## 3 Optimization Problems in Vehicular Networks
### Ridesharing
They used a variety of techniques, including exact methods based on branch-and-cut and the rolling horizon method to solve the problem dynamically for quality of solution; an AI-based technique to limit the number of requests for the solver; a clustering method such as K-means and hierarchical clustering based on the shareability function to place the most shareable trips within a separate cluster; and finally, a heuristic algorithm to solve the matching problem within each cluster. As a result, the final algorithm provided high-quality solutions for large-scale problems in a short amount of time. It considers both passengers' and service providers' objectives, minimizing total travel time and distance while also minimizing passenger waiting time. To simulate the operation of their proposed dynamic ridesharing system, the authors created a plant model using macroscopic fundamental diagrams (MFD) to simulate real-world traffic conditions and a prediction model which calculates travel times during the assignment process using mean vehicle speed. [PERSON] et al. [70] proposed a new ride matching algorithm that takes into account the participants' characteristics and preferences. They defined joint socialness score (JSS) as a score of similarity between a driver and a rider while maintaining the maximum number of participants in a ridesharing system, and they planned to maximize JSS and allow a driver to be matched with more than one rider, even if only single rider-single driver matches are permitted. They checked the similarities between the routes of the drivers and riders using the Needleman-Wunsch algorithm and specified the score of matching, mismatching, and gap. Then, it was modified by removing the traceback process. They used the first-come, first-served method for matching. As a result, when a rider enters the system, the feasibility of each available driver is checked first. Following that, the JSSs for all possible drivers are computed. The rider is paired with the driver who has the highest corresponding JSS. The computational burden imposed by splitting drivers' routes on the algorithm resulted in longer computation times. Additionally, the proposed heuristic finds matches on relatively short notice, compared to integer programming, and also can be used to solve more complex and large-scale problems. Unlike most vehicular applications, which rely on the availability of an easily accessible internet infrastructure, [PERSON] et al. [71] focused on advanced services deployed by VANET to vehicles without infrastructure access. Their proposed multi-layer architecture is built on a procedure combining request, response, and acknowledgment messages with timers. A knowledge management layer facilitates the modeling of locally stored user profiles, using context-aware algorithms to match potential riders' mobility needs based on their itineraries and preferences. The route matching algorithm defines two distinct methods based on Euclidean distances: one that detects regular user routes using past itineraries, and one to determine whether two users can share a route based on weighted user characteristics. Both methods use GIS technologies to locate routes on a map and model user mobility patterns. Testing their proposal in a VANET simulator using the Simulation of Urban Mobility (SUMO) software to model traffic and NS-3 to simulate communications, they found that their VaNetLayer significantly reduced downtime and increased savings, outperforming the AODC and VNAODV protocols for delivery ratios. [PERSON] and [PERSON] [72] proposed a novel 1-to-n ridesharing scheme for effective ridesharing capable of collaborative 1-to-n ridesharing and recommending the shortest routes and pickup points for riders and drivers based on previously visited locations. Records for this 1-to-n ridesharing scheme are divided into three stages: trust and similarity models, 1-to-n ridesharing shortest routes and pickup points recommendations, and mutual authentication between the rider and car owner.