The Experts below are selected from a list of 31563 Experts worldwide ranked by ideXlab platform
Lu Feng - One of the best experts on this subject based on the ideXlab platform.
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sastl Spatial Aggregation signal temporal logic for runtime monitoring in smart cities
arXiv: Computers and Society, 2020Co-Authors: Ezio Bartocci, Eli Lifland, John A Stankovic, Lu FengAbstract:We present SaSTL---a novel Spatial Aggregation Signal Temporal Logic---for the efficient runtime monitoring of safety and performance requirements in smart cities. We first describe a study of over 1,000 smart city requirements, some of which can not be specified using existing logic such as Signal Temporal Logic (STL) and its variants. To tackle this limitation, we develop two new logical operators in SaSTL to augment STL for expressing Spatial Aggregation and Spatial counting characteristics that are commonly found in real city requirements. We also develop efficient monitoring algorithms that can check a SaSTL requirement in parallel over multiple data streams (e.g., generated by multiple sensors distributed Spatially in a city). We evaluate our SaSTL monitor by applying to two case studies with large-scale real city sensing data (e.g., up to 10,000 sensors in one requirement). The results show that SaSTL has a much higher coverage expressiveness than other Spatial-temporal logics, and with a significant reduction of computation time for monitoring requirements. We also demonstrate that the SaSTL monitor can help improve the safety and performance of smart cities via simulated experiments.
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ICCPS - SaSTL: Spatial Aggregation Signal Temporal Logic for Runtime Monitoring in Smart Cities
2020 ACM IEEE 11th International Conference on Cyber-Physical Systems (ICCPS), 2020Co-Authors: Ezio Bartocci, Eli Lifland, John A Stankovic, Lu FengAbstract:We present SaSTL—a novel Spatial Aggregation Signal Temporal Logic—for the efficient runtime monitoring of safety and performance requirements in smart cities. We first describe a study of over 1,000 smart city requirements, some of which can not be specified using existing logic such as Signal Temporal Logic (STL) and its variants. To tackle this limitation, we develop two new logical operators in SaSTL to augment STL for expressing Spatial Aggregation and Spatial counting characteristics that are commonly found in real city requirements. We also develop efficient monitoring algorithms that can check a SaSTL requirement in parallel over multiple data streams (e.g., generated by multiple sensors distributed Spatially in a city). We evaluate our SaSTL monitor by applying to two case studies with large-scale real city sensing data (e.g., up to 10,000 sensors in one requirement). The results show that SaSTL has a much higher coverage expressiveness than other Spatial-temporal logics, and with a significant reduction of computation time for monitoring requirements. We also demonstrate that the SaSTL monitor can help improve the safety and performance of smart cities via simulated experiments.
Ouassim Manout - One of the best experts on this subject based on the ideXlab platform.
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Spatial Aggregation Issues in Traffic Assignment Models
Networks and Spatial Economics, 2020Co-Authors: Ouassim Manout, Patrick Bonnel, François PacullAbstract:Most transport models rely on a discrete description of space, and are, therefore, subject to Spatial Aggregation bias. Spatial Aggregation induces the use of centroid connectors and the omission of intrazonal trips in traffic assignment. This practice is shown to bias main traffic assignment outcomes, especially in Spatially coarse models. To address these modeling errors, the literature suggests some solutions but no clear-cut conclusion on the contribution of these solutions is available. In the current research, we undergo a detailed investigation of the contribution of some of these modeling solutions in order to provide useful and practical recommendations to academics and policy makers. Different assignment strategies that are deemed to mitigate the impacts of Spatial Aggregation in traffic assignment are explored in different case studies. Findings from this research outline that demand-side assignment strategies outperform supply-side methods in addressing the Spatial Aggregation problem. The results also suggest that the inclusion of intrazonal demand in traffic assignment is not sufficient to overcome Aggregation biases. The definition of connectors is also of importance.
