The Pervasive Challenge of Error and Uncertainty in Geospatial Data

Wechsler, Suzanne P.; Ban, Hyowon; Li, Linna

doi:10.1007/978-3-030-04750-4_16

Cited by 4 publications

(2 citation statements)

References 32 publications

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“…Thus, in a traffic flow forecasting system robustness could only imply that the system does not crack when input data fail [ 109 ], and it continues to operate; on the other hand, for critical systems such as air traffic management, robustness would require additional measurements to contain damage [ 110 , 111 ]. All in all, robust data-based workflows should be able to accommodate unseen operational circumstances, such as data distribution shifts or unprecedented levels of information uncertainty, which particularly prevail in crowdsourced and Social Media data [ 112 , 113 ]. Stable and resilient : Actionable systems require a certain output stability in order to be understandable by their users.…”

Section: Functional Requirements For Model Actionabilitymentioning

confidence: 99%

From Data to Actions in Intelligent Transportation Systems: A Prescription of Functional Requirements for Model Actionability

Laña

Medina

Vlahogianni

et al. 2021

Sensors

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Advances in Data Science permeate every field of Transportation Science and Engineering, resulting in developments in the transportation sector that are data-driven. Nowadays, Intelligent Transportation Systems (ITS) could be arguably approached as a “story” intensively producing and consuming large amounts of data. A diversity of sensing devices densely spread over the infrastructure, vehicles or the travelers’ personal devices act as sources of data flows that are eventually fed into software running on automatic devices, actuators or control systems producing, in turn, complex information flows among users, traffic managers, data analysts, traffic modeling scientists, etc. These information flows provide enormous opportunities to improve model development and decision-making. This work aims to describe how data, coming from diverse ITS sources, can be used to learn and adapt data-driven models for efficiently operating ITS assets, systems and processes; in other words, for data-based models to fully become actionable. Grounded in this described data modeling pipeline for ITS, we define the characteristics, engineering requisites and challenges intrinsic to its three compounding stages, namely, data fusion, adaptive learning and model evaluation. We deliberately generalize model learning to be adaptive, since, in the core of our paper is the firm conviction that most learners will have to adapt to the ever-changing phenomenon scenario underlying the majority of ITS applications. Finally, we provide a prospect of current research lines within Data Science that can bring notable advances to data-based ITS modeling, which will eventually bridge the gap towards the practicality and actionability of such models.

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Section: Functional Requirements For Model Actionabilitymentioning

confidence: 99%

From Data to Actions in Intelligent Transportation Systems: A Prescription of Functional Requirements for Model Actionability

Laña

Medina

Vlahogianni

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…Thus, in a traffic flow forecasting system robustness could only imply that the system does not crack when input data fail [104], and it continues to operate; on the other hand, for critical systems such as air traffic management, robustness would require additional measurements to contain damage [105], [106]. All in all, robust data-based workflows should be able to accommodate unseen operational circumstances, such as data distribution shifts or unprecedented levels of information uncertainty, which particularly prevail in crowdsourced and Social Media data [107], [108].…”

Section: B Self-sustainabilitymentioning

confidence: 99%

From Data to Actions in Intelligent Transportation Systems: a Prescription of Functional Requirements for Model Actionability

Laña¹,

Sánchez-Medina²,

Vlahogianni³

et al. 2020

Preprint

View full text Add to dashboard Cite

Advances in Data Science permeate every field of Transportation Science and Engineering, making it straightforward to imagine that developments in the transportation sector will be data-driven. Nowadays, Intelligent Transportation Systems (ITS) could be arguably approached as a "story" intensively producing and consuming large amounts of data. A diversity of sensing devices densely spread over the infrastructure, vehicles or the travelers' personal devices act as sources of data flows that are eventually fed to software running on automatic devices, actuators or control systems producing, in turn, complex information flows between users, traffic managers, data analysts, traffic modeling scientists, etc. These information flows provide enormous opportunities to improve model development and decision-making. This work aims to describe how data, coming from diverse ITS sources, can be used to learn and adapt data-driven models for efficiently operating ITS assets, systems and processes; in other words, for data-based models to fully become actionable. Grounded on this described data modeling pipeline for ITS, we define the characteristics, engineering requisites and challenges intrinsic to its three compounding stages, namely, data fusion, adaptive learning and model evaluation. We deliberately generalize model learning to be adaptive, since, in the core of our paper is the firm conviction that most learners will have to adapt to the everchanging phenomenon scenario underlying the majority of ITS applications. Finally, we provide a prospect of current research lines within Data Science that can bring notable advances to data-based ITS modeling, which will eventually bridge the gap towards the practicality and actionability of such models.

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In what kinds of communities do people on the sex offender registry live? An analysis of ten states

Drake

Yejin

Morrison

et al. 2021

Children and Youth Services Review

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The Pervasive Challenge of Error and Uncertainty in Geospatial Data

Cited by 4 publications

References 32 publications

From Data to Actions in Intelligent Transportation Systems: A Prescription of Functional Requirements for Model Actionability

From Data to Actions in Intelligent Transportation Systems: A Prescription of Functional Requirements for Model Actionability

From Data to Actions in Intelligent Transportation Systems: a Prescription of Functional Requirements for Model Actionability

In what kinds of communities do people on the sex offender registry live? An analysis of ten states

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