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Spatio-Temporal Representation Learn...

Wong, Vivian W.H.,

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Spatio-Temporal Representation Learning: Applications to Manufacturing Planning and Pedestrian Crowd Analysis /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Spatio-Temporal Representation Learning: Applications to Manufacturing Planning and Pedestrian Crowd Analysis // Vivian W.H Wong.
作者:	Wong, Vivian W.H.,
面頁冊數:	1 electronic resource (134 pages)
附註:	Source: Dissertations Abstracts International, Volume: 86-03, Section: B.
Contained By:	Dissertations Abstracts International86-03B.
標題:	Table tennis. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31460291
ISBN:	9798384340195

Spatio-Temporal Representation Learning: Applications to Manufacturing Planning and Pedestrian Crowd Analysis /
Wong, Vivian W.H.,

Spatio-Temporal Representation Learning: Applications to Manufacturing Planning and Pedestrian Crowd Analysis /Vivian W.H Wong. - 1 electronic resource (134 pages)

Source: Dissertations Abstracts International, Volume: 86-03, Section: B.

The recent availability of data and advancements in computational tools have opened up new possibilities and challenges in urban planning and city management. Data-driven spatio-temporal models are continuously being developed to study complex activities within cities, such as supply chain (the flow of goods) and urban mobility (the flow of people). This thesis focuses on applying spatio-temporal representation learning techniques to two main application domains: manufacturing planning and pedestrian crowd analysis. In the manufacturing planning domain, we address the interruptive swap-allowed job shop scheduling problem (ISBJSSP), using graph neural networks (GNNs) and reinforcement learning (RL) to generate adaptive scheduling policies. We develop a simulator to model real-world manufacturing constraints and validate our approach using benchmark instances.In the pedestrian crowd analysis domain, we propose a computer vision-based procedure to generate crowd data from raw surveillance footage and spatial connectivity priors, introducing crowd mobility graphs (CMGraphs) to represent aggregated crowd flow. Subsequently, we formulate the crowd flow forecasting problem to predict future crowd states and design a neural network framework (STEN) that utilizes spatial and temporal information for this task. We work with both public single-camera video data (Grand Central Station dataset) and real-world collected multi-camera video data (Campus Crowd dataset) to validate our approaches and provide resources for future research.The main contributions of this thesis are threefold: (1) spatio-temporal data acquisition from three sources: simulated, public single-camera, and real-world multi-camera; (2) problem definition and spatio-temporal graph representation of two problems: ISBJSSP and crowd flow forecasting; and (3) two deep learning model implementation to generate solutions for the defined problems under architectural frameworks: GNN-RL and STEN.

English

ISBN: 9798384340195Subjects--Topical Terms:

548794
Table tennis.

Spatio-Temporal Representation Learning: Applications to Manufacturing Planning and Pedestrian Crowd Analysis /
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