## 5 Current Trends
### Descriptive Analysis
On the other hand, a MO optimisation problem involves two or more optimisation goals in the study. The RB or so-called return restrictions is a feature which allows a participant with a car to leave their vehicle at the meeting point and join the carpool during the planning horizon. Therefore, they can return to the meeting point, where the car is parked, to drive to the final destination [64]. Meanwhile, a MH ridesharing system allows drivers to carry multiple riders at the same time and riders to transfer between vehicles or different modes of transportation [25]. A TP allows passengers to be transferred between multiple drivers. The driver drops off passengers at the transfer point and another driver will bring those passengers to their final destination [28]. Transfer services play an important role in mitigating the shortage of vehicle supply. It can ensure a high rate of ride-matching with a smaller number of vehicles on road. It is also proven that transfer services reduce the total travel distance. Furthermore, a MP can be an alternative point for riders to be picked up and dropped off instead of at the origin or destination. This meeting point can act as a hub for drivers to pick up and drop off multiple riders without visiting every riders' origins and destinations. It shortens the travel time, travel distance and cost in providing transportation services [50]. A TW is a time constraint where a customer or a task must be served within a certain time interval or time frame [118]. A ROF indicates that the participants' roles are flexible and can be changed in-between, either as a rider or a driver [21]. A DDF indicates the vehicles are not free to move everywhere and are limited by the vehicles' sources and destinations with possible detour flexibility [48]. Meanwhile, a PDF indicates the potential pickup/delivery locations are further selected from within the same areas, with proper adjustment to reduce the system cost and improve the matching rate with less inconvenience cost [55]. The TF indicates how much earlier a participant is willing to leave in exchange for being paired with another participant [35], while ATF indicates how flexible the participants are, in terms of time, to reach their final destination. This influences the matching rate in ridesharing [69]. The ROUF indicates the routes of the drivers involved in the rider's itinerary are not fixed to first-come, first-served order. This allows the driver's route to be flexible for the other riders [26]. The SB represents rideshare matches that considered a certain notion of stability to maximize the participation rate in a ridesharing [17]. In STA ridesharing, the matching of ridesharing requests must be made in advance. In STO environment, the simulated data involved random variables that are similar to real-time data. On the other hand, in Dy ridesharing, the ride-matching is matched within an instant and it may change over time. Riders in Dy ridesharing can join the ridesharing anytime and at any location without any booking in advance. Figure 7 shows the distribution of papers based on the problem characteristics mentioned earlier. The most researched problem characteristic is the TW which appears in 59 papers. The one with second-highest coverage is MO (51). In short, TW and MO are the most common problem characteristics considered in SMP. On the other hand, both ATF and PDF are the least studied (1) among the types of problem characteristics. Those problem characteristics could be the potential areas for future studies to create more precise and detailed results. Similar patterns can be observed for ridesharing, taxisharing, and carpooling problems on the distribution of the papers where both TW and MO are the most researched problem characteristics. These observations are clearly presented in Figures 8-10. In this review, there are two papers which focus on the busyooling problem. Both of the papers have the DC characteristics. In addition, each of the papers has an extra characteristic which is MO and TW, respectively.