# A Predictive Vehicle Ride Sharing Recommendation System for Smart Cities Commuting
## 4 Discussion
### Comparison with Other Research Efforts  
The proposed system focuses on multidisciplinary research in the field of car ride-sharing systems. The next destination prediction is evaluated through prediction accuracy, \(a\), of the adopted classification model as well as, \(map@N\), recommendation evaluation metric. System is based on optimal personalized information of users commute in SCs. Comparing observed results with other research efforts in next destination prediction, such as [29, 30, 31], the adopted research effort reaches better values for, \(a\), prediction accuracy evaluation metric. Specifically, the proposed system achieves \(a=0.9168\), while effort in [29] achieves \(a=0.9028\). In addition, the system in [30] achieves \(a=0.9097\), while effort in [31] achieves \(a=0.9125\). Concretely, comparing results with research in other recommender systems, such as [32, 33, 34], the proposed system reaches optimal values for, \(map@N\), recommendation evaluation metric. Subsequently, the adopted system achieves\(map@N=0.8234\), while effort in [32] achieves \(map@N=0.8113\). In addition, system in [33] achieves \(map@N=0.8204\), while effort in [34] achieves \(map@N=0.8079\).  
Strengths of the proposed approach are the analyses of user mobility behavior towards a sustainable way of transport in rural and urban environments. Such personalized user behavior enables a context-aware car riding system, which faces commute in contemporary green ecosystems. The exact timing for online riders picks up and drop off in areas of interest in the SC is achieved by dynamic scheduling of the proposed system. Concretely its ability to provide online dynamic routing destinations towards next location ride sharing prediction is an advantage of the adopted system. The proposed approach provides its services with free of charge policy, which means that riders are not required to pay for the commute.  
The weaknesses of the proposed system are that the adopted approach does not studies multimodal means of transport like other research approaches. Since during the commuting process there is sensitive information that is shared between drivers and riders, compliance with privacy regulations is crucial. However, the proposed system does not cover such a utility in the current stage of research. In addition, current research effort is not tuned to support its efficiency in cases of EV as well as AV and CAV commuting environments. Operational policies are not taken into consideration during this research since it is a separate research area which is not covered by the data provided in this effort.  
Compared with Uber-Pool, in [25, 26, 27, 28], proposed research effort does not serve multiuser demands. Specifically, Uber batch matching is not provided by the adopted system. However, it supports single users commute services like Uber while it exploits personalized riders' information. The next step of the proposed effort is to incorporate social context as well as social mobility context to provide a more social car ride sharing environment in SCs commute. In addition, the aim is to provide a mobile application where riders can collect desired commutes with drivers that they have common interests with. Such a mobile application will not require pricing options, like Uber does, but it will be a free of charge service for every citizen who wishes to commute in SCs environments.