PhD Dissertation Defense by CHEN Cen

Recommending Personalized Schedules in Urban Environments

Rapid urbanization is a social phenomenon, that has led to huge increase in demand vis-à-vis supply of public facilities and services in urban cities. In response to that, city planners may invest on building more infrastructures. At the same time, greater emphasis should be placed on better coordination mechanisms to make use of the existing infrastructures so as to improve service efficiency. In this thesis, we are broadly interested in solving real world problems that involve decision support for coordinating agent movements in dynamic urban environments, where people are agents exhibiting different human behavior patterns and preferences. The rapid development of mobile technologies makes it easier to capture agent behavioral and preference information. Such rich agent-specific information opens many opportunities that we could potentially leverage, to better guide/influence the agents in urban environments.

The purpose of this thesis is to investigate how we can effectively and efficiently guide and coordinate the agents with a personal touch, which entails optimized resource allocation and scheduling at the operational level. More specifically, we look into the agent coordination from three specific aspects with different application domains: (a) crowd control in leisure environments by providing personalized guidance to individual agents to smooth the congestions due to the crowd; (b) mobile crowdsourcing by distributing location-based tasks to part-time crowd workers on-the-go to promote the platform efficiency; (c) workforce scheduling by better utilizing full-time workforce to provide location-based services at customers' homes. For each, we propose models and efficient algorithms, considering agent-level preferences and problem-specific requirements. The proposed solution approaches are shown to be effective through various experiments on real-world and synthetic datasets.