Pruning Techniques for the Stochastic on-time Arrival Problem\texorpdfstring -- An Experimental Study

TL;DR

This paper introduces a novel pruning approach to significantly speed up stochastic on-time arrival route planning on large networks, reducing computational effort while maintaining accuracy.

Contribution

It presents a new pruning technique that leverages existing methods for alternative routes to accelerate stochastic routing computations.

Findings

Speed-up of stochastic route planning by orders of magnitude

Minimal loss in accuracy with the new pruning method

Effective application on large-scale road networks

Abstract

Computing shortest paths is one of the most researched topics in algorithm engineering. Currently available algorithms compute shortest paths in mere fractions of a second on continental sized road networks. In the presence of unreliability, however, current algorithms fail to achieve results as impressive as for the static setting. In contrast to speed-up techniques for static route planning, current implementations for the stochastic on-time arrival problem require the computationally expensive step of solving convolution products. Running times can reach hours when considering large scale networks. We present a novel approach to reduce this immense computational effort of stochastic routing based on existing techniques for alternative routes. In an extensive experimental study, we show that the process of stochastic route planning can be speed-up immensely, without sacrificing much in terms of accuracy.