## 6 Conclusions and Further Research  
Studying the mobility patterns in smart cities may provide useful insights to support decision-making related to urban mobility. Optimizing processes such as the placement of electric vehicle charging stations or designing efficient routes may minimize economic costs while contributing to reducing the environmental impacts and increasing social welfare. In this context, this paper explored mobility patterns in the city of Barcelona (Spain), relying on the open data service of the Ajuntament de Barcelona. An exploratory analysis, visualization techniques, and predictive models have been presented. In addition, a discussion regarding the potential of these tools for policymakers has been provided.  
The primary limitation of this research lies in its reliance on data spanning only one month. While the selection of this period facilitated building models and yielded intriguing insights, examining a more extended period (spanning years) would enable exploration of trends, seasonal fluctuations, variability, and the influence of COVID-19 and lockdown policies on traffic patterns, among other factors. This work opens up several lines of future research. First, the use of more variables could lead to more robust, powerful, and interpretable models. Interesting variables to consider are related to weather (rainwater level, temperature, etc.), traffic accidents, and events that attract a lot of people such as football games and international conferences. In addition, comparing open data portals of smart cities across the world (regarding datasets related to urban mobility and characteristics such as update frequency, granularity, and documentation) and studying the use that different agents have for them is another promising research line. Further lines in this topic could involve comparing the results of XGBoost to those of other machine learning algorithms and examining ways to improve its interpretability. Research on the potential benefits of combining XGBoost with the use of more advanced spatio-temporal models, in order to capture the spatio-temporal dependence, is another promising option for implementing better predictive models.  
Conceptualization, L.C., P.C., C.S. and P.M.; methodology, E.G., L.C., P.C., C.S. and P.M.; software, E.G. and M.P.; validation, E.G. and M.P.; investigation, L.C., P.C., C.S. and P.M.; data curation, E.G. and M.P.; writing--original draft preparation, E.G., L.C., P.C. and C.S.; writing--review and editing, L.C. and P.C. All authors have read and agreed to the published version of the manuscript.  
This work has been partially funded by the Spanish Ministry of Science (PID2019-111100 RB-C21-C22/AEI/10.13039/501100011033), as well as by the Barcelona City Council and Fundacio \"la Caixa\" under the framework of the Barcelona Science Plan 2020-2023 (grant 21S09355-001). The authors appreciate the support received from the research group GRBIO under the grant 2021 SGR 01421 from the Departament de Recerca i Universitats de la Generalitat de Catalunya (Spain).  
Not applicable.  
The original data presented in the study are openly available in Open Data BCN service at opendata-ajuntament.barcelona.cat/en (accessed on 2 April 2024).  
The authors declare no conflicts of interest.