An Efficient Multi-Criteria Path Selection Approach in Road Networks through Influencer Nodes and K-hop Search

We present an efficient approach for a multi-criteria path selection problem in road networks. Since, computing shortest paths among all pairs of nodes in a large network is computationally expensive even with a single criterion. Therefore, performing multi-criteria path search becomes costly and inefficient due to excessive traversals over the network with comparisons between all possible paths. Existing algorithm solved the aforementioned problem by introducing a reference point but they performed a large number of offline pre-computations. In this paper, we introduce an efficient multi-criteria-based approach, in which we uniquely model a road network with places of interest into an attributed graph and restrict our algorithm to explore a minimum number of hops while satisfying maximum criteria. Our objective is to get an optimal path, having minimum path length in terms of number of hops while meeting maximum criteria. Experiments are conducted to verify the effectiveness of the proposed approach. The experimental results depict that the proposed algorithm achieves high accuracy in terms of fulfilling the criteria while avoiding pre-processing steps with minor change in execution time.