## 5 Current Trends
### Overall Observations  
Under the problem variants, there are two types of classification proposed by [5; 15]. The authors of [5] proposed a problem classification based on the number of drivers and riders while [15] proposed another type of problem classification based on number of origins and destinations. The classification from [15] does not receive much attention as they only considered the origin and destination in classifying the variants. On the other hand, the classification in [5] is much more welcoming as it can consider whether the riders can be transferred to another driver in between the trips (such as M-1 and M-M). In term of the simplicity of the problem, it can be ranked from low complexity to high complexity which are 1-1, M-1, 1-M, and M-M, respectively. Among all problems, 1-1 is the simplest because it involves a single-vehicle transport with a single passenger (or multiple passengers who share the same destination locations). However, it does not fit well in a real life situation where vehicles can be fully utilized. The 1-M problem has the \begin{table}
\begin{tabular}{l c c c c c c c c c c c c} \hline \hline \multicolumn{11}{c}{**Solution Approaches**} & \multicolumn{11}{c}{**Year**} \\ \cline{2-11}  & **2010** & **2011** & **2012** & **2013** & **2014** & **2015** & **2016** & **2017** & **2018** & **2019** & **2020** & **2021** & **Total** \\ \hline Exact only & 0 & 1 & 0 & 0 & 2 & 3 & 4 & 4 & 3 & 2 & 3 & 0 & 22 \\ Heuristic \& Metaheuristic & 0 & 6 & 6 & 3 & 3 & 5 & 7 & 4 & 9 & 10 & 10 & 1 & 64 \\ Exact, Heuristic, \& Metaheuristic & 0 & 0 & 0 & 1 & 3 & 2 & 0 & 3 & 0 & 2 & 1 & 0 & 12 \\ Other Approaches & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 2 & 1 & 0 & 0 & 1 & 5 \\ \hline Total & 0 & 7 & 6 & 5 & 8 & 10 & 11 & 13 & 13 & 14 & 14 & 2 & 103 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Distribution of Papers based on the Solution Approaches over the Years. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Solution Approaches** & **Ridesharing** & **Carpooling** & **Taxisharing** & **Vanpooling** & **Buspooling** & **Multi-Modal** & **Total** \\ \hline Exact only & 14 & 0 & 1 & 0 & 0 & 7 & 22 \\ Heuristic \& Metaheuristic & 33 & 18 & 10 & 1 & 1 & 1 & 64 \\ Exact, Heuristic, \& Metaheuristic & 6 & 3 & 2 & 0 & 1 & 0 & 12 \\ Other Approaches & 3 & 0 & 0 & 0 & 0 & 2 & 5 \\ \hline Total & 56 & 21 & 13 & 1 & 2 & 10 & 103 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Distribution of Papers based on the Solution Approacheshighest coverage in the review due to its practical usage. The 1-M scheme allows drivers to serve multiple passengers which can greatly utilise the vehicle's seat capacity. There are four categories according to data characteristics. These are: (i) static, (ii) stochastic, (iii) dynamic, and (iv) static and dynamic. In the static case, the vehicle task allocation can only be planned if the passenger information is known in advance. The stochastic case is a scenario that is similar to a real-life situation where some of the data are uncertain instead of fixed. Meanwhile, a dynamic shared mobility system can bring riders and drivers with similar travel patterns and schedules together on very short notice. The paper that considers a static problem first and further extends it to the dynamic problem is assigned to the fourth category (static and dynamic scenario). The use of software packages, real-time services, financial incentives, and social networking platforms is involved and impacts the current era of shared mobility [2]. They have resulted in a gradual increase of the dynamic SMP in recent decades [5]. Dynamic shared mobility requires an automated matching system so that passengers' demand can be scheduled on very short notice. In short, the dynamic shared mobility system is a problem with high complexity that requires a great deal of attention from researchers [26]. It could be the reason for the increasing trend of dynamic shared mobility studies as referred to in Table 5. As mentioned before, the dynamic shared mobility system is a high-complexity problem.