A Vehicle Ride-Sharing Algorithm Assessing Passenger Satisfaction According to Spatial, Temporal, and Social Behavior Context Based on Real Data Sources

Vehicle ride-sharing commute in smart cities is a service that has changed the way of citizens’ daily life and transportation schedule. Research in vehicle ride sharing aims to provide passengers with a comfortable living and well-being within the city. Ride sharing has a significant role in vehicle transportation services provided to passengers during their daily schedule from a certain origin to a desired destination within smart cities. Combining ride sharing with spatial, temporal, and social context has an impact on passenger satisfaction. In this paper, a vehicle ride-sharing algorithm is introduced, which incorporates certain spatial, temporal, and social behavior context restrictions that are able to provide a satisfactory routing trajectory that serves the daily needs of passengers in the smart city of Athens, Greece. Real data sources were exploited to evaluate certain spatial, temporal, and social matching distance functions, which define specific spatial, temporal, and social matching similarity thresholds of passengers’ social mobility behavior. The proposed algorithm is evaluated experimentally with real data based on specific evaluation metrics assessing its efficiency with regards to certain spatial, temporal, social, capacity, and satisfaction contexts. The evaluation process has an impact on the adoption of the proposed algorithm in vehicle ride-sharing commute in smart cities.