Samiul Hasan - Academia.edu (original) (raw)

Papers by Samiul Hasan

Networks and Spatial Economics, 2016

Household behavior and dynamic traffic flows are the two most important aspects of hurricane evac... more Household behavior and dynamic traffic flows are the two most important aspects of hurricane evacuations. However, current evacuation models largely overlook the complexity of household behavior leading to oversimplified traffic assignments and, as a result, inaccurate evacuation clearance times in the network. In this paper, we present a high fidelity multi-agent simulation model called A-RESCUE (Agent-based Regional Evacuation Simulator Coupled with User Enriched behavior) that integrates the rich activity behavior of the evacuating households with the network level assignment to predict and evaluate evacuation clearance times. The simulator can generate evacuation demand on the fly, truly capturing the dynamic nature of a hurricane evacuation. The simulator consists of two major components: household decision-making module and traffic flow module. In the simulation, each household is an agent making various evacuation related decisions based on advanced behavioral models. From household decisions, a number of vehicles are generated and entered in the evacuation transportation network at different time intervals. An adaptive routing strategy that can achieve efficient network-wide traffic measurements is proposed. Computational results are presented based on simulations over the Miami-Dade network with detailed representation of the road network geometry. The simulation results demonstrate the evolution of traffic congestion as a function of the household decision-making, the variance of the congestion Netw Spat Econ

Frontiers in Built Environment

Ubiquitous smartphone technologies and virtual social networks offer us a unique opportunity to i... more Ubiquitous smartphone technologies and virtual social networks offer us a unique opportunity to instantly share information to a large number of people. Online social media platforms facilitate easy and rapid communication of real-time information by producing a huge amount of digital content. In this paper, we present an analysis of the data collected from 14 Florida Department of Transportation (FDOT) Twitter accounts created for sharing real-time traffic information. We analyze the activities, influence, attention received, and the effectiveness of gaining attention by these accounts. We propose several metrics in disseminating real-time traffic information. Using topic models, we also analyze the content of the shared information given in the tweets. Finally, we estimate an ordered logit model to determine the information value of a shared content based on its chance of getting retweeted. Based on the study, we propose a framework called Social Media-based Adaptive Real-time Traffic feed (SMART-Feed) that will significantly improve the effectiveness of real-time traffic information sharing through social media. Moreover, it will help to assess the value of real-time traffic information to travelers and developing social media strategies for sharing information.

EPJ Data Science

Mobility is one of the fundamental requirements of human life with significant societal impacts i... more Mobility is one of the fundamental requirements of human life with significant societal impacts including productivity, economy, social wellbeing, adaptation to a changing climate, and so on. Although human movements follow specific patterns during normal periods, there are limited studies on how such patterns change due to extreme events. To quantify the impacts of an extreme event to human movements, we introduce the concept of mobility resilience which is defined as the ability of a mobility system to manage shocks and return to a steady state in response to an extreme event. We present a method to detect extreme events from geo-located movement data and to measure mobility resilience and transient loss of resilience due to those events. Applying this method, we measure resilience metrics from geo-located social media data for multiple types of disasters occurred all over the world. Quantifying mobility resilience may help us to assess the higher-order socioeconomic impacts of extreme events and guide policies towards developing resilient infrastructures as well as a nation's overall disaster resilience strategies.

Nucleic acids research, Jan 4, 2017

We have designed and developed a data integration and visualization platform that provides eviden... more We have designed and developed a data integration and visualization platform that provides evidence about the association of known and potential drug targets with diseases. The platform is designed to support identification and prioritization of biological targets for follow-up. Each drug target is linked to a disease using integrated genome-wide data from a broad range of data sources. The platform provides either a target-centric workflow to identify diseases that may be associated with a specific target, or a disease-centric workflow to identify targets that may be associated with a specific disease. Users can easily transition between these target- and disease-centric workflows. The Open Targets Validation Platform is accessible at https://www.targetvalidation.org.

Natural hazards such as floods, bushfires, cyclones or hurricanes, can cause significant damage t... more Natural hazards such as floods, bushfires, cyclones or hurricanes, can cause significant damage that disrupts our infrastructure systems. Climate change is making such extreme events more frequent and more severe. In addition, infrastructure systems have become more interconnected and interdependent. By interconnected, we mean that infrastructure systems use each other's output and operate together to provide joint services. The interdependence of two systems or components, on the other hand, refers to the effect of a decline of the performance of one system or component on another. Hence, a disruption to one infrastructure system may propagate into others and eventually affect various services that are critical for the well-being of communities. In recent years, cascading failures of infrastructure systems at a national scale have attracted significant attention. However, our understanding of these failures at a local scale remains limited. During major disasters, communities f...

