Mazhar Hussain, M. O’nils, J. Lundgren, M. Carratù, Irida Shallari
{"title":"基于数据融合的城市公交车油耗预测参数选择","authors":"Mazhar Hussain, M. O’nils, J. Lundgren, M. Carratù, Irida Shallari","doi":"10.1109/SAS54819.2022.9881365","DOIUrl":null,"url":null,"abstract":"The study aims to explore the fuel consumption of city buses with data fusion using a dataset with multiple parameters such as travelled distance, weekday, hour of the day, drivers, buses, and routes, that influence the trip fuel consumption. In this study, manipulated parameters such as modified driver, bus and route identification numbers are used together with original parameters to identify the optimal combination of parameters that can be used to enhance the accuracy of the prediction model. Two regression methods, i.e. cubic SVM and artificial neural networks (ANN), are used to demonstrate the performance of the proposed approach. Results shows that a combination of original parameters and processed parameters increases the performance.","PeriodicalId":129732,"journal":{"name":"2022 IEEE Sensors Applications Symposium (SAS)","volume":" 5","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Selection of optimal parameters to predict fuel consumption of city buses using data fusion\",\"authors\":\"Mazhar Hussain, M. O’nils, J. Lundgren, M. Carratù, Irida Shallari\",\"doi\":\"10.1109/SAS54819.2022.9881365\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The study aims to explore the fuel consumption of city buses with data fusion using a dataset with multiple parameters such as travelled distance, weekday, hour of the day, drivers, buses, and routes, that influence the trip fuel consumption. In this study, manipulated parameters such as modified driver, bus and route identification numbers are used together with original parameters to identify the optimal combination of parameters that can be used to enhance the accuracy of the prediction model. Two regression methods, i.e. cubic SVM and artificial neural networks (ANN), are used to demonstrate the performance of the proposed approach. Results shows that a combination of original parameters and processed parameters increases the performance.\",\"PeriodicalId\":129732,\"journal\":{\"name\":\"2022 IEEE Sensors Applications Symposium (SAS)\",\"volume\":\" 5\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 IEEE Sensors Applications Symposium (SAS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SAS54819.2022.9881365\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE Sensors Applications Symposium (SAS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SAS54819.2022.9881365","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Selection of optimal parameters to predict fuel consumption of city buses using data fusion
The study aims to explore the fuel consumption of city buses with data fusion using a dataset with multiple parameters such as travelled distance, weekday, hour of the day, drivers, buses, and routes, that influence the trip fuel consumption. In this study, manipulated parameters such as modified driver, bus and route identification numbers are used together with original parameters to identify the optimal combination of parameters that can be used to enhance the accuracy of the prediction model. Two regression methods, i.e. cubic SVM and artificial neural networks (ANN), are used to demonstrate the performance of the proposed approach. Results shows that a combination of original parameters and processed parameters increases the performance.