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Spatial Aggregation issues in traffic assignment models
Networks and Spatial Economics, 2020Co-Authors: Ouassim ManoutAbstract:Cities are complex systems that urban models can help to comprehend. From simplistic models to more sophisticated ones, urban models have pushed forward our understanding the urban phenomenon and its intricacies. In this context, models can be of great value to policy makers providing that these tools become practical. In this regard, research has put little emphasis on the practicality of urban models and their use under operational conditions.To date, urban models which rely on Spatial Aggregation are the closest possibility to come to practical models. For this reason, the Spatially aggregated modeling framework is widely used. This framework is relatively practical when compared to other modeling frameworks like microsimulation. Nevertheless, Spatial Aggregation is a serious source of bias in these models. This is especially the case of Land-Use and Transport Interaction (LUTI) models and more particularly of Four Step Models.The current PhD is committed to the study of Spatial Aggregation issues in traffic assignment models. Traffic assignment is responsable for the computation of travel times and travel conditions of present and future travel demand. Accessibility measurement, which is at the core of LUTI models, is tightly dependent on traffic assignment modeling and outcomes. Any bias in traffic assignment is likely to corrupt the overall modeling framework. In this context, a special attention is to be paid to Spatial Aggregation in traffic assignment models.In traffic assignment, Spatial Aggregation consists in grouping observations using zones or traffic analysis zones instead of using a continuous representation of space. By design, Aggregation bears an implicit omission in data variability and thus a potential bias if this omission is not random. This is the case with the definition of centroid connectors and the omission of intrazonal demand in traffic assignment. With the use of zones as the basic Spatial units, transport models require the use of centroid connectors to attach zones to the transportation network. Centroid connectors are introduced to model average access and egress conditions to and from the network. Nevertheless, average accessibility conditions are found to be too crude to render accurately accessibility conditions as encountered by trip makers. The current PhD explores the extent of the impact of this Spatial Aggregation bias in the case of transit models and suggests a new modeling strategy to overcome such modeling errors.The use of zones as Spatial units induces a loss of intrazonal data. The omission of intrazonal trips in traffic assignment models is an example of such omission. This research introduces an uncertainty framework to study the statistical impact of ignoring intrazonal trips in traffic assignment models. Findings from this research are used to design new assignment strategies that are more robust towards the omission bias and more generally towards the Spatial Aggregation bias.
Ezio Bartocci - One of the best experts on this subject based on the ideXlab platform.
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sastl Spatial Aggregation signal temporal logic for runtime monitoring in smart cities
arXiv: Computers and Society, 2020Co-Authors: Ezio Bartocci, Eli Lifland, John A Stankovic, Lu FengAbstract:We present SaSTL---a novel Spatial Aggregation Signal Temporal Logic---for the efficient runtime monitoring of safety and performance requirements in smart cities. We first describe a study of over 1,000 smart city requirements, some of which can not be specified using existing logic such as Signal Temporal Logic (STL) and its variants. To tackle this limitation, we develop two new logical operators in SaSTL to augment STL for expressing Spatial Aggregation and Spatial counting characteristics that are commonly found in real city requirements. We also develop efficient monitoring algorithms that can check a SaSTL requirement in parallel over multiple data streams (e.g., generated by multiple sensors distributed Spatially in a city). We evaluate our SaSTL monitor by applying to two case studies with large-scale real city sensing data (e.g., up to 10,000 sensors in one requirement). The results show that SaSTL has a much higher coverage expressiveness than other Spatial-temporal logics, and with a significant reduction of computation time for monitoring requirements. We also demonstrate that the SaSTL monitor can help improve the safety and performance of smart cities via simulated experiments.
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ICCPS - SaSTL: Spatial Aggregation Signal Temporal Logic for Runtime Monitoring in Smart Cities
2020 ACM IEEE 11th International Conference on Cyber-Physical Systems (ICCPS), 2020Co-Authors: Ezio Bartocci, Eli Lifland, John A Stankovic, Lu FengAbstract:We present SaSTL—a novel Spatial Aggregation Signal Temporal Logic—for the efficient runtime monitoring of safety and performance requirements in smart cities. We first describe a study of over 1,000 smart city requirements, some of which can not be specified using existing logic such as Signal Temporal Logic (STL) and its variants. To tackle this limitation, we develop two new logical operators in SaSTL to augment STL for expressing Spatial Aggregation and Spatial counting characteristics that are commonly found in real city requirements. We also develop efficient monitoring algorithms that can check a SaSTL requirement in parallel over multiple data streams (e.g., generated by multiple sensors distributed Spatially in a city). We evaluate our SaSTL monitor by applying to two case studies with large-scale real city sensing data (e.g., up to 10,000 sensors in one requirement). The results show that SaSTL has a much higher coverage expressiveness than other Spatial-temporal logics, and with a significant reduction of computation time for monitoring requirements. We also demonstrate that the SaSTL monitor can help improve the safety and performance of smart cities via simulated experiments.
Eli Lifland - One of the best experts on this subject based on the ideXlab platform.
