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to locate the collected/classified data. Each one of this
information is a different continuous time series. A sampling rate
of 100 Hz was used for each time series. It is important to note
that the Android system does not guarantee a perfect precision on
the interval between readings. This means that when we choose a
sampling rate of 100 Hz, the device should output between 95 and
105 observations per second.
For this data, the (X,Y,Z) acceleration time series are treated as
separate dimensions. There is a univariate version in the archive
also.
In order to obtain labeled data, the Asfault application allows the
expert to inform the pavement condition before collecting the data.
To guarantee the integrity of data, Asfault also records videos of
the road over the data collection using the built-in camera. Thus,
it is possible to perform the analysis of these videos to confirm
the class labels assigned by the expert.
The datasets were collected in the Brazilian cities of Sao Carlos,
Ribeirao Preto, Araraquara, and Maringa using a medium sized
hatchback car (Hyundai i30) and two different devices (Samsung
Galaxy A5 and Samsung S7).
The problem AsphaltRegularity has two classes based on the comfort
felt by the driver according to the condition of the pavement was
considered. Regular: when the pavement is regular and the driver
comfort is very little changed over time;
Deteriorated: when is observed some irregularities and roughness in
a deteriorated pavement that are responsible for transferring
vibrations to the cabin of the vehicle, reducing the comfort of the
driver.
There are 1502 cases: regular (762) and Deteriorated (740 cases).
Data is variable length, minimum 66 observations, maximum 4201. The
data is split randomly into 50/50 default train series. The data
can be resampled without bias.
The best reported accuracy using combining rules with the XYZ data
was achieved by the distance measure LCSS combined with the
Complexity Invariant Distance (CID-LCSS) which achieved 98.48\%
accuracy (measured with a 5x2 CV). See Table 5 in [1]
The best reported accuracy with this univariate data was achieved
by the distance measure LCSS combined with the Complexity Invariant
Distance (CID-LCSS) which achieved 96.48\% accuracy (measured with
a 5x2 CV, see Table 5 of [1]).
[1] Souza V.M.A. Asphalt pavement classification using smartphone
accelerometer and Complexity Invariant Distance. Engineering
Applications of Artificial Intelligence Volume 74, pp. 198-211.
https://www.sciencedirect.com/science/article/pii/S0952197618301349
[2] Souza V.M.A., Cherman E.A., Rossi R.G., Souza R.A. Towards
automatic evaluation of asphalt irregularity using smartphones
sensors International Symposium on Intelligent Data Analysis
(2017), pp. 322-333