Unlike commercial ridesharing, non-commercial peer-to-peer (P2P) ridesharing
has been subject to limited research — although it can promote viable
solutions in non-urban communities. This paper focuses on the core problem in
P2P ridesharing: the matching of riders and drivers. We elevate users’
preferences as a first-order concern and introduce novel notions of fairness
and stability in P2P ridesharing. We propose algorithms for efficient matching
while considering user-centric factors, including users’ preferred departure
time, fairness, and stability. Results suggest that fair and stable solutions
can be obtained in reasonable computational times and can improve baseline
outcomes based on system-wide efficiency exclusively.