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APPES Maps as Tools for Quantifying Performance of Truck Drivers
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.ORCID iD: 0000-0002-8797-5112
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.ORCID iD: 0000-0002-7796-5201
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.ORCID iD: 0000-0001-5163-2997
Volvo Group Trucks Technology, Advanced Technology & Research, Göteborg, Sweden.
2014 (English)In: Proceedings of the 2014 International Conference on Data Mining, DMIN'14 / [ed] Robert Stahlbock & Gary M. Weiss, USA: CSREA Press, 2014, 10-16 p.Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
USA: CSREA Press, 2014. 10-16 p.
Keyword [en]
data mining, truck drivers, fuel, fuel consumptions, histograms
National Category
Information Systems
Identifiers
URN: urn:nbn:se:hh:diva-27411ISBN: 9781601323132 (print)OAI: oai:DiVA.org:hh-27411DiVA: diva2:776024
Conference
The 10th International Conference on Data Mining, DMIN´14, July 21-24, Las Vegas, Nevada, USA
Projects
Learning Fleet
Available from: 2015-01-06 Created: 2015-01-06 Last updated: 2017-02-08Bibliographically approved
In thesis
1. Methods to quantify and qualify truck driver performance
Open this publication in new window or tab >>Methods to quantify and qualify truck driver performance
2017 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Fuel consumption is a major economical component of vehicles, particularly for heavy-duty vehicles. It is dependent on many factors, such as driver and environment, and control over some factors is present, e.g. route, and we can try to optimize others, e.g. driver. The driver is responsible for around 30% of the operational cost for the fleet operator and is therefore important to have efficient drivers as they also inuence fuel consumption which is another major cost, amounting to around 40% of vehicle operation. The difference between good and bad drivers can be substantial, depending on the environment, experience and other factors.

In this thesis, two methods are proposed that aim at quantifying and qualifying driver performance of heavy duty vehicles with respect to fuel consumption. The first method, Fuel under Predefined Conditions (FPC), makes use of domain knowledge in order to incorporate effect of factors which are not measured. Due to the complexity of the vehicles, many factors cannot be quantified precisely or even measured, e.g. wind speed and direction, tire pressure. For FPC to be feasible, several assumptions need to be made regarding unmeasured variables. The effect of said unmeasured variables has to be quantified, which is done by defining specific conditions that enable their estimation. Having calculated the effect of unmeasured variables, the contribution of measured variables can be estimated. All the steps are required to be able to calculate the influence of the driver. The second method, Accelerator Pedal Position - Engine Speed (APPES) seeks to qualify driver performance irrespective of the external factors by analyzing driver intention. APPES is a 2D histogram build from the two mentioned signals. Driver performance is expressed, in this case, using features calculated from APPES.

The focus of first method is to quantify fuel consumption, giving us the possibility to estimate driver performance. The second method is more skewed towards qualitative analysis allowing a better understanding of driver decisions and how they affect fuel consumption. Both methods have the ability to give transferable knowledge that can be used to improve driver's performance or automatic driving systems.

Throughout the thesis and attached articles we show that both methods are able to operate within the specified conditions and achieve the set goal.

Place, publisher, year, edition, pages
Halmstad: Halmstad University Press, 2017. 23 p.
Series
Halmstad University Dissertations, 28
Keyword
Driver performance, heavy-duty vehicle, fuel economy, fuel consumption, fuel prediction, truck driver
National Category
Computer and Information Science
Identifiers
urn:nbn:se:hh:diva-33229 (URN)978-91-87045-59-2 (ISBN)978-91-87045-58-5 (ISBN)
Presentation
2017-02-10, Wigforssalen, Visionen, Kristian IV:s väg 3, Halmstad, 13:40 (English)
Opponent
Supervisors
Available from: 2017-02-08 Created: 2017-02-07 Last updated: 2017-02-08Bibliographically approved

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