{"title":"机场陆侧快速交通网络设计的多目标决策方法。","authors":"Danwen Bao, Shijia Tian, Rui Li, Tianxuan Zhang, Ting Zhu","doi":"10.1007/s11067-022-09571-y","DOIUrl":null,"url":null,"abstract":"<p><p>To better deploy the landside rapid transit network for large airports, this study proposes a multi-objective transit network design model to maximize passenger demand coverage, reduce passenger travel time and minimize operational cost simultaneously. This model is formulated as an equivalent integer programming problem by predefining the transportation corridors and passengers' OD pairs. A branch-and-cut algorithm is proposed to find a non-inferior solution set. We also conduct trade-off analysis between efficiency, effectiveness and equity under each deployment strategy using the modified Gini coefficient method. The effectiveness of the proposed model and solution algorithm are tested with rapid transit network of the Beijing Capital International Airport. Results show that among the three common network topologies, including star, tree and finger, the passenger demand coverage and travel time reduction per unit cost under star topology outperform the other two topologies. As for finger topology, the performances of the passenger demand coverage and travel time reduction are the best among the three, but the cost is the poorest. In addition, the trade-off analysis shows that the solution whose objective is to maximize passenger demand coverage has a higher efficiency and a lower unit cost than the solution whose objective is to reduce travel time. However, the latter has a higher level of equity, especially for the medium and low-cost solutions. The proposed method in this study can help the decision makers to design effective landside rapid transit networks for large airports to improve the service level.</p>","PeriodicalId":54733,"journal":{"name":"Networks & Spatial Economics","volume":"22 4","pages":"767-801"},"PeriodicalIF":1.6000,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9244575/pdf/","citationCount":"1","resultStr":"{\"title\":\"Multi-Objective Decision Method for Airport Landside Rapid Transit Network Design.\",\"authors\":\"Danwen Bao, Shijia Tian, Rui Li, Tianxuan Zhang, Ting Zhu\",\"doi\":\"10.1007/s11067-022-09571-y\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>To better deploy the landside rapid transit network for large airports, this study proposes a multi-objective transit network design model to maximize passenger demand coverage, reduce passenger travel time and minimize operational cost simultaneously. This model is formulated as an equivalent integer programming problem by predefining the transportation corridors and passengers' OD pairs. A branch-and-cut algorithm is proposed to find a non-inferior solution set. We also conduct trade-off analysis between efficiency, effectiveness and equity under each deployment strategy using the modified Gini coefficient method. The effectiveness of the proposed model and solution algorithm are tested with rapid transit network of the Beijing Capital International Airport. Results show that among the three common network topologies, including star, tree and finger, the passenger demand coverage and travel time reduction per unit cost under star topology outperform the other two topologies. As for finger topology, the performances of the passenger demand coverage and travel time reduction are the best among the three, but the cost is the poorest. In addition, the trade-off analysis shows that the solution whose objective is to maximize passenger demand coverage has a higher efficiency and a lower unit cost than the solution whose objective is to reduce travel time. However, the latter has a higher level of equity, especially for the medium and low-cost solutions. The proposed method in this study can help the decision makers to design effective landside rapid transit networks for large airports to improve the service level.</p>\",\"PeriodicalId\":54733,\"journal\":{\"name\":\"Networks & Spatial Economics\",\"volume\":\"22 4\",\"pages\":\"767-801\"},\"PeriodicalIF\":1.6000,\"publicationDate\":\"2022-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9244575/pdf/\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Networks & Spatial Economics\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1007/s11067-022-09571-y\",\"RegionNum\":3,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2022/6/25 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q3\",\"JCRName\":\"OPERATIONS RESEARCH & MANAGEMENT SCIENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Networks & Spatial Economics","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1007/s11067-022-09571-y","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2022/6/25 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"OPERATIONS RESEARCH & MANAGEMENT SCIENCE","Score":null,"Total":0}
Multi-Objective Decision Method for Airport Landside Rapid Transit Network Design.
To better deploy the landside rapid transit network for large airports, this study proposes a multi-objective transit network design model to maximize passenger demand coverage, reduce passenger travel time and minimize operational cost simultaneously. This model is formulated as an equivalent integer programming problem by predefining the transportation corridors and passengers' OD pairs. A branch-and-cut algorithm is proposed to find a non-inferior solution set. We also conduct trade-off analysis between efficiency, effectiveness and equity under each deployment strategy using the modified Gini coefficient method. The effectiveness of the proposed model and solution algorithm are tested with rapid transit network of the Beijing Capital International Airport. Results show that among the three common network topologies, including star, tree and finger, the passenger demand coverage and travel time reduction per unit cost under star topology outperform the other two topologies. As for finger topology, the performances of the passenger demand coverage and travel time reduction are the best among the three, but the cost is the poorest. In addition, the trade-off analysis shows that the solution whose objective is to maximize passenger demand coverage has a higher efficiency and a lower unit cost than the solution whose objective is to reduce travel time. However, the latter has a higher level of equity, especially for the medium and low-cost solutions. The proposed method in this study can help the decision makers to design effective landside rapid transit networks for large airports to improve the service level.
期刊介绍:
Networks and Spatial Economics (NETS) is devoted to the mathematical and numerical study of economic activities facilitated by human infrastructure, broadly defined to include technologies pertinent to information, telecommunications, the Internet, transportation, energy storage and transmission, and water resources. Because the spatial organization of infrastructure most generally takes the form of networks, the journal encourages submissions that employ a network perspective. However, non-network continuum models are also recognized as an important tradition that has provided great insight into spatial economic phenomena; consequently, the journal welcomes with equal enthusiasm submissions based on continuum models.
The journal welcomes the full spectrum of high quality work in networks and spatial economics including theoretical studies, case studies and algorithmic investigations, as well as manuscripts that combine these aspects. Although not devoted exclusively to theoretical studies, the journal is "theory-friendly". That is, well thought out theoretical analyses of important network and spatial economic problems will be considered without bias even if they do not include case studies or numerical examples.