Proactive and reactive carpooling recommendation system based on spatiotemporal and geosocial data
In this paper, we present a new carpooling recommendation system whose main objective is to find the best carpool matchings and recommend individuals to join their friends during trips or travels. The proposed recommendation system utilizes user's mobility history and user social network information to find carpool matchings. The proposed system employs a probabilistic model based on continuous time Markov chain to model user's mobility and to predict user future movements. Moreover, it uses two similarity measures (interest based similarity and friendship based similarity) to find the similarities between users. The interest based similarity uses a weighted bipartite graph between users and places, where the edges are weighted by the term frequency inverse document frequency. The friendship based similarity uses the common friends as a similarity measure. The proposed system is evaluated using the number of carpool matchings that can be found, this number gives an indication of the reduction that can be made in vehicular traffic congestion, pollutant emissions and energy consumption.
2016 IEEE 12th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob)