Philip E. Brown, Krystian Czapiga, Arun Jotshi, Y. Kanza, Velin Kounev, Poornima Suresh
Microwave backhaul links are often used as wireless connections between telecommunication towers, in places where deploying optical fibers is impossible or too expensive. The relatively high frequency of microwaves increases their ability to transfer information at a high rate, but it also makes them susceptible to spatial obstructions and interference. Hence, when deploying wireless links, there are two conflicting considerations. First, the antennas height, selected from the available slots on each tower, should be as low as possible. Second, there should be a line of sight (LoS) between the antennas, and a buffer around the LoS defined by the first Fresnel zone should be clear of obstacles. To compute antenna heights, a planning system for wireless links has to maintain an elevation model, efficiently discover obstacles between towers, and execute Fresnel-zone clearance tests over a 3D model of the deployment area. In this article we present a system and algorithms for computing the height of antennas, by testing LoS and clearance of Fresnel zones. The system handles the following requirements: (1) the need to cover large areas, e.g., all of the USA, (2) big distance between towers, e.g., 100 kilometers, and (3) computing batches of thousands of pairs within a few minutes. We introduce three novel algorithms for efficient computation of antenna heights, we show how to effectively model and manage the large-scale geospatial data needed for the planning, and we present the results of tests over real-world settings.
{"title":"Planning Wireless Backhaul Links by Testing Line of Sight and Fresnel Zone Clearance","authors":"Philip E. Brown, Krystian Czapiga, Arun Jotshi, Y. Kanza, Velin Kounev, Poornima Suresh","doi":"10.1145/3517382","DOIUrl":"https://doi.org/10.1145/3517382","url":null,"abstract":"Microwave backhaul links are often used as wireless connections between telecommunication towers, in places where deploying optical fibers is impossible or too expensive. The relatively high frequency of microwaves increases their ability to transfer information at a high rate, but it also makes them susceptible to spatial obstructions and interference. Hence, when deploying wireless links, there are two conflicting considerations. First, the antennas height, selected from the available slots on each tower, should be as low as possible. Second, there should be a line of sight (LoS) between the antennas, and a buffer around the LoS defined by the first Fresnel zone should be clear of obstacles. To compute antenna heights, a planning system for wireless links has to maintain an elevation model, efficiently discover obstacles between towers, and execute Fresnel-zone clearance tests over a 3D model of the deployment area. In this article we present a system and algorithms for computing the height of antennas, by testing LoS and clearance of Fresnel zones. The system handles the following requirements: (1) the need to cover large areas, e.g., all of the USA, (2) big distance between towers, e.g., 100 kilometers, and (3) computing batches of thousands of pairs within a few minutes. We introduce three novel algorithms for efficient computation of antenna heights, we show how to effectively model and manage the large-scale geospatial data needed for the planning, and we present the results of tests over real-world settings.","PeriodicalId":43641,"journal":{"name":"ACM Transactions on Spatial Algorithms and Systems","volume":"9 1","pages":"1 - 30"},"PeriodicalIF":1.9,"publicationDate":"2022-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48891574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Traveling salesman problem (TSP) is one of the most studied combinatorial optimization problems; several exact, heuristic or even learning-based strategies have been proposed to solve this challenging issue. Targeting on the research problem of bi-objective non-monotonic Euclidean TSP and based on the concept of the multi-agent-based approach, we propose a two-stage parallel matching approaching for solving TSP. Acting as a divide-and-conquer strategy, the merit lies in the simultaneously clustering and routing in the dividing process. Precisely, we first propose the Two-Stage Parallel Matching algorithm (TSPM) to deal with the bi-objective TSP. We then formulate the Grid-Based Two-Stage Parallel Matching (GRAPE) framework, which can synergize with TSPM, exact method, or other state-of-the-art TSP solvers, for solving large-scale Euclidean TSP. According to this framework, the original problem space is divided into smaller regions and then computed in parallel, which helps to tackle and derive solutions for larger-scale Euclidean TSP within reasonable computational resources. Preliminary evaluation based on TSPLIB testbed shows that our proposed GRAPE framework holds a decent quality of solutions in especially runtime for large-scale Euclidean TSP. Meanwhile, experiments conducted on two real-world datasets demonstrate the efficacy and adaptability of our proposed TSPM in solving the bi-objective non-monotonic TSP.
