## 5 Current Trends
### Descriptive Analysis
For the vanpooling problem, only one paper is found in the review and this is categorised as a TW problem. Figure 8: Distribution of Papers based on the Problem Characteristics: Ridesharing. Figure 7: Distribution of Papers based on the Problem Characteristics. Figure 9: Distribution of Papers based on the Problem Characteristics: Taxisharing. Figure 11 shows the distribution of papers based on the problem characteristics for multi-modal ridesharing. About a quarter of the papers on multi-modal ridesharing researched in TP. On the other hand, MO problem characteristics are covered in six papers. TF, DC, and RB is the least-studied problem characteristics in multi-modal ridesharing which only accounted for one paper each. Table 5 reveals the different types of data characteristics researched in SMP over the years. It is observed that the static and dynamic problems are the most popular topics among the researchers with 49 and 48 papers, respectively. Moreover, four papers considered both the static and dynamic problems in the SMP. The stochastic problem is the least researched in this study, as it only accounted for two papers. Figure 11: Distribution of Papers based on the Problem Characteristics: Multi-Modal. Figure 10: Distribution of Papers based on the Problem Characteristics: Carpooling. In Table 6, we see that the dynamic ridesharing problem is the most popular problem focused on as compared to other types of data characteristics. The dynamic ridesharing problem is covered in 37 of the papers in the review. Slightly more than half of the papers in the taxisharing problem considered the static problem, making it the most among other papers in the review for taxisharing. Only two papers considered the busyooling problem and both are concerned with the static problem. This single focus is also the case where there is only a single vanpooling paper and it too considered the static problem. In the carpooling problem, 18 papers considered the static cases while the remaining 3 papers focused on the dynamic cases. No research is conducted on the stochastic carpooling problem. In Multi-Modal problem, six of the multi-modal-related papers focused on the dynamic problem while the remaining four of the papers focused on the static problem. No research is conducted on the stochastic multi-modal problem. Table S4 (see Supplementary File) presents the summary of papers based on the 19 distinctive optimisation objectives of the SMP. The optimisation objectives are: Travel Time (TM), Number of Vehicles on Road (NV), Total Cost (TC), Travel Distance (TD), Waiting Time (WT), Delay Time (DT), CO\({}_{2}\) Emission (CE), Number of Transfer (NT), Passengers' Inconvenience Cost (PIC), Penalty Cost (PC), Number of Modal Changes (NMC), Waiting Distance Cost (WDC), Subsidy (SD), Number of Matched Rides (NMR), Ride Profit (RP), Evaluation Credit Score (ECS), Seat Usage Rate (SUR), Quality of Service (QoS), and lastly, Social Welfare (SW). Minimisation of TC stands for the cost minimisation for the driver or rider to travel from one origin to one destination [31]. Meanwhile, minimisation of TM refers to the shortest time taken to travel for the driver or rider from one location to one destination [64]. TD minimisation represents the shortest distance path for either the driver or rider from origin to a destination [23]. Minimisation of CE is one of the objectives that takes the CO\({}_{2}\) emission from the vehicles into consideration [76]. Minimisation of the NV is to reduce the number of drivers on the road by maximising the successful matching of riders and drivers [45]. As for the DT, the smallest difference between the desired arrival time and the actual arrival time for a trip is to be minimised [102]. On the other hand, WT for the driver and rider can be different in terms of waiting as a target. Minimisation of the driver's WT is to minimise the time used by a driver to wait for the rider at the pickup point while minimisation of the rider's WT is to minimise the time wasted to wait for the