Modeling movement decisions in networks: A discrete choice model approach
In this dissertation, we address the subject of modeling and simulation of agents and their movement decision in a network environment. We emphasize the development of high quality agent-based simulation models as a prerequisite before utilization of the model as an evaluation tool for various recom...
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| Format: | text |
| Language: | English |
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Institutional Knowledge at Singapore Management University
2018
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| Online Access: | https://ink.library.smu.edu.sg/etd_coll/165 https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=1165&context=etd_coll |
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| Institution: | Singapore Management University |
| Language: | English |
| Summary: | In this dissertation, we address the subject of modeling and simulation of agents and their movement decision in a network environment. We emphasize the development of high quality agent-based simulation models as a prerequisite before utilization of the model as an evaluation tool for various recommender systems and policies. To achieve this, we propose a methodological framework for development of agent-based models, combining approaches such as discrete choice models and data-driven modeling.
The discrete choice model is widely used in the field of transportation, with a distinct utility function (e.g., demand or revenue-driven). Through discrete choice models, the movement decision of agents are dependent on a utility function, where every agent chooses a travel option (e.g., travel to a link) out of a finite set. In our work, not only do we demonstrate the effectiveness of this model in the field of transportation with a multiagent simulation model and a tiered decision model, we demonstrate our approach in other domains (i.e., leisure and migration). where the utility function might not be as clear, or involve various qualitative variables.
The contribution of this dissertation is therefore two-fold. We first propose a methodological framework for development of agent-based models under the conditions of varying data observability and network model scale. Thereafter, we demonstrate the applicability of the proposed framework through the use of three case studies, each representing a different problem domain. |
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