{"title":"A Grid-Based Two-Stage Parallel Matching Framework for Bi-Objective Euclidean Traveling Salesman Problem","authors":"Fandel Lin, Hsun-Ping Hsieh","doi":"10.1145/3526025","DOIUrl":"https://doi.org/10.1145/3526025","url":null,"abstract":"Traveling salesman problem (TSP) is one of the most studied combinatorial optimization problems; several exact, heuristic or even learning-based strategies have been proposed to solve this challenging issue. Targeting on the research problem of bi-objective non-monotonic Euclidean TSP and based on the concept of the multi-agent-based approach, we propose a two-stage parallel matching approaching for solving TSP. Acting as a divide-and-conquer strategy, the merit lies in the simultaneously clustering and routing in the dividing process. Precisely, we first propose the Two-Stage Parallel Matching algorithm (TSPM) to deal with the bi-objective TSP. We then formulate the Grid-Based Two-Stage Parallel Matching (GRAPE) framework, which can synergize with TSPM, exact method, or other state-of-the-art TSP solvers, for solving large-scale Euclidean TSP. According to this framework, the original problem space is divided into smaller regions and then computed in parallel, which helps to tackle and derive solutions for larger-scale Euclidean TSP within reasonable computational resources. Preliminary evaluation based on TSPLIB testbed shows that our proposed GRAPE framework holds a decent quality of solutions in especially runtime for large-scale Euclidean TSP. Meanwhile, experiments conducted on two real-world datasets demonstrate the efficacy and adaptability of our proposed TSPM in solving the bi-objective non-monotonic TSP.","PeriodicalId":43641,"journal":{"name":"ACM Transactions on Spatial Algorithms and Systems","volume":"8 1","pages":"1 - 33"},"PeriodicalIF":1.9,"publicationDate":"2022-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46960368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Bayesian networks are a popular class of multivariate probabilistic models as they allow for the translation of prior beliefs about conditional dependencies between variables to be easily encoded into their model structure. Due to their widespread usage, they are often applied to spatial data for inferring properties of the systems under study and also generating predictions for how these systems may behave in the future. We review published research on methodologies for representing spatial data with Bayesian networks and also summarize the application areas for which Bayesian networks are employed in the modeling of spatial data. We find that a wide variety of perspectives are taken, including a GIS-centric focus on efficiently generating geospatial predictions, a statistical focus on rigorously constructing graphical models controlling for spatial correlation, as well as a range of problem-specific heuristics for mitigating the effects of spatial correlation and dependency arising in spatial data analysis. Special attention is also paid to potential future directions for the integration of Bayesian networks with spatial processes.
