# A Predictive Vehicle Ride Sharing Recommendation System for Smart Cities Commuting
## 1 Introduction
In [23], the authors study the effects of privacy regulations on dynamic car riding sharing systems, where they experiment with spatiotemporal data optimization to provide an optimal privacy-based approach for commute in sustainable cities. Pricing options are studied in [24], where the authors present a joint pricing and matching architecture to enable cost efficient ride sharing systems in green ecosystems. The Uber marketplace is presented in [25], where the authors propose a matching policy for riders and drivers as well as batch matching policy to serve increased ride sharing demand in the SC. Both policies are optimized based on historical data to reduce riders waiting time. Uber's shared mobility system is also examined in [26], where the authors focus on ride sharing services available for AV technology. Ride hailing is also considered as a commute option, which focuses mainly on vehicle distance traveled in rural and urban areas. Uber-Pool is considered in [27], where the research exploits the data analyses of car ride sharing systems with regards to obtained service time and detour guarantees provided by taxi policies. Operational policies in dynamic environments are studied in [28], where the authors focus on analyzing Uber's mobility services. The research focuses mainly on trip pattern matching and searching efficiency to provide detailed knowledge on emerged Uber's operational policies. Dynamic routing strategies are researched in [29], where the focus is on designing a scalable matching system for exploiting online routing to face car ride sharing demands. Such a system is evaluated by assessing the prediction accuracy of the proposed routes, which are available to riders. An electric vehicles (EV) car ride sharing system is evaluated in [30], where the authors focus on providing efficient data driven on demand prediction accuracy. A car ride sharing system for commuting in SCs is proposed, in [31], where human behavior characteristics are evaluated to propose a recommender system. Such a system also exploits the prediction accuracy of routing trips selected by riders. A recommender system is examined in [32], which uses an expansive search-based model to provide advanced ride sharing services in SCs' daily commute. A multipath planning system for car ride sharing services is proposed in [33], where the authors provide a recommendation system, which is evaluated with dynamic optimization processes. Shortest path clustering is used in [34], to enhance the spatial data mining of a high-capacity car ride sharing system, which incorporates a recommender system for efficient commute in sustainable cities. Current research on car ride sharing systems is widely expanding in a range of contemporary technologies. These approaches focus mainly on certain research areas of SC commuting rather than covering a multidisciplinary approach. In this paper, the focus is on performing a multidisciplinary research on car riding systems taking into consideration personalized user mobility behavior, which is further optimized to serve a population of riders. Location prediction is also part of the research effort, which is evaluated with prediction accuracy on selected destinations in SCs. The efficiency of the provided commute choices is also evaluated by the incorporation of a recommender system based on personalized riders' information. Specifically, this paper presents research on smart vehicle commuting systems and proposes a predictive vehicle ride sharing system, which has a positive impact on SCs' green and sustainable environment. The adopted system also provides a recommendation capability to citizens to select the persons they would like to commute with. An Artificial Intelligence (AI)-enabled weighted pattern matching model is used to assess user movement behavior in SC and provide the best predicted recommendation list of commuting users. Citizens are then able to engage a current trip to next destination with the more suitable user provided by the list.