PLOS ONE, 2015

Geo-location data from social media offers us information, in new ways, to understand people's at... more Geo-location data from social media offers us information, in new ways, to understand people's attitudes and interests through their activity choices. In this paper, we explore the idea of inferring individual lifestyle patterns from activity-location choices revealed in social media. We present a model to understand lifestyle patterns using the contextual information (e. g. location categories) of user check-ins. Probabilistic topic models are developed to infer individual geo lifestyle patterns from two perspectives: i) to characterize the patterns of user interests to different types of places and ii) to characterize the patterns of user visits to different neighborhoods. The method is applied to a dataset of Foursquare check-ins of the users from New York City. The coexistence of several location contexts and the corresponding probabilities in a given pattern provide useful information about user interests and choices. It is found that geo lifestyle patterns have similar items-either nearby neighborhoods or similar location categories. The semantic and geographic proximity of the items in a pattern reflects the hidden regularity in user preferences and location choice behavior.

PLoS ONE, 2014

Factors that contribute to the transmission of human immunodeficiency virus type 1 (HIV-1), espec... more Factors that contribute to the transmission of human immunodeficiency virus type 1 (HIV-1), especially drug-resistant HIV-1 variants remain a significant public health concern. In-depth phylogenetic analyses of viral sequences obtained in the screening phase from antiretroviral-naïve HIV-infected patients seeking enrollment in EPZ108859, a large open-label study in the USA, Canada and Puerto Rico (ClinicalTrials.gov NCT00440947) were examined for insights into the roles of drug resistance and epidemiological factors that could impact disease dissemination. Viral transmission clusters (VTCs) were initially predicted from a phylogenetic analysis of population level HIV-1 pol sequences obtained from 690 antiretroviralnaïve subjects in 2007. Subsequently, the predicted VTCs were tested for robustness by ultra deep sequencing (UDS) using pyrosequencing technology and further phylogenetic analyses. The demographic characteristics of clustered and nonclustered subjects were then compared. From 690 subjects, 69 were assigned to 1 of 30 VTCs, each containing 2 to 5 subjects. Race composition of VTCs were significantly more likely to be white (72% vs. 60%; p = 0.04). VTCs had fewer reverse transcriptase and major PI resistance mutations (9% vs. 24%; p = 0.002) than non-clustered sequences. Both men-who-havesex-with-men (MSM) (68% vs. 48%; p = 0.001) and Canadians (29% vs. 14%; p = 0.03) were significantly more frequent in VTCs than non-clustered sequences. Of the 515 subjects who initiated antiretroviral therapy, 33 experienced confirmed virologic failure through 144 weeks while only 3/33 were from VTCs. Fewer VTCs subjects (as compared to those with nonclustering virus) had HIV-1 with resistance-associated mutations or experienced virologic failure during the course of the study. Our analysis shows specific geographical and drug resistance trends that correlate well with transmission clusters defined by HIV sequences of similarity. Furthermore, our study demonstrates the utility of molecular and epidemiological analysis of VTCs for identifying population-specific risks associated with HIV-1 transmission and developing effective local healthcare strategies.

BMC Evolutionary Biology, 2008

Background Related species, such as humans and chimpanzees, often experience the same disease wit... more Background Related species, such as humans and chimpanzees, often experience the same disease with varying degrees of pathology, as seen in the cases of Alzheimer's disease, or differing symptomatology as in AIDS. Furthermore, certain diseases such as schizophrenia, epithelial cancers and autoimmune disorders are far more frequent in humans than in other species for reasons not associated with lifestyle. Genes that have undergone positive selection during species evolution are indicative of functional adaptations that drive species differences. Thus we investigate whether biomedical disease differences between species can be attributed to positively selected genes. Results We identified genes that putatively underwent positive selection during the evolution of humans and four mammals which are often used to model human diseases (mouse, rat, chimpanzee and dog). We show that genes predicted to have been subject to positive selection pressure during human evolution are implicated...