{"title":"A Review of Bayesian Networks for Spatial Data","authors":"C. Krapu, R. Stewart, A. Rose","doi":"10.1145/3516523","DOIUrl":"https://doi.org/10.1145/3516523","url":null,"abstract":"Bayesian networks are a popular class of multivariate probabilistic models as they allow for the translation of prior beliefs about conditional dependencies between variables to be easily encoded into their model structure. Due to their widespread usage, they are often applied to spatial data for inferring properties of the systems under study and also generating predictions for how these systems may behave in the future. We review published research on methodologies for representing spatial data with Bayesian networks and also summarize the application areas for which Bayesian networks are employed in the modeling of spatial data. We find that a wide variety of perspectives are taken, including a GIS-centric focus on efficiently generating geospatial predictions, a statistical focus on rigorously constructing graphical models controlling for spatial correlation, as well as a range of problem-specific heuristics for mitigating the effects of spatial correlation and dependency arising in spatial data analysis. Special attention is also paid to potential future directions for the integration of Bayesian networks with spatial processes.","PeriodicalId":43641,"journal":{"name":"ACM Transactions on Spatial Algorithms and Systems","volume":"9 1","pages":"1 - 21"},"PeriodicalIF":1.9,"publicationDate":"2022-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44924090","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Urban planning refers to the efforts of designing land-use configurations given a region. However, to obtain effective urban plans, urban experts have to spend much time and effort analyzing sophisticated planning constraints based on domain knowledge and personal experiences. To alleviate the heavy burden of them and produce consistent urban plans, we want to ask that can AI accelerate the urban planning process, so that human planners only adjust generated configurations for specific needs? The recent advance of deep generative models provides a possible answer, which inspires us to automate urban planning from an adversarial learning perspective. However, three major challenges arise: (1) how to define a quantitative land-use configuration? (2) how to automate configuration planning? (3) how to evaluate the quality of a generated configuration? In this article, we systematically address the three challenges. Specifically, (1) We define a land-use configuration as a longitude-latitude-channel tensor. (2) We formulate the automated urban planning problem into a task of deep generative learning. The objective is to generate a configuration tensor given the surrounding contexts of a target region. In particular, we first construct spatial graphs using geographic and human mobility data crawled from websites to learn graph representations. We then combine each target area and its surrounding context representations as a tuple, and categorize all tuples into positive (well-planned areas) and negative samples (poorly-planned areas). Next, we develop an adversarial learning framework, in which a generator takes the surrounding context representations as input to generate a land-use configuration, and a discriminator learns to distinguish between positive and negative samples. (3) We provide quantitative evaluation metrics and conduct extensive experiments to demonstrate the effectiveness of our framework.
{"title":"Automated Urban Planning for Reimagining City Configuration via Adversarial Learning: Quantification, Generation, and Evaluation","authors":"Dongjie Wang, Yanjie Fu, Kunpeng Liu, Fanglan Chen, Pengyang Wang, Chang-Tien Lu","doi":"10.1145/3524302","DOIUrl":"https://doi.org/10.1145/3524302","url":null,"abstract":"Urban planning refers to the efforts of designing land-use configurations given a region. However, to obtain effective urban plans, urban experts have to spend much time and effort analyzing sophisticated planning constraints based on domain knowledge and personal experiences. To alleviate the heavy burden of them and produce consistent urban plans, we want to ask that can AI accelerate the urban planning process, so that human planners only adjust generated configurations for specific needs? The recent advance of deep generative models provides a possible answer, which inspires us to automate urban planning from an adversarial learning perspective. However, three major challenges arise: (1) how to define a quantitative land-use configuration? (2) how to automate configuration planning? (3) how to evaluate the quality of a generated configuration? In this article, we systematically address the three challenges. Specifically, (1) We define a land-use configuration as a longitude-latitude-channel tensor. (2) We formulate the automated urban planning problem into a task of deep generative learning. The objective is to generate a configuration tensor given the surrounding contexts of a target region. In particular, we first construct spatial graphs using geographic and human mobility data crawled from websites to learn graph representations. We then combine each target area and its surrounding context representations as a tuple, and categorize all tuples into positive (well-planned areas) and negative samples (poorly-planned areas). Next, we develop an adversarial learning framework, in which a generator takes the surrounding context representations as input to generate a land-use configuration, and a discriminator learns to distinguish between positive and negative samples. (3) We provide quantitative evaluation metrics and conduct extensive experiments to demonstrate the effectiveness of our framework.","PeriodicalId":43641,"journal":{"name":"ACM Transactions on Spatial Algorithms and Systems","volume":"9 1","pages":"1 - 24"},"PeriodicalIF":1.9,"publicationDate":"2021-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43034813","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
O. Wolfson, Prabin Giri, S. Jajodia, Goce Trajcevski
We consider surveillance of a geographic region by a collaborative system of drones. The drones assist each other in identifying and managing activities of interest on the ground. We also consider an adversary who can create both genuine and fake activities on the ground. The objective of the adversary is to use fake activities to maximize the response time to genuine activities. We present two collaboration algorithms and analyze their response times, as well as the adversary’s efforts in terms of the number of fake activities required to achieve a certain response time.
