An integrated framework for modeling and predicting spatiotemporal phenomena in urban environments

This thesis proposes a general solution framework that integrates methods in machine learning in creative ways to solve a diverse set of problems arising in urban environments. It particularly focuses on modeling spatiotemporal data for the purpose of predicting urban phenomena. Concretely, the fram...

Full description

Saved in:
Bibliographic Details
Main Author: LE, Tuc Viet
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2017
Subjects:
Online Access:https://ink.library.smu.edu.sg/etd_coll/141
https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=1140&context=etd_coll
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Singapore Management University
Language: English
id sg-smu-ink.etd_coll-1140
record_format dspace
spelling sg-smu-ink.etd_coll-11402019-07-11T07:43:59Z An integrated framework for modeling and predicting spatiotemporal phenomena in urban environments LE, Tuc Viet This thesis proposes a general solution framework that integrates methods in machine learning in creative ways to solve a diverse set of problems arising in urban environments. It particularly focuses on modeling spatiotemporal data for the purpose of predicting urban phenomena. Concretely, the framework is applied to solve three specific real-world problems: human mobility prediction, trac speed prediction and incident prediction. For human mobility prediction, I use visitor trajectories collected a large theme park in Singapore as a simplified microcosm of an urban area. A trajectory is an ordered sequence of attraction visits and corresponding timestamps produced by a visitor. This problem has two related subproblems: (spatial) bundle prediction and trajectory prediction. In the first problem, I apply the framework to predict a bundle (i.e., an unordered set) of attractions that a given visitor would visit given a time budget. In the second problem, the framework is applied to predict the visitor's actual trajectory given the current partial trajectory and time budget. In both problems, I apply the methods of trajectory clustering, hidden Markov model, revealed preference learning and (inverse) reinforcement learning in the integrated framework. In trac speed prediction, I wish to predict the spatiotemporal distribution of trac speed over urban road networks. To this end, I propose local Gaussian processes which combine non-negative matrix (NMF) factorization with Gaussian process (GP) in order to enhance the efficiency of model training such that the solution could be deployed in real-time use cases. NMF is essentially a spatiotemporal clustering technique. The solution is extensively evaluated using real-world trac data collected in two U.S. cities. The incident prediction problem is about predicting the distribution of the number of crime incidents over urban areas in future time periods. Because of its similarity to the trac prediction problem above, its solution greatly benefits from the GP model developed earlier. Particularly, the GP kernel function is inherited and extended to model the distribution of incidents in urban areas and their features. The proposed solution is evaluated using real-world incident data collected in a large Asian city. Conceptually, this thesis uses machine learning techniques to solve three separate urban problems, whose contribution belongs to the large category of urban computing. At the core, its technical contribution lies in the unification of separate solutions tailored to those problems into an integrated framework that reasons with spatiotemporal data and, thus, is highly generalizable to other problems of similar nature. 2017-11-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/etd_coll/141 https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=1140&context=etd_coll http://creativecommons.org/licenses/by-nc-nd/4.0/ Dissertations and Theses Collection (Open Access) eng Institutional Knowledge at Singapore Management University framework machine learning spatiotemporal geospatial reinforcement learning craussian process Artificial Intelligence and Robotics Databases and Information Systems Urban Studies and Planning
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic framework
machine learning
spatiotemporal
geospatial
reinforcement learning
craussian process
Artificial Intelligence and Robotics
Databases and Information Systems
Urban Studies and Planning
spellingShingle framework
machine learning
spatiotemporal
geospatial
reinforcement learning
craussian process
Artificial Intelligence and Robotics
Databases and Information Systems
Urban Studies and Planning
LE, Tuc Viet
An integrated framework for modeling and predicting spatiotemporal phenomena in urban environments
description This thesis proposes a general solution framework that integrates methods in machine learning in creative ways to solve a diverse set of problems arising in urban environments. It particularly focuses on modeling spatiotemporal data for the purpose of predicting urban phenomena. Concretely, the framework is applied to solve three specific real-world problems: human mobility prediction, trac speed prediction and incident prediction. For human mobility prediction, I use visitor trajectories collected a large theme park in Singapore as a simplified microcosm of an urban area. A trajectory is an ordered sequence of attraction visits and corresponding timestamps produced by a visitor. This problem has two related subproblems: (spatial) bundle prediction and trajectory prediction. In the first problem, I apply the framework to predict a bundle (i.e., an unordered set) of attractions that a given visitor would visit given a time budget. In the second problem, the framework is applied to predict the visitor's actual trajectory given the current partial trajectory and time budget. In both problems, I apply the methods of trajectory clustering, hidden Markov model, revealed preference learning and (inverse) reinforcement learning in the integrated framework. In trac speed prediction, I wish to predict the spatiotemporal distribution of trac speed over urban road networks. To this end, I propose local Gaussian processes which combine non-negative matrix (NMF) factorization with Gaussian process (GP) in order to enhance the efficiency of model training such that the solution could be deployed in real-time use cases. NMF is essentially a spatiotemporal clustering technique. The solution is extensively evaluated using real-world trac data collected in two U.S. cities. The incident prediction problem is about predicting the distribution of the number of crime incidents over urban areas in future time periods. Because of its similarity to the trac prediction problem above, its solution greatly benefits from the GP model developed earlier. Particularly, the GP kernel function is inherited and extended to model the distribution of incidents in urban areas and their features. The proposed solution is evaluated using real-world incident data collected in a large Asian city. Conceptually, this thesis uses machine learning techniques to solve three separate urban problems, whose contribution belongs to the large category of urban computing. At the core, its technical contribution lies in the unification of separate solutions tailored to those problems into an integrated framework that reasons with spatiotemporal data and, thus, is highly generalizable to other problems of similar nature.
format text
author LE, Tuc Viet
author_facet LE, Tuc Viet
author_sort LE, Tuc Viet
title An integrated framework for modeling and predicting spatiotemporal phenomena in urban environments
title_short An integrated framework for modeling and predicting spatiotemporal phenomena in urban environments
title_full An integrated framework for modeling and predicting spatiotemporal phenomena in urban environments
title_fullStr An integrated framework for modeling and predicting spatiotemporal phenomena in urban environments
title_full_unstemmed An integrated framework for modeling and predicting spatiotemporal phenomena in urban environments
title_sort integrated framework for modeling and predicting spatiotemporal phenomena in urban environments
publisher Institutional Knowledge at Singapore Management University
publishDate 2017
url https://ink.library.smu.edu.sg/etd_coll/141
https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=1140&context=etd_coll
_version_ 1712300892265381888