The goal of this paper is to demonstrate the use of an innovative social media-based data source,... more The goal of this paper is to demonstrate the use of an innovative social media-based data source, Twitter, to evaluate transit rider satisfaction. Transit authorities have access to vast amounts of performance metrics that measure ridership, timeliness, efficiency, safety, cleanliness, and service, to name a few. These performance metrics, however, are generally one-sided; they represent the interests of the business and are not customer-based. This paper recognizes the limitations of standard performance metrics and attempts to gauge transit rider sentiments by measuring Twitter feeds. Sentiment analysis is used to classify a population of rider sentiments over a period of time. Conclusions are drawn from totals of positive and negative sentiments, normalized average sentiments, and the total number of Tweets collected over a time period.

Natural Hazards Review, Jul 24, 2012

Hurricanes are costly natural disasters periodically faced by households in coastal and, to some ... more Hurricanes are costly natural disasters periodically faced by households in coastal and, to some extent, inland areas. Public agencies must understand household behavior in order to develop evacuation plans than align with evacuee choices and behavior. This paper presents a household-level hurricane evacuation destination type choice model never presented before. The discrete choice of destination type is modeled using a Nested Logit model. While previous literature considers only houses of friends and relatives and hotels ...

Transportation Research Record: Journal of the Transportation Research Board, Dec 1, 2011

An econometric framework was developed to combine data from various sources to identify the key f... more An econometric framework was developed to combine data from various sources to identify the key factors contributing to travel time variations in Central London. Nonlinear latent variable regression models that explicitly accounted for measurement errors in the data were developed to combine data extracted from automatic number plate recognition cameras and automatic traffic counters. This procedure significantly differed from previous research in this area that was based primarily on traffic flow data and ignored ...

Transportation Research Record: Journal of the Transportation Research Board, Dec 1, 2011

Pedestrian safety has been a major concern for megacities such as New York City. Although pedestr... more Pedestrian safety has been a major concern for megacities such as New York City. Although pedestrian fatalities show a downward trend, these fatalities constitute a high percentage of overall traffic fatalities in the city. Data from New York City were used to study the factors that influence the frequency of pedestrian crashes. Specifically, a random parameter, negative binomial model was developed for predicting pedestrian crash frequencies at the census tract level. This approach allows the incorporation of unobserved heterogeneity across the ...

Networks and Spatial Economics, 2016

Household behavior and dynamic traffic flows are the two most important aspects of hurricane evac... more Household behavior and dynamic traffic flows are the two most important aspects of hurricane evacuations. However, current evacuation models largely overlook the complexity of household behavior leading to oversimplified traffic assignments and, as a result, inaccurate evacuation clearance times in the network. In this paper, we present a high fidelity multi-agent simulation model called A-RESCUE (Agent-based Regional Evacuation Simulator Coupled with User Enriched behavior) that integrates the rich activity behavior of the evacuating households with the network level assignment to predict and evaluate evacuation clearance times. The simulator can generate evacuation demand on the fly, truly capturing the dynamic nature of a hurricane evacuation. The simulator consists of two major components: household decision-making module and traffic flow module. In the simulation, each household is an agent making various evacuation related decisions based on advanced behavioral models. From household decisions, a number of vehicles are generated and entered in the evacuation transportation network at different time intervals. An adaptive routing strategy that can achieve efficient network-wide traffic measurements is proposed. Computational results are presented based on simulations over the Miami-Dade network with detailed representation of the road network geometry. The simulation results demonstrate the evolution of traffic congestion as a function of the household decision-making, the variance of the congestion Netw Spat Econ

Frontiers in Built Environment

Ubiquitous smartphone technologies and virtual social networks offer us a unique opportunity to i... more Ubiquitous smartphone technologies and virtual social networks offer us a unique opportunity to instantly share information to a large number of people. Online social media platforms facilitate easy and rapid communication of real-time information by producing a huge amount of digital content. In this paper, we present an analysis of the data collected from 14 Florida Department of Transportation (FDOT) Twitter accounts created for sharing real-time traffic information. We analyze the activities, influence, attention received, and the effectiveness of gaining attention by these accounts. We propose several metrics in disseminating real-time traffic information. Using topic models, we also analyze the content of the shared information given in the tweets. Finally, we estimate an ordered logit model to determine the information value of a shared content based on its chance of getting retweeted. Based on the study, we propose a framework called Social Media-based Adaptive Real-time Traffic feed (SMART-Feed) that will significantly improve the effectiveness of real-time traffic information sharing through social media. Moreover, it will help to assess the value of real-time traffic information to travelers and developing social media strategies for sharing information.