{"title":"Geographic-Region Monitoring by Drones in Adversarial Environments","authors":"O. Wolfson, Prabin Giri, S. Jajodia, Goce Trajcevski","doi":"10.1145/3611009","DOIUrl":"https://doi.org/10.1145/3611009","url":null,"abstract":"We consider surveillance of a geographic region by a collaborative system of drones. The drones assist each other in identifying and managing activities of interest on the ground. We also consider an adversary who can create both genuine and fake activities on the ground. The objective of the adversary is to use fake activities to maximize the response time to genuine activities. We present two collaboration algorithms and analyze their response times, as well as the adversary’s efforts in terms of the number of fake activities required to achieve a certain response time.","PeriodicalId":43641,"journal":{"name":"ACM Transactions on Spatial Algorithms and Systems","volume":"9 1","pages":"1 - 36"},"PeriodicalIF":1.9,"publicationDate":"2021-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44787657","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the last decade, more and more spatial data has been acquired on a global scale due to satellite missions, social media, and coordinated governmental activities. This observational data suffers from huge storage footprints and makes global analysis challenging. Therefore, many information products have been designed in which observations are turned into global maps showing features such as land cover or land use, often with only a few discrete values and sparse spatial coverage like only within cities. Traditional coding of such data as a raster image becomes challenging due to the sizes of the datasets and spatially non-local access patterns, for example, when labeling social media streams. This article proposes GloBiMap, a randomized data structure, based on Bloom filters, for modeling low-cardinality sparse raster images of excessive sizes in a configurable amount of memory with pure random access operations avoiding costly intermediate decompression. In addition, the data structure is designed to correct the inevitable errors of the randomized layer in order to have a fully exact representation. We show the feasibility of the approach on several real-world datasets including the Global Urban Footprint in which each pixel denotes whether a particular location contains a building at a resolution of roughly 10m globally as well as on a global Twitter sample of more than 220 million precisely geolocated tweets. In addition, we propose the integration of a denoiser engine based on artificial intelligence in order to reduce the amount of error correction information for extremely compressive GloBiMaps.
{"title":"GloBiMapsAI: An AI-Enhanced Probabilistic Data Structure for Global Raster Datasets","authors":"M. Werner","doi":"10.1145/3453184","DOIUrl":"https://doi.org/10.1145/3453184","url":null,"abstract":"In the last decade, more and more spatial data has been acquired on a global scale due to satellite missions, social media, and coordinated governmental activities. This observational data suffers from huge storage footprints and makes global analysis challenging. Therefore, many information products have been designed in which observations are turned into global maps showing features such as land cover or land use, often with only a few discrete values and sparse spatial coverage like only within cities. Traditional coding of such data as a raster image becomes challenging due to the sizes of the datasets and spatially non-local access patterns, for example, when labeling social media streams. This article proposes GloBiMap, a randomized data structure, based on Bloom filters, for modeling low-cardinality sparse raster images of excessive sizes in a configurable amount of memory with pure random access operations avoiding costly intermediate decompression. In addition, the data structure is designed to correct the inevitable errors of the randomized layer in order to have a fully exact representation. We show the feasibility of the approach on several real-world datasets including the Global Urban Footprint in which each pixel denotes whether a particular location contains a building at a resolution of roughly 10m globally as well as on a global Twitter sample of more than 220 million precisely geolocated tweets. In addition, we propose the integration of a denoiser engine based on artificial intelligence in order to reduce the amount of error correction information for extremely compressive GloBiMaps.","PeriodicalId":43641,"journal":{"name":"ACM Transactions on Spatial Algorithms and Systems","volume":" ","pages":"1 - 24"},"PeriodicalIF":1.9,"publicationDate":"2021-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/3453184","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44081864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
D. Teixeira, A. C. Viana, J. Almeida, Mrio S. Alvim
