SwePub - search: L773:9781601323132

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1.	Carpatorea, Iulian, 1982-, et al. (author) APPES Maps as Tools for Quantifying Performance of Truck Drivers 2014 In: Proceedings of the 2014 International Conference on Data Mining, DMIN'14. - USA : CSREA Press. - 9781601323132 ; , s. 10-16 Conference paper (peer-reviewed)abstract Understanding and quantifying drivers’ influence on fuel consumption is an important and challenging problem. A number of commonly used approaches are based on collection of Accelerator Pedal Position - Engine Speed (APPES) maps. Up until now, however, most publicly available results are based on limited amounts of data collected in experiments performed under well-controlled conditions. Before APPES maps can be considered a reliable solution, there is a need to evaluate the usefulness of those models on a larger and more representative data.In this paper we present analysis of APPES maps that were collected, under actual operating conditions, on more than 1200 trips performed by a fleet of 5 Volvo trucks owned by a commercial transporter in Europe. We use Gaussian Mixture Models to identify areas of those maps that correspond to different types of driver behaviour, and investigate how the parameters of those models relate to variables of interest such as vehicle weight or fuel consumption.

Carpatorea, Iulian, 1982-, et al. (author)
APPES Maps as Tools for Quantifying Performance of Truck Drivers
2014
In: Proceedings of the 2014 International Conference on Data Mining, DMIN'14. - USA : CSREA Press. - 9781601323132 ; , s. 10-16
Conference paper (peer-reviewed)abstract
- Understanding and quantifying drivers’ influence on fuel consumption is an important and challenging problem. A number of commonly used approaches are based on collection of Accelerator Pedal Position - Engine Speed (APPES) maps. Up until now, however, most publicly available results are based on limited amounts of data collected in experiments performed under well-controlled conditions. Before APPES maps can be considered a reliable solution, there is a need to evaluate the usefulness of those models on a larger and more representative data.In this paper we present analysis of APPES maps that were collected, under actual operating conditions, on more than 1200 trips performed by a fleet of 5 Volvo trucks owned by a commercial transporter in Europe. We use Gaussian Mixture Models to identify areas of those maps that correspond to different types of driver behaviour, and investigate how the parameters of those models relate to variables of interest such as vehicle weight or fuel consumption.

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