# Exploring Hybrid-Multimodal Routing to Improve User Experience in Urban Trips
## 6 Final Remarks  
In this work, we presented a novel flow clustering technique for geo-located and anonymized data and a user experience model for urban trips. These are the main contributions of this paper. We detailed the model and the experiments, so it is possible to replicate this work with other datasets (using the code on Github). Overall, this study showed that hybrid routing is a valuable option for traditional urban routing systems.  
The proposed model has some parameters that might be, at times, difficult to calibrate. Some of them can be obtained from datasets that one might have access to, such as the average income of users of public transportation and the bikeability and walkability functions; on the other hand, others cannot be easily obtained, such as the weight for the profile operator. A possible solution, as well as a possible future direction of this study, is to use learning techniques to adapt the weights in the profile operator [5]. Some of the issues were identified when running the experiments, such as the anonymized datasets favored the formation of small-length trips. Thus, some modes have more advantages than others in the analysis. However, we presented an approach to cluster data without anonymization. Besides, the Hybrid option showed to be a viable choice for short trips, and previous results [7] showed that it presents good efficiency in more extended trips. Our solution does not explore the full road network when evaluating routes, but a reduced network suggested by services, such as TomTom Routing and Google Directions. We need to check whether the usage of the full graph (or semi-full depending on the heuristic) would bring gains for our work. This could be achieved by changing the MLGLS layer to another, which implements algorithms, such as the shortest path.  
One interesting possible continuation to the present study would be to combine the route recommendation with a central ITS of a smart city. For instance, the mode transition points in our proposal depend on the location of the bus terminals and bike dockers. Once integrated with the central ITS, our approach could be used as a evaluation function to optimize the placement of these stations. To put these ideas in practice, our study would have to be integrated with optimization algorithms with the same purpose [46]. Furthermore, in a integrated ITS scenario, our approach could make use of different strategies to not only keep away from traffic jams, but also avoid their creation. For this, different traffic control mechanisms should be integrated with our method, such as traffic lights and street crossings orchestration control [47].  
Conceptualization, D.O.R., G.M., T.B., A.A.F.L., M.L.M.P., and L.A.V.; methodology, D.O.R., G.M., T.B., A.A.F.L., M.L.M.P., and L.A.V.; software, D.O.R.; validation, D.O.R., G.M., and L.A.V.; formal analysis, D.O.R.; data curation, D.O.R.; writing--original draft preparation, D.O.R., G.M., T.B., A.A.F.L., M.L.M.P., and L.A.V.; writing--review and editing, D.O.R., G.M., T.B., A.A.F.L., M.L.M.P., and L.A.V.; visualization, D.O.R.; supervision, G.M., T.B., A.A.F.L., and L.A.V.; funding acquisition, D.O.R., T.B., L.A.V. All authors have read and agreed to the published version of the manuscript.  
The authors would like to thank the Sao Paulo Research Foundation (FAPESP), grants #2020/11259-9, #2018/12447-3, #2018/23064-8, #2018/19639-5, #2018/23126-3, and #2015/24494-8.