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sastl Spatial Aggregation signal temporal logic for runtime monitoring in smart cities
arXiv: Computers and Society, 2020Co-Authors: Ezio Bartocci, Eli Lifland, John A Stankovic, Lu FengAbstract:We present SaSTL---a novel Spatial Aggregation Signal Temporal Logic---for the efficient runtime monitoring of safety and performance requirements in smart cities. We first describe a study of over 1,000 smart city requirements, some of which can not be specified using existing logic such as Signal Temporal Logic (STL) and its variants. To tackle this limitation, we develop two new logical operators in SaSTL to augment STL for expressing Spatial Aggregation and Spatial counting characteristics that are commonly found in real city requirements. We also develop efficient monitoring algorithms that can check a SaSTL requirement in parallel over multiple data streams (e.g., generated by multiple sensors distributed Spatially in a city). We evaluate our SaSTL monitor by applying to two case studies with large-scale real city sensing data (e.g., up to 10,000 sensors in one requirement). The results show that SaSTL has a much higher coverage expressiveness than other Spatial-temporal logics, and with a significant reduction of computation time for monitoring requirements. We also demonstrate that the SaSTL monitor can help improve the safety and performance of smart cities via simulated experiments.
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ICCPS - SaSTL: Spatial Aggregation Signal Temporal Logic for Runtime Monitoring in Smart Cities
2020 ACM IEEE 11th International Conference on Cyber-Physical Systems (ICCPS), 2020Co-Authors: Ezio Bartocci, Eli Lifland, John A Stankovic, Lu FengAbstract:We present SaSTL—a novel Spatial Aggregation Signal Temporal Logic—for the efficient runtime monitoring of safety and performance requirements in smart cities. We first describe a study of over 1,000 smart city requirements, some of which can not be specified using existing logic such as Signal Temporal Logic (STL) and its variants. To tackle this limitation, we develop two new logical operators in SaSTL to augment STL for expressing Spatial Aggregation and Spatial counting characteristics that are commonly found in real city requirements. We also develop efficient monitoring algorithms that can check a SaSTL requirement in parallel over multiple data streams (e.g., generated by multiple sensors distributed Spatially in a city). We evaluate our SaSTL monitor by applying to two case studies with large-scale real city sensing data (e.g., up to 10,000 sensors in one requirement). The results show that SaSTL has a much higher coverage expressiveness than other Spatial-temporal logics, and with a significant reduction of computation time for monitoring requirements. We also demonstrate that the SaSTL monitor can help improve the safety and performance of smart cities via simulated experiments.
John A Stankovic - One of the best experts on this subject based on the ideXlab platform.
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sastl Spatial Aggregation signal temporal logic for runtime monitoring in smart cities
arXiv: Computers and Society, 2020Co-Authors: Ezio Bartocci, Eli Lifland, John A Stankovic, Lu FengAbstract:We present SaSTL---a novel Spatial Aggregation Signal Temporal Logic---for the efficient runtime monitoring of safety and performance requirements in smart cities. We first describe a study of over 1,000 smart city requirements, some of which can not be specified using existing logic such as Signal Temporal Logic (STL) and its variants. To tackle this limitation, we develop two new logical operators in SaSTL to augment STL for expressing Spatial Aggregation and Spatial counting characteristics that are commonly found in real city requirements. We also develop efficient monitoring algorithms that can check a SaSTL requirement in parallel over multiple data streams (e.g., generated by multiple sensors distributed Spatially in a city). We evaluate our SaSTL monitor by applying to two case studies with large-scale real city sensing data (e.g., up to 10,000 sensors in one requirement). The results show that SaSTL has a much higher coverage expressiveness than other Spatial-temporal logics, and with a significant reduction of computation time for monitoring requirements. We also demonstrate that the SaSTL monitor can help improve the safety and performance of smart cities via simulated experiments.
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ICCPS - SaSTL: Spatial Aggregation Signal Temporal Logic for Runtime Monitoring in Smart Cities
2020 ACM IEEE 11th International Conference on Cyber-Physical Systems (ICCPS), 2020Co-Authors: Ezio Bartocci, Eli Lifland, John A Stankovic, Lu FengAbstract:We present SaSTL—a novel Spatial Aggregation Signal Temporal Logic—for the efficient runtime monitoring of safety and performance requirements in smart cities. We first describe a study of over 1,000 smart city requirements, some of which can not be specified using existing logic such as Signal Temporal Logic (STL) and its variants. To tackle this limitation, we develop two new logical operators in SaSTL to augment STL for expressing Spatial Aggregation and Spatial counting characteristics that are commonly found in real city requirements. We also develop efficient monitoring algorithms that can check a SaSTL requirement in parallel over multiple data streams (e.g., generated by multiple sensors distributed Spatially in a city). We evaluate our SaSTL monitor by applying to two case studies with large-scale real city sensing data (e.g., up to 10,000 sensors in one requirement). The results show that SaSTL has a much higher coverage expressiveness than other Spatial-temporal logics, and with a significant reduction of computation time for monitoring requirements. We also demonstrate that the SaSTL monitor can help improve the safety and performance of smart cities via simulated experiments.