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dc.contributor.authorHoffman, Alwyn J.
dc.date.accessioned2019-11-18T08:53:00Z
dc.date.available2019-11-18T08:53:00Z
dc.date.issued2019
dc.identifier.citationHoffman, A. J. 2019. Proceedings of the 11th International Joint Conference on Computational Intelligence, Vienna, Austria, 17-19 Sep: 379-390. [http://insticc.org/node/TechnicalProgram/ijcci/presentationDetails/80657]en_US
dc.identifier.urihttp://hdl.handle.net/10394/33655
dc.identifier.urihttp://insticc.org/node/TechnicalProgram/ijcci/presentationDetails/80657
dc.description.abstractThe transport industry is a primary contributor towards emissions that impact climate change. Fuel economy is also of critical importance to the profitability of road freight transport operators. Empirical evidence identified a variety of factors impacting fuel consumption, including route inclination, payload and truck driver behaviour. This creates the need for accurate fuel usage models and objective methods to distinguish the impact of drivers from other factors, in order to enable reliable driver performance assessment. We compiled a data set for 331 drivers completing 7332 trips over 21 routes to obtain evidence of the impact of route, payload and driver behaviour on fuel economy. We then extracted various regression and neural models for fuel economy and used these models to remove the impact of route inclination and payload, allowing the impact of driver performance to be measured more accurately. All models demonstrated significant out-of-sample predictive ability. Neural models in general outperformed regression models, while amongst neural models radial basis models slightly outperformed multi-layer perceptron models. The significance of compensating for factors not controlled by the driver was verified by demonstrating large differences in driver performance ranking before and after compensating for route inclination and payloaden_US
dc.language.isoenen_US
dc.publisherINSTICCen_US
dc.subjectFuel economyen_US
dc.subjectTruck driveren_US
dc.subjectPerformance benchmarkingen_US
dc.subjectGeneralized regression neural networken_US
dc.subjectMultilayer perceptronen_US
dc.titleNeural models for benchmarking of truck driver fuel economy performanceen_US
dc.typePresentationen_US
dc.contributor.researchID10196978 - Hoffman, Alwyn Jakobus


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