A Novel GPU-Based Approach to Exploit Time-Respectingness in Public Transport Networks for Efficient Computation of Earliest Arrival Time

Abstract

In static temporal networks, the Earliest Arrival Time (EAT) problem is to calculate the earliest possible time of arrival at a set of vertices from a given source vertex. Applications of the EAT problem include designing efficient evacuation planning in dynamic scenarios, optimal journey planning in transport networks, and optimal flow management in supply chains. There exist several solutions for the EAT problem in the literature, however, there is limited work on GPU-based solutions to leverage the capabilities of the high throughput accelerator for better performance. Further, there is also a need for more efficient methods to process the inherent earliest arrival dependencies in a transport network. In this paper, we propose GPU algorithms for the one-to-all Earliest Arrival Time problem in public transport networks. Its key characteristic is that it is very fast for the best-case networks where all temporal paths are <i>time-respecting</i>. We propose our algorithms in five incremental ways, where the subsequent approaches are the improved version of previous ones. The Selective-check-version is the most improved approach and hence, the key algorithm. It uses shared memory for efficient computations. For the Selective-check version, we observed an average speedup of 6.45 against the Serial Connection-scan algorithm and 2.77 w.r.t. the Edge-version algorithm.