Cooper Grever, Debora Kropp, Joshua Smith, Tori Monteleone
{"title":"Railway Transportation Expansion and Resource Coverage Analysis in Nigeria","authors":"Cooper Grever, Debora Kropp, Joshua Smith, Tori Monteleone","doi":"10.1109/SIEDS.2019.8735650","DOIUrl":null,"url":null,"abstract":"The Federal Republic of Nigeria has extensive natural resources and significant economic potential, but portions of the population suffer from poverty and lack of access to essential supplies, such as food and vaccines, due to geographic isolation. Railways offer a stable, practical method to transport resources over large distances. Railway infrastructure has also been shown to increase welfare of isolated communities and while the current railway network is limited in structure and technology, renewed government interest provides an opportunity to expand the current Nigerian railway. This paper describes the construction of a data-driven railway development map to increase coverage for the resource of interest, vaccines, to remote areas of Nigeria. A dataset of hundreds of potential city-hubs was evaluated and minimized based on coverage radius. Multiple railway networks were modeled to maximize coverage through both a minimum spanning tree and traveling salesman method while constrained by minimal distance. These models were then refined by applying nominal scoring to the city-hubs. To calculate scores, the significant factors for vaccination coverage were determined on a state-by-state basis by a stepwise regression. The coefficient values of the normalized significant factors were applied to city-hubs through nominal scoring based on their state and population. The expanded network and selected city-hubs were implemented into a geospatial map of Nigeria to provide a data-driven display for recommended railway expansions. The networks were further constrained by topographic obstacles and a forecasting model was developed based on population growth and the expanded railway. The methodology established in this project can be adapted to assist other developing countries facing similar challenges.","PeriodicalId":265421,"journal":{"name":"2019 Systems and Information Engineering Design Symposium (SIEDS)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 Systems and Information Engineering Design Symposium (SIEDS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SIEDS.2019.8735650","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
The Federal Republic of Nigeria has extensive natural resources and significant economic potential, but portions of the population suffer from poverty and lack of access to essential supplies, such as food and vaccines, due to geographic isolation. Railways offer a stable, practical method to transport resources over large distances. Railway infrastructure has also been shown to increase welfare of isolated communities and while the current railway network is limited in structure and technology, renewed government interest provides an opportunity to expand the current Nigerian railway. This paper describes the construction of a data-driven railway development map to increase coverage for the resource of interest, vaccines, to remote areas of Nigeria. A dataset of hundreds of potential city-hubs was evaluated and minimized based on coverage radius. Multiple railway networks were modeled to maximize coverage through both a minimum spanning tree and traveling salesman method while constrained by minimal distance. These models were then refined by applying nominal scoring to the city-hubs. To calculate scores, the significant factors for vaccination coverage were determined on a state-by-state basis by a stepwise regression. The coefficient values of the normalized significant factors were applied to city-hubs through nominal scoring based on their state and population. The expanded network and selected city-hubs were implemented into a geospatial map of Nigeria to provide a data-driven display for recommended railway expansions. The networks were further constrained by topographic obstacles and a forecasting model was developed based on population growth and the expanded railway. The methodology established in this project can be adapted to assist other developing countries facing similar challenges.