EPJ Data Science

Mobility is one of the fundamental requirements of human life with significant societal impacts i... more Mobility is one of the fundamental requirements of human life with significant societal impacts including productivity, economy, social wellbeing, adaptation to a changing climate, and so on. Although human movements follow specific patterns during normal periods, there are limited studies on how such patterns change due to extreme events. To quantify the impacts of an extreme event to human movements, we introduce the concept of mobility resilience which is defined as the ability of a mobility system to manage shocks and return to a steady state in response to an extreme event. We present a method to detect extreme events from geo-located movement data and to measure mobility resilience and transient loss of resilience due to those events. Applying this method, we measure resilience metrics from geo-located social media data for multiple types of disasters occurred all over the world. Quantifying mobility resilience may help us to assess the higher-order socioeconomic impacts of extreme events and guide policies towards developing resilient infrastructures as well as a nation's overall disaster resilience strategies.

Nucleic acids research, Jan 4, 2017

We have designed and developed a data integration and visualization platform that provides eviden... more We have designed and developed a data integration and visualization platform that provides evidence about the association of known and potential drug targets with diseases. The platform is designed to support identification and prioritization of biological targets for follow-up. Each drug target is linked to a disease using integrated genome-wide data from a broad range of data sources. The platform provides either a target-centric workflow to identify diseases that may be associated with a specific target, or a disease-centric workflow to identify targets that may be associated with a specific disease. Users can easily transition between these target- and disease-centric workflows. The Open Targets Validation Platform is accessible at https://www.targetvalidation.org.

Natural hazards such as floods, bushfires, cyclones or hurricanes, can cause significant damage t... more Natural hazards such as floods, bushfires, cyclones or hurricanes, can cause significant damage that disrupts our infrastructure systems. Climate change is making such extreme events more frequent and more severe. In addition, infrastructure systems have become more interconnected and interdependent. By interconnected, we mean that infrastructure systems use each other's output and operate together to provide joint services. The interdependence of two systems or components, on the other hand, refers to the effect of a decline of the performance of one system or component on another. Hence, a disruption to one infrastructure system may propagate into others and eventually affect various services that are critical for the well-being of communities. In recent years, cascading failures of infrastructure systems at a national scale have attracted significant attention. However, our understanding of these failures at a local scale remains limited. During major disasters, communities f...

PLOS ONE, 2015

Geo-location data from social media offers us information, in new ways, to understand people's at... more Geo-location data from social media offers us information, in new ways, to understand people's attitudes and interests through their activity choices. In this paper, we explore the idea of inferring individual lifestyle patterns from activity-location choices revealed in social media. We present a model to understand lifestyle patterns using the contextual information (e. g. location categories) of user check-ins. Probabilistic topic models are developed to infer individual geo lifestyle patterns from two perspectives: i) to characterize the patterns of user interests to different types of places and ii) to characterize the patterns of user visits to different neighborhoods. The method is applied to a dataset of Foursquare check-ins of the users from New York City. The coexistence of several location contexts and the corresponding probabilities in a given pattern provide useful information about user interests and choices. It is found that geo lifestyle patterns have similar items-either nearby neighborhoods or similar location categories. The semantic and geographic proximity of the items in a pattern reflects the hidden regularity in user preferences and location choice behavior.

