LAUSR

Trip planning services have been developed along with tourism promotion and information technology evolutions, where we must construct trip routes that simultaneously optimize multi-objective functions such as trip expenses and user satisfaction. Moreover, utilization of past-trip records is essential, because similarities to past-trip records well reflect users’ general preferences and tendencies during trip planning. In this paper, we propose a multi-objective trip planning method using ant colony optimization (ACO). By effectively using the pheromones in ACO, we can construct trip routes similar to trip records stored before and the constructed route can reflect users’ general preferences. In addition, we vary ants’ behaviors in ACO corresponding to various objective functions and hence we can obtain multi-objective trip routes naturally. Experimental results demonstrated that our method outperforms the baseline methods in terms of point-of-interest (POI) satisfaction, POI cost, and past-trip similarity. We also conducted a user study, which clearly indicates that our method obtains high scores through various user questionnaires.