SP-TAG: a routing algorithm in non-stationary transportation networks

In a transportation network, the network topology and parameters can change with time, resulting in non-stationarity and time-dependent route preferences. Finding shortest routes is one of the most common queries on these time dependent networks. Developing efficient algorithms for computing shortest paths in a time varying spatial network is challenging because these journeys do not always display a greedy property or optimal substructure, making techniques like dynamic programming inapplicable. Time expanded graphs, which have been used to model dynamic networks employ, replication of the network across time instants, resulting in high storage overhead and algorithms that are computationally expensive. In contrast, we propose an algorithm based on a model called a time aggregated graph, which allows the properties of edges and nodes to be modeled as a time series, thus avoiding replication. The proposed algorithm uses an A* search framework and is based on an admissible and monotone heuristic. We present the analytical cost model for the algorithm and provide an experimental comparison with existing algorithms.