The effects of including social factors in ride-matching algorithms on the performance and the quality of matches

Advancement in communication technologies has fostered alternative transport modes, such as ride-sharing. Ride-sharing aims to increase vehicle occupancy rates by matching riders with the drivers, who have empty seats on their vehicles and similar routes time schedules. Regarding success of a ride-sharing system, many researchers been interested efficient ride-matching algorithms. Ride-matching optimization problem is considered NP-Hard Problem. In most algorithms literature, be able find matches at short notice some parameters were omitted. Hence, social characteristics choices participants, gender, age, employment status willingness socialize, not included this paper, effects including factors algorithm performance quality are investigated. Several literature simulated randomly generated data. The simulation results show that when computation times significantly.