Matching Problem in Shared Ridehailing Services: Dynamics, Structures, and Algorithms

The demand for transportation is continuously growing due to the rapid urbanization and growth in population. To meet this rising demand and mitigate the negative impacts of transportation, more sustainable travel modes are needed. In recent years, with the advancement of technology and the advent of smartphones, high-speed internet, and new on-road communication equipment, interest in shared mobility services as a sustainable form of transportation has increased significantly. Ridehailing service is one of the most common types of shared mobility that plays an important role in current urban mobility systems. However, studies showed that on-demand ridehailing companies (e.g., Uber and Lyft) have aggravated the traffic congestion since each vehicle is assigned to only one triprequest at a time. Shared ridehailing which enables vehicles to serve multiple passengers simultaneously can be an appropriate solution. Ride-matching problem is the core of such services that matches vehicles and passengers. This study focuses on proposing novel algorithms for different types of ride-matching problems in which one vehicle can serve more than one passenger. Their dynamics and applications in urban transportation systems are also extensively addressed. First, a Two-to-One ride-matching algorithm is developed for the case where each passenger can share their ride with only one more passenger, which is one of the most common types of shared services. This service is then extended to a generic case and a novel Many-to-One ride-matching algorithm is proposed in which a vehicle can serve multiple passengers at a time. Both algorithms ensure a high level of service, while being efficient in terms of computational complexity. Since different parameters are involved in developing such algorithms, comparing them can provide a great deal of insights into their differences as well as their performance under various circumstances. To further improve the complexity and reduce the computational time of the proposed algorithms, a decentralized structure is presented that takes advantage of vehicle to infrastructure (V2I) and infrastructure to infrastructure (I2I) connectivity. Finally, the impact of adding a new shared ridehailing service on an urban transportation system is addressed, while analyzing its effects on the demand of different travel modes.