Computing the Fewest-turn Map Directions based on the Connectivity of Natural Roads
Bin Jiang, Xintao Liu

TL;DR
This paper presents a novel method for computing map directions that minimize turns and distance by leveraging natural roads' connectivity, resulting in routes that are more cognitively friendly and computationally efficient.
Contribution
The paper introduces a new approach based on natural roads' connectivity to compute routes with the fewest turns and shortest distances, improving over existing methods.
Findings
Fewest-turn routes have 50% fewer turns than Google Maps.
Fewest-turn-and-shortest routes are on average 15% shorter than Google Maps routes.
The approach reduces computational complexity by using a smaller graph of natural roads.
Abstract
In this paper, we introduced a novel approach to computing the fewest-turn map directions or routes based on the concept of natural roads. Natural roads are joined road segments that perceptually constitute good continuity. This approach relies on the connectivity of natural roads rather than that of road segments for computing routes or map directions. Because of this, the derived routes posses the fewest turns. However, what we intend to achieve are the routes that not only possess the fewest turns, but are also as short as possible. This kind of map direction is more effective and favorable by people, because they bear less cognitive burden. Furthermore, the computation of the routes is more efficient, since it is based on the graph encoding the connectivity of roads, which is significantly smaller than the graph of road segments. We made experiments applied to eight urban street…
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Taxonomy
TopicsUrban Design and Spatial Analysis · Data Management and Algorithms · Geographic Information Systems Studies
