstruction of stochastic processes, this paper develops a novel real-time estimation method for nonstationary stochastic spatio-temporal behaviour based on the Integro-Difference Equation (IDE). This methodology is applied to collected marine pollution data from a Norwegian fjord. Comparison of the results obtained by the proposed method with...
2017 25th Mediterranean Conference on Control and Automation (MED), 2017
An online self-estimation algorithm is developed to jointly estimate the states describing the tr... more An online self-estimation algorithm is developed to jointly estimate the states describing the traffic flow dynamics in a signalized junction under different traffic conditions, together with model parameters and their uncertainties. The proposed novel methodology is based on the Expectation-Maximization algorithm and Robbins-Monro stochastic approximation. The algorithm is validated by simulating a typical signalized 4-arm junction. This algorithm could form part of an adaptive control loop for traffic light systems that is able to autonomously adjust to changing traffic conditions.
With the increase in the urban population witnessed in all major cities worldwide, efficient urba... more With the increase in the urban population witnessed in all major cities worldwide, efficient urban traffic planning, modeling and control are indispensable. In this paper the authors propose a novel macro-model for urban networks based on queuing theory and cast in a state-space representation. The model for each junction incorporates a switching mechanism capturing both the nonlinear dynamics of normal traffic conditions and the linear evolution of vehicle queues during junction block-back scenarios. Moreover, while competing models suffer a quadratic increase in computational cost with every added junction, the authors' model exhibits only a linear increase in dimensionality and thus significantly lowers its computational demands. The simulation results from the proposed model are validated against a standard micro-modelling simulation package. These results will demonstrate through a Monte Carlo simulation that given correct model parameters, the proposed macro-model can well...
... work a method from systems theory is used to further reduce the dimensionality of the ... Thu... more ... work a method from systems theory is used to further reduce the dimensionality of the ... Thus in this paper a simulation method based on balanced model reduction of an IDE ... [16] S. Gibson and B. Ninness, “Robust maximum-likelihood estimation of multivariable dynamic systems ...
Propagation Phase Contrast Synchrotron Microtomography (PPC-SRμCT) is the gold standard for non-i... more Propagation Phase Contrast Synchrotron Microtomography (PPC-SRμCT) is the gold standard for non-invasive and non-destructive access to internal structures of archaeological remains. In this analysis, the virtual specimen needs to be segmented to separate different parts or materials, a process that normally requires considerable human effort. In the Automated SEgmentation of Microtomography Imaging (ASEMI) project, we developed a tool to automatically segment these volumetric images, using manually segmented samples to tune and train a machine learning model. For a set of four specimens of ancient Egyptian animal mummies we achieve an overall accuracy of 94–98% when compared with manually segmented slices, approaching the results of off-the-shelf commercial software using deep learning (97–99%) at much lower complexity. A qualitative analysis of the segmented output shows that our results are close in term of usability to those from deep learning, justifying the use of these techniq...
Various alternative means of transportation are emerging aiming to combat traffic congestion. Amo... more Various alternative means of transportation are emerging aiming to combat traffic congestion. Among these solutions, car sharing and pooling schemes are currently gaining in popularity. Such schemes require computationally tractable algorithms for the efficient allocation of resources. Towards such an aim, the Variable Neighbourhood Search has emerged as the leading algorithmic solution. Aiming to speed up its convergence, this paper introduces two novelties to this algorithm. Results based on a realistic simulation scenario in a densely populated area demonstrate the added accuracy obtained using these novelties in a time-sensitive application.
In this work, we identify a problem with the process of volume-to-surface mapping of functional M... more In this work, we identify a problem with the process of volume-to-surface mapping of functional Magnetic Resonance Imaging (fMRI) data when interested in investigating local connectivity. We show that neighborhood correlations on the surface of the brain vary spatially with anatomical patterns (gyral structure), even when the underlying volumetric data are uncorrelated noise. This could potentially have impacted studies focusing upon local neighborhood connectivity. We explore the effects of this anomaly across varying data resolutions and surface mesh densities, and propose an approach to mitigate these unwanted effects.
IEEE Transactions on Intelligent Transportation Systems
An online dual estimation algorithm is developed to jointly estimate in real-time traffic quantit... more An online dual estimation algorithm is developed to jointly estimate in real-time traffic quantities such as queue lengths, occupancies and flows, as well as the parameters of a macroscopic model of a signalized junction. These parameters include turning ratios and saturation flows, together with model uncertainties. The proposed novel methodology is based on the Expectation-Maximization algorithm, modified for real-time estimation, with a Kalman filter implementing the expectation step and a multivariate gradient-based approach for the maximisation step. The algorithm is validated by simulating the typical signalized 3-arm and 4-arm junctions. This work is aimed to form a part of the adaptive control loops for traffic light systems that are able to autonomously adjust with changing traffic conditions, so as to ensure efficient vehicle flows.
