Cell-based positioning for improving LBS

Markus Ray

Figure 3: Example for visualizing prominent places in GoogleEarth. Yellow rectangles indicate the composition of cell-based positions for prominent places.

Step-by-step, for each hour of day, the visiting frequencies of the prominent places are compared and the prominent place with the highest representation is selected for the sequence. In order to avoid toggling in the sequence at transition times of one prominent place to another, smoothed distributions are used for prominent place selections.
A typical daily routine of a work day of an Austrian employee (All volunteers are Austrian) is manually defined as

i) being at home (at night/early morning), being at work (in the morning)

ii) being somewhere else (in the afternoon/evening) and

iii) being at home (in the evening/night)

From this daily routine three classes of prominent places ‘home’, ‘work’ and ‘spare time’ are derived. Hence, the sequence which is to be compared to the computed sequence is {‘home’, ‘work’, ‘spare time’, ‘home’}.
For classification, the first element of the computed sequence is taken and labeled with the first element of the manually defined sequence. By assuming the computed sequence is {‘unknown 3’, ‘unknown 2’, ‘unknown 1’, ‘unknown 3’} – ‘unknown3’ is labeled to ‘home’. The same is done with the next element (here ‘unknown2’ is labeled to ‘work’). Finally all other elements of the computed sequence are classified as ‘spare time’ untilthe end is reached (Here ‘unknown 1’ is labeled as ‘spare time’). Once-classified prominent places are not re-classified.

Experimental results

validated by a 250 000 cell-based positioning data set obtained during a half year of permanent observation. Eleven of twelve home locations (92%) and nine of ten work locations (90%) have been found and correctly classified (Two volunteers moved their home during observation phase). Each volunteer has validated the result based on her provided cell-based positioning data with respect to the correctness of found and classified prominent places. All found prominent places are close to the real location. Geographical accuracy of the found places mainly depends on the cell-network distribution in the surrounding area and cannot be influenced by the used method. Hence, no quantitative validation about the localization quality was performed.
In Figure 3 is an example for visualizing the results in Google Earth. This visualization was used to validate the results together with the volunteers.
The demonstrated grouping and classification results are promising and can be used as basis for improved LBS.

Acknowledgements
The author gratefully acknowledges the support of the Austrian Mobile Phone Provider “mobilkom austria” by providing a web interface for obtaining cell-based positioning data for free. The author also thanks the volunteers for providing their position information over this long time period.

References

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