## 5 Conclusions and Future Works  
This work proposed a multi agent system for bike sharing systems. As part of this work, a case study was conducted to test the feasibility of the system, using the data of a BSS located in Salamanca, a middle-sized city. The inclusion of agents in charge of collecting data from heterogeneous sources as well as its cleaning and fusion has eased these tasks. This agent architecture will also make it easier to obtain data from different bike sharing systems.  
Moreover, the information collected by the other system agents was employed by the agents that perform demand forecasting. The use of these agents has allowed us to offer services to third party applications.  
Different regression algorithms have also been employed in the process of bike demand prediction. Additionally, a statistical analysis has been performed in order to show the differences in their performance and to determine the relevance of results. Random Forest Regressor is the regressor algorithm that outperformed the rest of the algorithms.  
With regards to the visualization of the data collected by the agents, a web application has been developed where it is possible to analyse and filter the previously processed data. Thanks to filters in this application the BSS operator can view the desired information thanks and have an insight on the behaviour of the users in the system.  
This web application includes a prediction module where the BSS operator can request predictions for future days and make decisions in matters of bike reallocation strategies.  
In future works we will employ this multi agent system to collect, process and analyse the data of BSSs in bigger cities such as Madrid.  
We will also evaluate the different prediction models with that data in order to compare the performance of the system in middle-sized and big cities. Furthermore, in order to employ the output demand prediction of this system, future work will concern reallocation bike strategies in BSS and the obtaining of an optimal route.  
In future works we will analyse how to include the new dock-less bike sharing systems into the developed multi agent system. We will analyse the new additional data that these kinds of systems provide and how to include them into our system. In dock-less systems, the deployed fleet of bicycles could be grouped, creating clusters which act as virtual stations in the city. This data could be used to analyse the bicycle demand and flow of dock-less bike sharing systems.  
[PERSON] is supported by the pre-doctoral fellowship from the University of Salamanca and Banco Santander. This work was supported by the Spanish Ministry, Ministerio de Economia y Competitividad and FEDER funds. Project. SURF: Intelligent System for integrated and sustainable management of urban fleets TIN2015-65515-C4-3-R. We would also like to show our gratitude to the SalenBici bike share system for sharing with us the data for the current work.  
[PERSON] and [PERSON] conceived and designed the multi agent system while [PERSON] and [PERSON] contributed to the development and implementation of the system; [PERSON] carried out the experimentation, the model selection and implementation and wrote the paper. [PERSON] reviewed and supervised the whole work and provided his great expertise in the field.  
The authors declare no conflict of interest.