A GPU Algorithm for Earliest Arrival Time Problem in Public Transport Networks

Given a temporal graph G, a source vertex s, and a departure time at source vertex t
_s
, the earliest arrival time problem (EAT) is to start from s on or after t
_s
and reach all the vertices in G as early as possible. Ni et al. have proposed a parallel algorithm for EAT and obtained a speedup up to 9.5× on real-world graphs with respect to the connection-scan serial algorithm by using multi-core processors. We propose a topology-driven parallel algorithm for EAT on public transport networks and implement using general-purpose programming on the graphics processing unit (GPU). A temporal connection in a temporal graph for a public transport network is associated with a departure time and a duration time, and many connections exist from u to v for an edge ( u, v). We propose two pruning techniques connection-type and clustering, and use arithmetic progression technique appropriately to process many connections of an edge, without scanning all of them. In the connection-type technique, the connections of an edge with the same duration are grouped together. In the clustering technique, we follow 24-hour format and the connections of an edge are partitioned into 24 clusters so that the departure time of connections in the i
^th
cluster is at least i-hour and at most i+1-hour. The arithmetic progression technique helps to store a sequence of departure times of various connections in a compact way. We propose a hybrid approach to combine the three techniques (connection-type, clustering and arithmetic progression) in an efficient way. Our techniques achieve an average speedup up to 61× when compared to the existing connection-scan serial algorithm running on CPU. Also, the average speedup of our algorithm is 12.65× against the parallel edge-scan-dependency graph algorithm running on GPU.