“Simplest” Paths: Automated Route Selection for Navigation

Abstract

Abstract. Numerous cognitive studies have indicated that the form and complexity of route instructions may be as important to human navigators as the overall length of route. Most automated navigation systems rely on computing the solution to the shortest path problem, and not the problem of finding the "simplest" path. This paper addresses the issue of finding the "simplest" paths through a network, in terms of the instruction complexity. We propose a "simplest" paths algorithm that has quadratic computation time for a planar graph. An empirical study of the algorithm's performance, based on an established cognitive model of navigation instruction complexity, revealed that the length of a simplest path was on average only 16% longer than the length of the corresponding shortest path. In return for marginally longer routes, the simplest path algorithm seems to offer considerable advantages over shortest paths in terms of their ease of description and execution. The conclusions indicate several areas for future research: in particular cognitive studies are needed to verify these initial computational results. Potentially, the simplest paths algorithm could be used to replace shortest paths algorithms in any automated system for generating human navigation instructions, including in-car navigation systems, Internet driving direction servers, and other location-based services.

Algorithms for Reliable Navigation and Wayfinding

“…The following algorithm is based on the simplest path algorithm [7]. The simplest path algorithm aims to minimize the instruction complexity of a route description, i.e., it favors intersections that are easy to describe.…”

“…The weight used in the simplest path algorithm represents the instruction complexity to negotiate an intersection. The landmark spider algorithm given in [1] uses the same algorithm as in [7] with the only difference in the weighting function for an intersection, which depends on the distance, saliency, and orientation of a traveler with respect to any landmark present near the intersection. The aim is to generate a clearest [1] path in terms of spatial references and landmarks used to describe the route.…”

Decentralized orchestration of data-centric workflows in Cloud environments

“…Most types of spatial analysis include the interrogation of fundamental topological spatial relationships between the constituent spatial objects, such as when two objects touch or overlap [14]. These relationships fundamentally underpin applications in the spatial sciences, from spatial autocorrelation analysis [15], trip planning [16] and route directions communication [17]. Graph-based data structures are efficient representations supporting the encoding of topological relationships and their computational analysis (e.g., least-cost path algorithms [18]).…”

Urban granularities—a data structure for cognitively ergonomic route directions

“…Mark [36] discussed this aspect in the context of inferring the navigational complexity of intersections from their structural qualities. Duckham and Kulik [13] used his approach to calculate a route with the least descriptional complexity. Within the context of a specific route, intersections can be highly salient features, especially when they enforce a change in the …”

“…This issue will dominate the discussions throughout this paper. Generally, the following approaches to route information can be distinguished: a) those that take a complete route as input and optimize route directions for this particular route [9,44,28]; b) those that optimize the route choice based on cognitive aspects, for example, to ease the description of the route or to reduce the likelyhood of getting lost [13,5,20]; c) those that differentiate between parts of the environment that are known to the wayfinder and parts that are unknown. The idea here is to provide only coarse information for the known parts (i.e., to abstract from a concrete route and only announce (intermediate) destinations), while being detailed in the unknown parts (i.e., giving turn-by-turn instructions) there [41,22,47,53,49,46].…”