Sequential decision making for improving efficiency in urban environments

Rapid "urbanization" (more than 50% of world's population now resides in cities) coupled with the natural lack of coordination in usage of common resources (ex: bikes, ambulances, taxis, traffic personnel, attractions) has a detrimental effect on a wide variety of response (ex: waitin...

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Bibliographic Details
Main Author: Pradeep VARAKANTHAM
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2016
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Online Access:https://ink.library.smu.edu.sg/sis_research/3601
https://ink.library.smu.edu.sg/context/sis_research/article/4602/viewcontent/Sequential_decision_making_for_improving_efficiency_in_urban_environments.pdf
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Institution: Singapore Management University
Language: English
Description
Summary:Rapid "urbanization" (more than 50% of world's population now resides in cities) coupled with the natural lack of coordination in usage of common resources (ex: bikes, ambulances, taxis, traffic personnel, attractions) has a detrimental effect on a wide variety of response (ex: waiting times, response time for emergency needs) and coverage metrics (ex: predictability of traffic/security patrols) in cities of today. Motivated by the need to improve response and coverage metrics in urban environments, my research group is focussed on building intelligent agent systems that make sequential decisions to continuously match available supply of resources to an uncertain demand for resources. Our broad approach to generating these sequential decision strategies is through a combination of data analytics (to obtain a model) and multistage optimization (planning/scheduling) under uncertainty (to solve the model). While we perform data analytics, our contributions are focussed on multi-stage optimization under uncertainty. We exploit key properties of urban environments, namely homogeneity and anonymity, limited influence of individual entities, abstraction and near decomposability to solve "multi-stage optimization under uncertainty" effectively and efficiently.