{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/155480"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/155480","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"A Community-Based Approach for Hub Placements","abstract":"Advanced Air Mobility (AAM) is a rapidly emerging sector in the aerospace industry that seeks to revolutionize transportation by integrating highly automated aircraft into the airspace. As AAM technology matures, establishing a network framework and strategic hub locations becomes crucial for transitioning from theoretical models to practical applications in transportation systems. This thesis investigates community-based strategies for hub placement within the AAM infrastructure. More specifically, it utilizes network segmentation to decompose a network into communities to simplify the hub selection process into more manageable sub-problems. Our first contribution is the development of a specialized community detection methodology called Directed Flow Communities (DFC), which is designed to accommodate the attributes of transportation networks. Next, we conduct a case study using the Freight Analysis Framework (FAF) dataset as a proxy for AAM demand. The empirical investigation focuses on three key sectors: pharmaceuticals, electronics, and comprehensive freight flows, each presenting distinct challenges and insights into the network’s structure. The findings show the effectiveness of the community detection-based methods in unveiling cost-efficient hub locations.","abstract_html":"Advanced Air Mobility (AAM) is a rapidly emerging sector in the aerospace industry that seeks to revolutionize transportation by integrating highly automated aircraft into the airspace. As AAM technology matures, establishing a network framework and strategic hub locations becomes crucial for transitioning from theoretical models to practical applications in transportation systems. This thesis investigates community-based strategies for hub placement within the AAM infrastructure. More specifically, it utilizes network segmentation to decompose a network into communities to simplify the hub selection process into more manageable sub-problems. Our first contribution is the development of a specialized community detection methodology called Directed Flow Communities (DFC), which is designed to accommodate the attributes of transportation networks. Next, we conduct a case study using the Freight Analysis Framework (FAF) dataset as a proxy for AAM demand. The empirical investigation focuses on three key sectors: pharmaceuticals, electronics, and comprehensive freight flows, each presenting distinct challenges and insights into the network’s structure. The findings show the effectiveness of the community detection-based methods in unveiling cost-efficient hub locations.","abstract_has_math":false,"creators":["Chavalithumrong, Alissa"],"institution":"Massachusetts Institute of Technology","degree_name":"Master","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. 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As AAM technology matures, establishing a network framework and strategic hub locations becomes crucial for transitioning from theoretical models to practical applications in transportation systems. This thesis investigates community-based strategies for hub placement within the AAM infrastructure. More specifically, it utilizes network segmentation to decompose a network into communities to simplify the hub selection process into more manageable sub-problems. Our first contribution is the development of a specialized community detection methodology called Directed Flow Communities (DFC), which is designed to accommodate the attributes of transportation networks. Next, we conduct a case study using the Freight Analysis Framework (FAF) dataset as a proxy for AAM demand. The empirical investigation focuses on three key sectors: pharmaceuticals, electronics, and comprehensive freight flows, each presenting distinct challenges and insights into the network’s structure. 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