PLoS ONE, 2014

Factors that contribute to the transmission of human immunodeficiency virus type 1 (HIV-1), espec... more Factors that contribute to the transmission of human immunodeficiency virus type 1 (HIV-1), especially drug-resistant HIV-1 variants remain a significant public health concern. In-depth phylogenetic analyses of viral sequences obtained in the screening phase from antiretroviral-naïve HIV-infected patients seeking enrollment in EPZ108859, a large open-label study in the USA, Canada and Puerto Rico (ClinicalTrials.gov NCT00440947) were examined for insights into the roles of drug resistance and epidemiological factors that could impact disease dissemination. Viral transmission clusters (VTCs) were initially predicted from a phylogenetic analysis of population level HIV-1 pol sequences obtained from 690 antiretroviralnaïve subjects in 2007. Subsequently, the predicted VTCs were tested for robustness by ultra deep sequencing (UDS) using pyrosequencing technology and further phylogenetic analyses. The demographic characteristics of clustered and nonclustered subjects were then compared. From 690 subjects, 69 were assigned to 1 of 30 VTCs, each containing 2 to 5 subjects. Race composition of VTCs were significantly more likely to be white (72% vs. 60%; p = 0.04). VTCs had fewer reverse transcriptase and major PI resistance mutations (9% vs. 24%; p = 0.002) than non-clustered sequences. Both men-who-havesex-with-men (MSM) (68% vs. 48%; p = 0.001) and Canadians (29% vs. 14%; p = 0.03) were significantly more frequent in VTCs than non-clustered sequences. Of the 515 subjects who initiated antiretroviral therapy, 33 experienced confirmed virologic failure through 144 weeks while only 3/33 were from VTCs. Fewer VTCs subjects (as compared to those with nonclustering virus) had HIV-1 with resistance-associated mutations or experienced virologic failure during the course of the study. Our analysis shows specific geographical and drug resistance trends that correlate well with transmission clusters defined by HIV sequences of similarity. Furthermore, our study demonstrates the utility of molecular and epidemiological analysis of VTCs for identifying population-specific risks associated with HIV-1 transmission and developing effective local healthcare strategies.

BMC Evolutionary Biology, 2008

Background Related species, such as humans and chimpanzees, often experience the same disease wit... more Background Related species, such as humans and chimpanzees, often experience the same disease with varying degrees of pathology, as seen in the cases of Alzheimer's disease, or differing symptomatology as in AIDS. Furthermore, certain diseases such as schizophrenia, epithelial cancers and autoimmune disorders are far more frequent in humans than in other species for reasons not associated with lifestyle. Genes that have undergone positive selection during species evolution are indicative of functional adaptations that drive species differences. Thus we investigate whether biomedical disease differences between species can be attributed to positively selected genes. Results We identified genes that putatively underwent positive selection during the evolution of humans and four mammals which are often used to model human diseases (mouse, rat, chimpanzee and dog). We show that genes predicted to have been subject to positive selection pressure during human evolution are implicated...

The goal of this paper is to demonstrate the use of an innovative social media-based data source,... more The goal of this paper is to demonstrate the use of an innovative social media-based data source, Twitter, to evaluate transit rider satisfaction. Transit authorities have access to vast amounts of performance metrics that measure ridership, timeliness, efficiency, safety, cleanliness, and service, to name a few. These performance metrics, however, are generally one-sided; they represent the interests of the business and are not customer-based. This paper recognizes the limitations of standard performance metrics and attempts to gauge transit rider sentiments by measuring Twitter feeds. Sentiment analysis is used to classify a population of rider sentiments over a period of time. Conclusions are drawn from totals of positive and negative sentiments, normalized average sentiments, and the total number of Tweets collected over a time period.

Natural Hazards Review, Jul 24, 2012

Hurricanes are costly natural disasters periodically faced by households in coastal and, to some ... more Hurricanes are costly natural disasters periodically faced by households in coastal and, to some extent, inland areas. Public agencies must understand household behavior in order to develop evacuation plans than align with evacuee choices and behavior. This paper presents a household-level hurricane evacuation destination type choice model never presented before. The discrete choice of destination type is modeled using a Nested Logit model. While previous literature considers only houses of friends and relatives and hotels ...

Transportation Research Record: Journal of the Transportation Research Board, Dec 1, 2011

An econometric framework was developed to combine data from various sources to identify the key f... more An econometric framework was developed to combine data from various sources to identify the key factors contributing to travel time variations in Central London. Nonlinear latent variable regression models that explicitly accounted for measurement errors in the data were developed to combine data extracted from automatic number plate recognition cameras and automatic traffic counters. This procedure significantly differed from previous research in this area that was based primarily on traffic flow data and ignored ...

Transportation Research Record: Journal of the Transportation Research Board, Dec 1, 2011

Pedestrian safety has been a major concern for megacities such as New York City. Although pedestr... more Pedestrian safety has been a major concern for megacities such as New York City. Although pedestrian fatalities show a downward trend, these fatalities constitute a high percentage of overall traffic fatalities in the city. Data from New York City were used to study the factors that influence the frequency of pedestrian crashes. Specifically, a random parameter, negative binomial model was developed for predicting pedestrian crash frequencies at the census tract level. This approach allows the incorporation of unobserved heterogeneity across the ...