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Data-Driven Decision Making in Spatial Temporal Tasks.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Data-Driven Decision Making in Spatial Temporal Tasks./
作者:	Zheng, Guanjie.
面頁冊數:	1 online resource (109 pages)
附註:	Source: Dissertations Abstracts International, Volume: 83-03, Section: B.
Contained By:	Dissertations Abstracts International83-03B.
標題:	Water quality. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28767335click for full text (PQDT)
ISBN:	9798535586588

Data-Driven Decision Making in Spatial Temporal Tasks.
Zheng, Guanjie.

Data-Driven Decision Making in Spatial Temporal Tasks. - 1 online resource (109 pages)

Source: Dissertations Abstracts International, Volume: 83-03, Section: B.

Thesis (Ph.D.)--The Pennsylvania State University, 2020.

Includes bibliographical references

With the emerging of pervasive data-collecting technologies and devices, we are collecting a large amount of data to describe human society and nature, e.g., human mobility, water quality, and climate change. Recently, people have been working on revealing the underlying patterns contained in the data, and predicting the future trends of the data. Though these studies may provide important insights of the situations that people are facing, they can not provide actionable policies to change the situation. For instance, traffic-related problems in metropolitan cities, e.g., traffic demand prediction and traffic congestion, have been intensively investigated both in data mining and transportation communities. However, given the known congestion and demand-supply mismatch, a meaningful question is to investigate how to take actions to mitigate the congestion and fulfill the demand. We call these problems decision-making problems. Making decisions has been a heated research area in different domains. For instance, in the transportation domain, people have applied methods from transportation engineering and control theories to control the traffic signals. These methods assume the arrivals of vehicles follow some pre-defined traffic models, and convert the decision problem to an optimization problem. However, these simplified assumptions might deviate from the real-world data observations, and hence lead to sub-optimal decisions. Recently, reinforcement learning approaches achieve great success in the decision-making problems in virtual environments, e.g., Go game and Atari game. They directly learn from the data via a trial-and-error search. However, these methods require a decent number of interactions with the environment before the algorithms converge. In real-world problems, every interaction means real cost (e.g., traffic congestion, traffic accidents). Hence, a more data-efficient method is necessary for use in the real world.In this dissertation, I will show the four stages along the pipeline of data-driven decision making, including identifying a problem (e.g., traffic congestion, environmental pollution), objective design, learning a policy (e.g., traffic signal control policy), and transferring policies to the real world. I have developed innovative solid techniques to tackle the tasks in each stage. In addition, I have applied the developed methods in different domains, including urban traffic prediction, traffic signal control, news recommendation, and pollution detection and control. We have achieved significant improvement over the state-of-the-art or currently employed methods, which will provide us with promising solutions to improve real-world situations.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798535586588Subjects--Topical Terms:

556913
Water quality.
Subjects--Index Terms:

Decision makingIndex Terms--Genre/Form:

542853
Electronic books.

Data-Driven Decision Making in Spatial Temporal Tasks.
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