Predicting mobility-related behavior is an important yet challenging task. On the one hand, factors such as one’s routine or preferences for a few favorite locations may help in predicting their mobility. On the other hand, several contextual factors, such as variations in individual preferences, weather, traffic, or even a person’s social contacts, can affect mobility patterns and make its modeling significantly more challenging. A fundamental approach to study mobility-related behavior is to assess how predictable such behavior is, deriving theoretical limits on the accuracy that a prediction model can achieve given a specific dataset. This approach focuses on the inherent nature and fundamental patterns of human behavior captured in that dataset, filtering out factors that depend on the specificities of the prediction method adopted. However, the current state-of-the-art method to estimate predictability in human mobility suffers from two major limitations: low interpretability and hardness to incorporate external factors that are known to help mobility prediction (i.e., contextual information). In this article, we revisit this state-of-the-art method, aiming at tackling these limitations. Specifically, we conduct a thorough analysis of how this widely used method works by looking into two different metrics that are easier to understand and, at the same time, capture reasonably well the effects of the original technique. We evaluate these metrics in the context of two different mobility prediction tasks, notably, next cell and next distinct cell prediction, which have different degrees of difficulty. Additionally, we propose alternative strategies to incorporate different types of contextual information into the existing technique. Our evaluation of these strategies offer quantitative measures of the impact of adding context to the predictability estimate, revealing the challenges associated with doing so in practical scenarios.
{"title":"The Impact of Stationarity, Regularity, and Context on the Predictability of Individual Human Mobility","authors":"D. Teixeira, A. C. Viana, J. Almeida, Mrio S. Alvim","doi":"10.1145/3459625","DOIUrl":"https://doi.org/10.1145/3459625","url":null,"abstract":"Predicting mobility-related behavior is an important yet challenging task. On the one hand, factors such as one’s routine or preferences for a few favorite locations may help in predicting their mobility. On the other hand, several contextual factors, such as variations in individual preferences, weather, traffic, or even a person’s social contacts, can affect mobility patterns and make its modeling significantly more challenging. A fundamental approach to study mobility-related behavior is to assess how predictable such behavior is, deriving theoretical limits on the accuracy that a prediction model can achieve given a specific dataset. This approach focuses on the inherent nature and fundamental patterns of human behavior captured in that dataset, filtering out factors that depend on the specificities of the prediction method adopted. However, the current state-of-the-art method to estimate predictability in human mobility suffers from two major limitations: low interpretability and hardness to incorporate external factors that are known to help mobility prediction (i.e., contextual information). In this article, we revisit this state-of-the-art method, aiming at tackling these limitations. Specifically, we conduct a thorough analysis of how this widely used method works by looking into two different metrics that are easier to understand and, at the same time, capture reasonably well the effects of the original technique. We evaluate these metrics in the context of two different mobility prediction tasks, notably, next cell and next distinct cell prediction, which have different degrees of difficulty. Additionally, we propose alternative strategies to incorporate different types of contextual information into the existing technique. Our evaluation of these strategies offer quantitative measures of the impact of adding context to the predictability estimate, revealing the challenges associated with doing so in practical scenarios.","PeriodicalId":43641,"journal":{"name":"ACM Transactions on Spatial Algorithms and Systems","volume":"7 1","pages":"1 - 24"},"PeriodicalIF":1.9,"publicationDate":"2021-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/3459625","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43962512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zakaria Mehrab, A. Adiga, M. Marathe, S. Venkatramanan, S. Swarup
High resolution mobility datasets have become increasingly available in the past few years and have enabled detailed models for infectious disease spread including those for COVID-19. However, there are open questions on how such mobility data can be used effectively within epidemic models and for which tasks they are best suited. In this paper, we extract a number of graph-based proximity metrics from high resolution cellphone trace data from X-Mode and use it to study COVID-19 epidemic spread in 50 land grant university counties in the US. We present an approach to estimate the effect of mobility on cases by fitting an ordinary differential equation based model and performing multivariate linear regression to explain the estimated time varying transmissibility. We find that, while mobility plays a significant role, the contribution is heterogeneous across the counties, as exemplified by a subsequent correlation analysis. We also evaluate the metrics’ utility for case surge prediction defined as a supervised classification problem, and show that the learnt model can predict surges with 95% accuracy and an 87% F1-score.