In this work, we take a closer look at the Vogt-Bailey (VB) index, proposed in Ref. [1] as a tool... more In this work, we take a closer look at the Vogt-Bailey (VB) index, proposed in Ref. [1] as a tool for studying local functional homogeneity in the human cortex. We interpret the VB index in terms of the minimum ratio cut, a weighted graph cut that indicates whether a network can easily be disconnected into two parts having a comparable number of nodes. In our case, the nodes of the network consist of a brain vertex/voxel and its nearest neighbours, and a given edge is weighted according to the affinity of the nodes it connects (as reflected by the modified Pearson correlation between their fMRI time series). Consequently, the minimum ratio cut quantifies the degree of similarity in local brain activity. We compare the performance of the VB index with that of the Regional Homogeneity (ReHo) algorithm, commonly used to assess whether voxels in close proximity have synchronised fMRI signals, and find that the VB index is uniquely placed to detect sharp changes in the (local) functional...
Traffic signal timing plays an important role in ensuring efficient flow and reduction of traffic... more Traffic signal timing plays an important role in ensuring efficient flow and reduction of traffic congestion. Fixed signal times work well when traffic conditions are consistent. However performance degrades when traffic conditions are subject to high demands or during unusual occurrences such as traffic incidents or unanticipated network obstructions, causing significant changes to the normal traffic conditions. To address these situations, several traffic-responsive systems were proposed. However in these cases, the controller parameters are typically set at installation and not adaptively tuned to changing traffic behaviour. Hence, in this work, a novel adaptive control system which can self-tune to respond to changing traffic conditions is developed, leading towards an autonomic system. This system makes use of a MPC based approach which can autonomously tune the controller to ensure good performance despite changing traffic conditions. Different norms were tested as objective functions for the optimization problem. Results highlight the effectiveness of the proposed traffic light timing controller.
A novel algorithm is presented for macro model estimation of the dynamics of traffic flow in a ju... more A novel algorithm is presented for macro model estimation of the dynamics of traffic flow in a junction having multiple input lanes for each turning direction. The proposed algorithm jointly estimates the states describing the traffic flow under different traffic conditions, together with model parameters and their uncertainties of the measurement and process noise. Use is made of the Expectation-Maximization methodology with a sliding window over time in order to obtain quasi real-time estimation.
16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013), 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.
Spatio-temporal models have the potential to represent a wide variety of dynamic behaviour such a... more Spatio-temporal models have the potential to represent a wide variety of dynamic behaviour such as the growth of bacteria, the dispersion of a pollutant or the changing spatial patterns in house prices. Classical methods for the simulation of such behaviours suffer from large computational demands due to their high dimensionality. Recent advances in spatio-temporal modelling have proposed a method based on a state-space representation of the spatio-temporal integro-difference equation. Although the dimension reduction obtained when using this model is significant, it is frequently not sufficient for online computation or rapid simulation. Thus this model is revisited in this work and a method for further dimension reduction based on a balanced realization of the state-space model is developed. The results will show that the computational cost reduction obtained is significant at the expense of a minor loss in accuracy.
2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), 2017
An online multidimensional self-estimation algorithm is developed to jointly estimate the paramet... more An online multidimensional self-estimation algorithm is developed to jointly estimate the parameters of a macroscopic model describing the traffic dynamics in a signalized junction under different traffic conditions, together with the state variables characterising traffic flow. The proposed novel methodology is based on the Expectation-Maximization algorithm and multidimensional Robbins-Monro stochastic approximation. The algorithm is validated on the geometry of a signalized 3-arm junction within the traffic network of Malta resulting in a mean percentage error of −0.965% on the parameter estimates. This is aimed to form part of an adaptive control loop for traffic light systems that is able to autonomously adjust to changing traffic conditions.
Due to the complexity of the interactions involved in various dynamic systems, known physical, bi... more Due to the complexity of the interactions involved in various dynamic systems, known physical, biological or chemical laws cannot adequately describe the dynamics behind these processes. The study of these systems thus depends on measurements often taken at various discrete spatial locations through time by noisy sensors. For this reason, scientists often necessitate interpolative, visualisation and analytical tools to deal with the large volumes of data common to these systems. The starting point of this study is the seminal research by C. Shannon on sampling and reconstruction theory and its various extensions. Based on recent work on the reconstruction of stochastic processes, this paper develops a novel real-time estimation method for nonstationary stochastic spatio-temporal behaviour based on the Integro-Difference Equation (IDE). This methodology is applied to collected marine pollution data from a Norwegian fjord. Comparison of the results obtained by the proposed method with...
