## 4 Origin-Destination Matrices Estimation
### Comparison to the 2016 Mobility Survey  
According to the best practices reviewed in the related literature, results of the OD matrix estimation must be compared with other sources of information. To this end, the results from the household urban mobility survey carried out in 2016 ([PERSON] et al., 2016) were used. The comparison is done at the municipality level, since the survey data does not allow comparing at a finer-grain. The unavailability of more disaggregated sources of OD data somewhat limits the comparison with our proposed OD matrix. The Spearman correlation coefficient was applied to quantify the similarities between the OD matrix estimated from ITS data and the OD matrix from the mobility survey. To compute this coefficient, matrices were vectorized in row-major order, without losing information since proximity in the matrix does not imply geographical proximity between municipalities. Results show that the estimated OD matrix and the mobility survey OD matrix have a Spearman correlation coefficient of 0.895 (\(p\)-value \(2.026\times 10^{-23}\)), indicating a strong correlation between them, thus validating the proposed approach for OD matrix estimation based on ITS data. Figure 14 presents a visual comparison between the OD matrices derived from ITS data and from the mobility survey. Each OD matrix is represented as a two-dimensional grid with colors mapped according to the number of transactions occurring in each OD pair. The visual representation of OD matrices as heatmaps on two-dimensional grids in Figure 14 allows identifying similarities between the results computed with ITS data and those from the mobility survey. Trips within municipalities A and B are the most dominant according to both estimations, followed by trips within municipality G. Both figures show that trips from B to CH and vice versa are also highly dominant. The diagonal of the grid is mapped to more intense colors in Figure 14a than in Figure 14b. This might be a consequence of the larger number of trips considered in the OD matrix generated from ITS data. Despite this observation, an outstanding number of similar patterns are found when comparing the grids both row-wise and column-wise. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & & & & & & & & & & & \\ \cline{3-10}  & & **A** & **B** & **C** & **CH** & **D** & **E** & **F** & **G** & **total** \\ \hline \multirow{10}{*}{**C**} & A & 626,388 & 199,196 & 184,905 & 98,087 & 30,108 & 40,370 & 21,875 & 73,390 & 1,274,319 \\  & B & 154,358 & 662,993 & 224,578 & 366,865 & 108,640 & 173,898 & 119,306 & 108,469 & 1,919,107 \\  & C & 174,040 & 260,526 & 320,368 & 111,113 & 102,244 & 64,691 & 62,188 & 101,337 & 1,196,507 \\  & CH & 100,348 & 334,040 & 131,089 & 362,377 & 101,433 & 156,  
Results show that OD matrices generated from ITS data are a valid alternative to understand mobility in a city. The proposed approach for building OD matrices has several advantages: (i) due to the large volume of data generated by ITS compared to the number of individuals that participate in a survey, a finer-grain OD matrix is obtained using data analysis (e.g., bus stop and census tract levels), whereas the mobility survey results only apply to municipalities; and (ii) data analysis allows computing different OD matrices applying different criteria (e.g., days of the week, hours of the day, etc) and the mobility survey refers to working days only. Thus, in order to gain insight on the mobility of citizens under different conditions (e.g., during weekends) a new survey ought to be carried out, with the associated costs and delays. Regarding costs, the proposed approach for OD matrix estimation provides an attractive alternative for public administrations to characterize mobility in a city. This alternative takes advantage of valuable data that arise from the infrastructure deployed in modern ITS. This is the case of Montevideo, where the ITS infrastructure has been deployed in the last decade.