  Despite the importance of fundamental parameters (traffic flow, density and
speed) to describe the traffic behavior, there still are some difficulties in
order to obtain and store this information. Furthermore, given the type of
study or the project the resolution analysis interval can vary from less than
one hour to annual. To create alternatives in database structures,this article
aims to present a method to reconstruct disaggregated historical data from
aggregated data using Wavelet Transform. From the proposed method, it is
possible to reconstruct data in short intervals from data with longer
intervals,since they have the same behavior, for example, data from the same or
similar highway. For such, a Detail coefficient is generated through the
Discrete Wavelet Transform (DWT) with the disaggregated data. The aggregated
data was reconstructed through an Approximation coefficient. After establishing
these coefficients, the Inverse Wavelet Transform (IWT) is applied. The results
indicated an average correlation between the reconstructed the original data of
0.960; 0.974; 0.968 and 0.960 for data initially aggregated at 10, 20, 40 and
80 minutes interval, respectively. The results also indicated an absolute mean
error of 7.13%; 8.74%; 9.83% and 11.23% for data initially aggregated in 10,
20, 40 and 80 minutes, respectively. In other words, results suggest that the
reconstituted data have a high correlation and a low percentage of mean
absolute error with the original signal. In conclusion, the reconstruction of
data from aggregated data and Wavelet Transform presents a good correlation and
low average absolute error rate, comparable to traffic estimation studies
(CASTRO-NETO et al., 2009; CORIC et al. 2012, LAM et al., 2006; LIM, 2001).
