With increasing urbanization and a need to reduce greenhouse gas emissions, new forms of urban mobility are studied worldwide. An interesting transportation method is ridesharing, where people can share parts of their rides with other passengers travelling in a similar direction in the same car. To im- prove the attraction of ridesharing systems, research into extensions to the existing ridesharing model formulation is required. The goal of this research is to make ridesharing more attractive to potential users by including the option to make reservations. In this work, the inclusion of reservations (pre-bookings) into the on-demand ridesharing problem is studied. The inclusion of ridesharing has to potential to make better assignments by having knowledge on part of the future demand. The main challenge from reservations is the increased computational load generated by having more available information. This thesis proposes multiple adjustments and different heuristics for a state-of-the-art on-demand ridesharing system to deal with the requirements and challenges brought by the incorporation of reservations. An additional consideration is a trade-off between providing benefits for reservations and serving on-demand requests. Numeric experiments are performed on the Manhattan street network with NYC taxicab request data. The results indicate that reservations can be included in on-demand ridesharing at a tolerable compu- tational cost with the proposed framework. Passengers that make reservations shortly in advance can be guaranteed service once initially accepted by the system. Having passengers make reservations in advance does increase the overall service the system can provide.

On-demand ridesharing might be an effective solution to reduce traffic congestion and, as a result, reduce emissions to mitigate environmental issues. However, travel times are more uncertain in ridesharing services compared to non-shared transit services due to their shared nature. For example, at one moment, the users are informed that the travel time takes five minutes, a moment later, a request is added to the trip and the users are now confronted with a travel time of ten minutes instead. This uncertainty in travel time is called unreliability. Unreliability is perceived as inconvenience by the users and it has a negative impact on the users' travel habits regarding ridesharing service.

Therefore, this research provides a novel solution to deal with this problem while mitigating the negative effects on the other quality-of-service indices, e.g., waiting time, rejection rate, and delays. This is achieved by providing two indicators to each new user: (1) the possibility that an additional request will be added to the trip that negatively influences the trip duration of the user; and (2) a prediction of the magnitude of a possible delay for whenever the case occurs that a new request will be added to the trip. These indicators are based on predictions, created by shareability shadows which determine the number of requests that might be added to the trip, and implementing a simple time-series demand forecasting method named exponential smoothing. The shareability shadow makes predictions by confining the regions of ridesharing opportunities by analyzing the travel constraints of the involved users. The solution is validated using a state-of-the-art routing and assignment method and a test case of Manhattan, New York City. With the formulated measure, on average, 58% of the requests receive a correct prediction about the possibility of a negative change, 17% receive an indecisive prediction, i.e., a prediction stating that the possibility of a negative change is "medium", and 25% of the requests receive an incorrect prediction about the possibility of a negative change. Moreover, the measure successfully provides a predicted magnitude of the travel time increase with a 95% confidence interval.