2017 25th Mediterranean Conference on Control and Automation (MED), 2017
An online self-estimation algorithm is developed to jointly estimate the states describing the tr... more An online self-estimation algorithm is developed to jointly estimate the states describing the traffic flow dynamics in a signalized junction under different traffic conditions, together with model parameters and their uncertainties. The proposed novel methodology is based on the Expectation-Maximization algorithm and Robbins-Monro stochastic approximation. The algorithm is validated by simulating a typical signalized 4-arm junction. This algorithm could form part of an adaptive control loop for traffic light systems that is able to autonomously adjust to changing traffic conditions.
With the increase in the urban population witnessed in all major cities worldwide, efficient urba... more With the increase in the urban population witnessed in all major cities worldwide, efficient urban traffic planning, modeling and control are indispensable. In this paper the authors propose a novel macro-model for urban networks based on queuing theory and cast in a state-space representation. The model for each junction incorporates a switching mechanism capturing both the nonlinear dynamics of normal traffic conditions and the linear evolution of vehicle queues during junction block-back scenarios. Moreover, while competing models suffer a quadratic increase in computational cost with every added junction, the authors' model exhibits only a linear increase in dimensionality and thus significantly lowers its computational demands. The simulation results from the proposed model are validated against a standard micro-modelling simulation package. These results will demonstrate through a Monte Carlo simulation that given correct model parameters, the proposed macro-model can well...
... work a method from systems theory is used to further reduce the dimensionality of the ... Thu... more ... work a method from systems theory is used to further reduce the dimensionality of the ... Thus in this paper a simulation method based on balanced model reduction of an IDE ... [16] S. Gibson and B. Ninness, “Robust maximum-likelihood estimation of multivariable dynamic systems ...
Propagation Phase Contrast Synchrotron Microtomography (PPC-SRμCT) is the gold standard for non-i... more Propagation Phase Contrast Synchrotron Microtomography (PPC-SRμCT) is the gold standard for non-invasive and non-destructive access to internal structures of archaeological remains. In this analysis, the virtual specimen needs to be segmented to separate different parts or materials, a process that normally requires considerable human effort. In the Automated SEgmentation of Microtomography Imaging (ASEMI) project, we developed a tool to automatically segment these volumetric images, using manually segmented samples to tune and train a machine learning model. For a set of four specimens of ancient Egyptian animal mummies we achieve an overall accuracy of 94–98% when compared with manually segmented slices, approaching the results of off-the-shelf commercial software using deep learning (97–99%) at much lower complexity. A qualitative analysis of the segmented output shows that our results are close in term of usability to those from deep learning, justifying the use of these techniq...
Various alternative means of transportation are emerging aiming to combat traffic congestion. Amo... more Various alternative means of transportation are emerging aiming to combat traffic congestion. Among these solutions, car sharing and pooling schemes are currently gaining in popularity. Such schemes require computationally tractable algorithms for the efficient allocation of resources. Towards such an aim, the Variable Neighbourhood Search has emerged as the leading algorithmic solution. Aiming to speed up its convergence, this paper introduces two novelties to this algorithm. Results based on a realistic simulation scenario in a densely populated area demonstrate the added accuracy obtained using these novelties in a time-sensitive application.
In this work, we identify a problem with the process of volume-to-surface mapping of functional M... more In this work, we identify a problem with the process of volume-to-surface mapping of functional Magnetic Resonance Imaging (fMRI) data when interested in investigating local connectivity. We show that neighborhood correlations on the surface of the brain vary spatially with anatomical patterns (gyral structure), even when the underlying volumetric data are uncorrelated noise. This could potentially have impacted studies focusing upon local neighborhood connectivity. We explore the effects of this anomaly across varying data resolutions and surface mesh densities, and propose an approach to mitigate these unwanted effects.
IEEE Transactions on Intelligent Transportation Systems
An online dual estimation algorithm is developed to jointly estimate in real-time traffic quantit... more An online dual estimation algorithm is developed to jointly estimate in real-time traffic quantities such as queue lengths, occupancies and flows, as well as the parameters of a macroscopic model of a signalized junction. These parameters include turning ratios and saturation flows, together with model uncertainties. The proposed novel methodology is based on the Expectation-Maximization algorithm, modified for real-time estimation, with a Kalman filter implementing the expectation step and a multivariate gradient-based approach for the maximisation step. The algorithm is validated by simulating the typical signalized 3-arm and 4-arm junctions. This work is aimed to form a part of the adaptive control loops for traffic light systems that are able to autonomously adjust with changing traffic conditions, so as to ensure efficient vehicle flows.
Uploads