{"title":"Evaluating the Utility of High-Resolution Proximity Metrics in Predicting the Spread of COVID-19","authors":"Zakaria Mehrab, A. Adiga, M. Marathe, S. Venkatramanan, S. Swarup","doi":"10.1145/3531006","DOIUrl":"https://doi.org/10.1145/3531006","url":null,"abstract":"High resolution mobility datasets have become increasingly available in the past few years and have enabled detailed models for infectious disease spread including those for COVID-19. However, there are open questions on how such mobility data can be used effectively within epidemic models and for which tasks they are best suited. In this paper, we extract a number of graph-based proximity metrics from high resolution cellphone trace data from X-Mode and use it to study COVID-19 epidemic spread in 50 land grant university counties in the US. We present an approach to estimate the effect of mobility on cases by fitting an ordinary differential equation based model and performing multivariate linear regression to explain the estimated time varying transmissibility. We find that, while mobility plays a significant role, the contribution is heterogeneous across the counties, as exemplified by a subsequent correlation analysis. We also evaluate the metrics’ utility for case surge prediction defined as a supervised classification problem, and show that the learnt model can predict surges with 95% accuracy and an 87% F1-score.","PeriodicalId":43641,"journal":{"name":"ACM Transactions on Spatial Algorithms and Systems","volume":"8 1","pages":"1 - 51"},"PeriodicalIF":1.9,"publicationDate":"2021-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47112596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mário Cardoso, A. Cavalheiro, Alexandre Borges, A. F. Duarte, A. Soares, M. Pereira, N. Nunes, L. Azevedo, Arlindo L. Oliveira
Europe was hit hard by the COVID-19 pandemic and Portugal was severely affected, having suffered three waves in the first twelve months. Approximately between January 19th and February 5th 2021 Portugal was the country in the world with the largest incidence rate, with 14-day incidence rates per 100,000 inhabitants in excess of 1,000. Despite its importance, accurate prediction of the geospatial evolution of COVID-19 remains a challenge, since existing analytical methods fail to capture the complex dynamics that result from the contagion within a region and the spreading of the infection from infected neighboring regions. We use a previously developed methodology and official municipality level data from the Portuguese Directorate-General for Health (DGS), relative to the first twelve months of the pandemic, to compute an estimate of the incidence rate in each location of mainland Portugal. The resulting sequence of incidence rate maps was then used as a gold standard to test the effectiveness of different approaches in the prediction of the spatial-temporal evolution of the incidence rate. Four different methods were tested: a simple cell level autoregressive moving average (ARMA) model, a cell level vector autoregressive (VAR) model, a municipality-by-municipality compartmental SIRD model followed by direct block sequential simulation, and a new convolutional sequence-to-sequence neural network model based on the STConvS2S architecture. We conclude that the modified convolutional sequence-to-sequence neural network is the best performing method in this task, when compared with the ARMA, VAR, and SIRD models, as well as with the baseline ConvLSTM model.
