Name
Abstract | ||
|---|---|---|
The aim of this paper is to apply predictive data mining (DM) techniques in order to predict the average fuel consumption for trucks and drivers resp., to identify the key factors that affect fuel consumption of vehicles and also to identify best practices and driving styles of drivers. For this purpose different models have been proposed to provide an overview of the key factors affecting fuel consumption for individual vehicles and their drivers. Predictive models enabled us to identify main influencing factors and provide recommendations for a logistics company to reduce the fuel consumption. Data were collected from Dynafleet information system of a small transport company. The company is dealing with freight traffic, particularly trucks. We first describe selected projects dealing with similar tasks in this area. Next, we explore and analyze data using CRISP-DM methodology by appropriate methods designed for data mining and then evaluate the results of the experiments. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-67220-5_15 | INFORMATION SYSTEMS ARCHITECTURE AND TECHNOLOGY, PT I |
Keywords | Field | DocType |
Data mining,CRISP-DM,Predictive data mining,Fuel consumption,Dynafleet,Naive bayes,Neural network | Information system,Truck,Data mining,Best practice,Naive Bayes classifier,Computer science,Fuel efficiency,Artificial neural network,Marketing | Conference |
Volume | ISSN | Citations |
655 | 2194-5357 | 0 |
PageRank | References | Authors |
0.34 | 4 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Miroslava Muchová | 1 | 0 | 0.34 |
| Jan Paralic | 2 | 56 | 13.96 |
| Michael Nemcík | 3 | 0 | 0.34 |