Evaluating schematic route maps in wayfinding tasks for in-car navigation

Abstract

Today's navigation systems use topographic maps to communicate route information. Being general-purpose maps, topographic maps lack optimal support for the specific task of route reading and navigation. In the public transportation domain, research demonstrated that topographic maps do not support planning of routes as good as schematic maps. Our current paper applies this idea to the domain of in-car navigation. Schematic maps emphasize functional aspects of geography and direction information by highlighting information relevant to navigation actions and orientation. However, there is a lack of systematic studies researching the usability of schematic cartography in wayfinding tasks. This article evaluates schematic route maps, created with an algorithm developed in our previous work, regarding user interaction, navigation performance, and spatial memorability. We compare these schematic maps with correspondent non-schematic ones in two different tasks: prospective and situated (driving simulator) route reading. The schematic map and the corresponding non-schematic map are identical in terms of their elements and topology; they vary only in their geometric shape: on the schematic maps, features are highly generalized, following schematic simplification rules for clarity. The experimental data shows that participants using the schematic route maps require fewer map interactions to complete the tasks, orientation information is more visible and leads to more accurate spatial knowledge acquisition. This result contributes to a better understanding of schematic route visualizations' benefits to support users in wayfinding and orientation tasks.

Route effects in city-based survey knowledge estimates

“…Lastly, our work has practical implications for cartography and spatial human–computer interaction (Galvão et al. 2021 , 2022 ; Krukar et al. 2020 ), as it shows how to identify spatial context particularly challenging for human survey knowledge estimation.…”

“…Lastly, our work has practical implications for cartography and spatial human-computer interaction (Galvão et al 2021(Galvão et al , 2022Krukar et al 2020), as it shows how to identify spatial context particularly challenging for human survey knowledge estimation. Wayfinding support applications could consider spatial context in which two equally plausible routes are available to a distant target and provide their user with additional support in these challenging situation.…”

Federated Learning-Based Detection and Control Mechanism of In-Car Navigation Safety System

“…Recently, in-car navigation systems have become integral to modern vehicles, providing drivers with real-time guidance and enhancing their overall driving experience. These systems rely on GPS technology, map data, and intelligent algorithms to offer accurate and efficient navigation [1][2]. However, the increasing complexity and reliance on in-car navigation systems pose significant challenges in ensuring their safety and reliability.…”

Revealing Schematic Map Designs with Preservation of Relativity in Node Position and Segment Length in Existing Official Maps

“…The original map is shown in Figure 1b. In recent decades, the automated generation of schematic maps has become an important research topic in the cartographic community, and many automated methods have been developed [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18]. In these automated methods of schematic network maps, the available design rules are defined by following the designs utilized in the London tube map, including the preservation of topology, simplification of the shapes and directions of network lines, and enlargement of dense regions [19,20].…”