{"title":"Modeling the Geospatial Evolution of COVID-19 using Spatio-temporal Convolutional Sequence-to-sequence Neural Networks","authors":"Mário Cardoso, A. Cavalheiro, Alexandre Borges, A. F. Duarte, A. Soares, M. Pereira, N. Nunes, L. Azevedo, Arlindo L. Oliveira","doi":"10.1145/3550272","DOIUrl":"https://doi.org/10.1145/3550272","url":null,"abstract":"Europe was hit hard by the COVID-19 pandemic and Portugal was severely affected, having suffered three waves in the first twelve months. Approximately between January 19th and February 5th 2021 Portugal was the country in the world with the largest incidence rate, with 14-day incidence rates per 100,000 inhabitants in excess of 1,000. Despite its importance, accurate prediction of the geospatial evolution of COVID-19 remains a challenge, since existing analytical methods fail to capture the complex dynamics that result from the contagion within a region and the spreading of the infection from infected neighboring regions. We use a previously developed methodology and official municipality level data from the Portuguese Directorate-General for Health (DGS), relative to the first twelve months of the pandemic, to compute an estimate of the incidence rate in each location of mainland Portugal. The resulting sequence of incidence rate maps was then used as a gold standard to test the effectiveness of different approaches in the prediction of the spatial-temporal evolution of the incidence rate. Four different methods were tested: a simple cell level autoregressive moving average (ARMA) model, a cell level vector autoregressive (VAR) model, a municipality-by-municipality compartmental SIRD model followed by direct block sequential simulation, and a new convolutional sequence-to-sequence neural network model based on the STConvS2S architecture. We conclude that the modified convolutional sequence-to-sequence neural network is the best performing method in this task, when compared with the ARMA, VAR, and SIRD models, as well as with the baseline ConvLSTM model.","PeriodicalId":43641,"journal":{"name":"ACM Transactions on Spatial Algorithms and Systems","volume":"8 1","pages":"1 - 19"},"PeriodicalIF":1.9,"publicationDate":"2021-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48171362","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
T. S. Jepsen, Christian S. Jensen, Thomas D. Nielsen
Travel time and speed estimation are part of many intelligent transportation applications. Existing estimation approaches rely on either function fitting or data aggregation and represent different tradeoffs between generalizability and accuracy. Function-fitting approaches learn functions that map feature vectors of, e.g., routes to travel time or speed estimates, which enables generalization to unseen routes. However, mapping functions are imperfect and offer poor accuracy in practice. Aggregation-based approaches instead form estimates by aggregating historical data, e.g., traversal data for routes. This enables very high accuracy given sufficient data. However, they rely on simplistic heuristics when insufficient data is available, yielding poor generalizability. We present a Unifying approach to Travel time and speed Estimation (UniTE) that combines function-fitting and aggregation-based approaches into a unified framework that aims to achieve the generalizability of function-fitting approaches and the accuracy of aggregation-based approaches when data is available. We demonstrate empirically that an instance of UniTE can improve the accuracies of travel speed and travel time estimation by 40–64% and 3–23%, respectively, compared to using only function fitting or data aggregation.
{"title":"UniTE—The Best of Both Worlds: Unifying Function-fitting and Aggregation-based Approaches to Travel Time and Travel Speed Estimation","authors":"T. S. Jepsen, Christian S. Jensen, Thomas D. Nielsen","doi":"10.1145/3517335","DOIUrl":"https://doi.org/10.1145/3517335","url":null,"abstract":"Travel time and speed estimation are part of many intelligent transportation applications. Existing estimation approaches rely on either function fitting or data aggregation and represent different tradeoffs between generalizability and accuracy. Function-fitting approaches learn functions that map feature vectors of, e.g., routes to travel time or speed estimates, which enables generalization to unseen routes. However, mapping functions are imperfect and offer poor accuracy in practice. Aggregation-based approaches instead form estimates by aggregating historical data, e.g., traversal data for routes. This enables very high accuracy given sufficient data. However, they rely on simplistic heuristics when insufficient data is available, yielding poor generalizability. We present a Unifying approach to Travel time and speed Estimation (UniTE) that combines function-fitting and aggregation-based approaches into a unified framework that aims to achieve the generalizability of function-fitting approaches and the accuracy of aggregation-based approaches when data is available. We demonstrate empirically that an instance of UniTE can improve the accuracies of travel speed and travel time estimation by 40–64% and 3–23%, respectively, compared to using only function fitting or data aggregation.","PeriodicalId":43641,"journal":{"name":"ACM Transactions on Spatial Algorithms and Systems","volume":"8 1","pages":"1 - 26"},"PeriodicalIF":1.9,"publicationDate":"2021-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45927316","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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