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Clustering driver’s destinations - using internal evaluation to adaptively set parameters

Levin, Carl and Håkansson, Christopher (2015)
Department of Automatic Control
Abstract
With advanced navigation systems becoming ubiquitous in modern cars, the availability of detailed GPS data opens up new research areas in the fields of pattern analysis and data mining. By capturing the end-of-trip GPS points of each trip made by a driver, that driver’s meaningful destinations could be identified. The knowledge of these destinations can be used for route prediction, which in turn can be used for optimizing the motor control to decrease emissions. It can also be used for developing functions for autonomous vehicles. In this thesis a way of extracting these meaningful destinations from GPS data using clustering algorithms has been developed and evaluated. The result is a clustering procedure consisting of 2 steps of... (More)
With advanced navigation systems becoming ubiquitous in modern cars, the availability of detailed GPS data opens up new research areas in the fields of pattern analysis and data mining. By capturing the end-of-trip GPS points of each trip made by a driver, that driver’s meaningful destinations could be identified. The knowledge of these destinations can be used for route prediction, which in turn can be used for optimizing the motor control to decrease emissions. It can also be used for developing functions for autonomous vehicles. In this thesis a way of extracting these meaningful destinations from GPS data using clustering algorithms has been developed and evaluated. The result is a clustering procedure consisting of 2 steps of clustering. First a pre-clustering to divide the data into subsets corresponding to smaller spatial areas. Then, a refining clustering step for which the parameter of the algorithm is adapted to each subset. Adaptively setting the parameter for each subset is done by testing a set of parameters and evaluating the results internally, with the Silhouette coefficient, and choosing the parameter giving the best evaluation score. The best performing configuration of our procedure, according to our external evaluation method, is in par with the performance of DBSCAN with a supervised choice of parameter setting. Further evaluation of data sets from different areas of the world are needed to draw strong conclusions of the developed procedures performance. (Less)
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author
Levin, Carl and Håkansson, Christopher
supervisor
organization
year
type
H3 - Professional qualifications (4 Years - )
subject
report number
TFRT-5995
ISSN
0280-5316
language
English
id
8567450
date added to LUP
2016-01-27 09:14:53
date last changed
2016-01-27 09:14:53
@misc{8567450,
  abstract     = {With advanced navigation systems becoming ubiquitous in modern cars, the availability of detailed GPS data opens up new research areas in the fields of pattern analysis and data mining. By capturing the end-of-trip GPS points of each trip made by a driver, that driver’s meaningful destinations could be identified. The knowledge of these destinations can be used for route prediction, which in turn can be used for optimizing the motor control to decrease emissions. It can also be used for developing functions for autonomous vehicles. In this thesis a way of extracting these meaningful destinations from GPS data using clustering algorithms has been developed and evaluated. The result is a clustering procedure consisting of 2 steps of clustering. First a pre-clustering to divide the data into subsets corresponding to smaller spatial areas. Then, a refining clustering step for which the parameter of the algorithm is adapted to each subset. Adaptively setting the parameter for each subset is done by testing a set of parameters and evaluating the results internally, with the Silhouette coefficient, and choosing the parameter giving the best evaluation score. The best performing configuration of our procedure, according to our external evaluation method, is in par with the performance of DBSCAN with a supervised choice of parameter setting. Further evaluation of data sets from different areas of the world are needed to draw strong conclusions of the developed procedures performance.},
  author       = {Levin, Carl and Håkansson, Christopher},
  issn         = {0280-5316},
  language     = {eng},
  note         = {Student Paper},
  title        = {Clustering driver’s destinations - using internal evaluation to adaptively set parameters},
  year         = {2015},
}