## 5 Conclusions and Future Work  
This article presented an urban data analysis approach to study mobility using ITS data. As a case study, the ITS in Montevideo, Uruguay was analyzed by studying a dataset of GPS bus location and smart card ticket sales. Several insights were obtained through data analysis, including: number of passengers traveling with the same smart card, frequency of use of the smart cards, and number of bus transfers. A temporal analysis of ticket sales was performed, identifying three peak hours during working days. Then, a spatiotemporal analysis revealed that citizens from areas farther away from the coastline start trips earlier than those near the coast. Additionally, two practical use cases were presented: event detection in the city and reckless driving behavior identification.  
Besides a purely descriptive utilization of ITS data to characterize a public transportation system, a methodology for building OD matrices using trip chaining was applied to estimate destinations. The implemented algorithm was able to estimate the destination for 81.62% of trips in the studied dataset, a highly competitive result when compared to the ones reported in the related literature.  
The OD matrix computed for Montevideo was compared against the one from the 2016 urban mobility survey. Results showed a Spearman correlation coefficient of 0.895, suggesting that the proposed approach is a valid alternative to understand mobility in the city. The proposed approach allows studying mobility at a finer grain, obtaining OD matrices between pairs of bus stops and census tracts, whereas the OD matrix from the mobility survey only applies to municipalities. The implemented solution is inexpensive if the ITS infrastructure is already deployed and it allows computing OD matrices considering different criteria and providing up-to-date mobility information.  
An interactive web application was developed to visually display the computed OD matrices. The visualization tool allows selecting a geographical area and displays a heatmap indicating the number of passengers traveling from the selected area to all other in the city. The application supports working at different aggregation levels for OD matrices and offers several tools to filter data.  
Figure 15: User interface of the OD matrix visualization tool.  
The main lines for future work include studying other interesting aspects of mobility in the city, e.g., the quality of service offered by the transportation system in terms of punctuality, frequency of lines, and load of passengers. Regarding OD matrices estimation, the proposed approach can be extended to tickets sold without smart cards, to account for all passengers of the transportation system. The destination estimation algorithm can be further refined by using historical passenger data and machine learning techniques to infer frequent destinations when trip chaining fails. Furthermore, activity detection could be used to discriminate between short individual trips using the same ticket from multi-leg trips involving bus transfers [24]. Parameters tuning of the destination estimation algorithm can be studied in case that fine-grain mobility data sources are available. Finally, the results of the analysis can be applied to solve optimization problems, e.g., synchronization of bus schedules [25], demand-based fleet size optimization, bus stops location, and bus line network redesign.  
Conceptualization, R.M. and S.N.; writing, review, and editing, R.M. and S.N. Data analysis, R.M. Both authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The work of [PERSON] has been partly supported by ANII and PEDECIBA, Uruguay. [PERSON] would like to thank Fundacion Carolina, Spain.  
The authors declare no conflict of interest.