{"title":"印尼群岛地区货运发电需求模型参数稳定性研究","authors":"Imam Sonny, A. BenAbdelhafid, S. Hadiwardoyo","doi":"10.1109/ICADLT.2013.6568432","DOIUrl":null,"url":null,"abstract":"This paper proposes a commodity freight generation demand model in such a way that the movement of commodities is explained by aggregate commodities production and attraction by zone. The proposed model is a modification of the first step of traditional four-step approach, which utilizes regression analysis. Regression analyses correlate freight production and freight attraction based on land use and natural resources productivity of the selected region. The analysis compares eight regression models within nine parameters. The research shows the using of multiple-linear regression and non-linear regression using inverse function to be more accurate and elastic than the other regression analysis.","PeriodicalId":269509,"journal":{"name":"2013 International Conference on Advanced Logistics and Transport","volume":"59 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Parameter stability in regional freight generation demand modeling in archipelagic regions of Indonesia\",\"authors\":\"Imam Sonny, A. BenAbdelhafid, S. Hadiwardoyo\",\"doi\":\"10.1109/ICADLT.2013.6568432\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper proposes a commodity freight generation demand model in such a way that the movement of commodities is explained by aggregate commodities production and attraction by zone. The proposed model is a modification of the first step of traditional four-step approach, which utilizes regression analysis. Regression analyses correlate freight production and freight attraction based on land use and natural resources productivity of the selected region. The analysis compares eight regression models within nine parameters. The research shows the using of multiple-linear regression and non-linear regression using inverse function to be more accurate and elastic than the other regression analysis.\",\"PeriodicalId\":269509,\"journal\":{\"name\":\"2013 International Conference on Advanced Logistics and Transport\",\"volume\":\"59 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-05-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 International Conference on Advanced Logistics and Transport\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICADLT.2013.6568432\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 International Conference on Advanced Logistics and Transport","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICADLT.2013.6568432","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Parameter stability in regional freight generation demand modeling in archipelagic regions of Indonesia
This paper proposes a commodity freight generation demand model in such a way that the movement of commodities is explained by aggregate commodities production and attraction by zone. The proposed model is a modification of the first step of traditional four-step approach, which utilizes regression analysis. Regression analyses correlate freight production and freight attraction based on land use and natural resources productivity of the selected region. The analysis compares eight regression models within nine parameters. The research shows the using of multiple-linear regression and non-linear regression using inverse function to be more accurate and elastic than the other regression analysis.