Efficient alternative route planning in road networks

Alternative route planning requires finding k alternative paths (including the shortest path) between a given source and target. These paths should be significantly different from each other and meaningful/natural (e.g., must not contain loops or unnecessary detours). While there exists many work on finding high-quality alternative paths, the existing techniques are computationally expensive and are unable to accommodate the high volume of queries required by modern navigation systems. To address this, in this paper, we propose an efficient approach to compute high-quality alternative paths. Our approach employs hub-labeling to efficiently identify candidate alternative paths. The candidate paths are then ranked considering multiple quality metrics and the top- k alternative paths are returned. We propose several non-trivial optimizations to significantly improve the computation time. In our experimental study, we conduct experiments on three diverse real-world road networks and compare our proposed algorithm against six state-of-the-art algorithms. The results demonstrate that our algorithm is not only up to 3 orders of magnitude faster compared to most algorithms but also consistently generates alternative paths that are comparable or even superior in terms of quality across various metrics.