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.

Keywords: Schematic map; digital cartography; human-computer interaction; navigation; route map; spatial cognition; spatial knowledge acquisition; spatial memorability; topological maps; usability; wayfinding.

Figure 1.

Example of the input (non-schematized) and output (schematized) route map produced by the method described by Galvão et al. (2020)

Figure 2.

Route 1 a) non-schematic vs b) schematic map type at initialization (lowest zoom level). Maps size was constantly 150 × 75 mm during the experiment. Label overlaps disappeared when participants zoomed in

Figure 3.

Route 2 a) non-schematic vs b) schematic map type at initialization (lowest zoom level). Maps size was constantly 75 × 150 mm during the experiment. Label overlaps disappeared when participants zoomed in

Figure 4.

Route instruction ordering (RIO) web application interface. On the left, the interactive route map, on the right, the sortable list of instructions

Figure 5.

Situated route reading task implemented in the virtual-reality driving simulator: a) 3D city model created with CityEngine and SketchUp (here showing part of Route 2 from above). b) Hardware setup. c) A user navigating in the virtual environment while using the interactive map application on the tablet

Figure 6.

Examples of typical map view during the driving task: a) Route 1 type non-schematic (on the left) vs. b) Route 1 type schematic. The map dimension is fixed (150x75 mm) but participants can zoom and pan to a desired focus point. The maps are always north-oriented and rotations are not allowed. The yellow car icon indicates the driver’s virtual location on the route

Figure 7.

The landmark placement application interface for Route 1 type schematic. Only the landmarks recalled previously in the landmark recall test can be placed on the base map. For polygonal landmarks, the user first needed to drag and drop the landmark’s bounding box onto the desired position before seeing the polygon’s true shape

Figure 8.

Experimental design and procedure. The experiment is composed of two tasks (RIO and driving) and two memory tests: a landmark recall and a landmark placement test. In this process diagram, the letter ”A” and ”B” is used to indicate two different routes, directions, or map types. For example, if ROUTE_A is the Route 1, then ROUTE_B is the Route 2; and if ROUTE_A is the Route 2, then ROUTE_B is the Route 1

Figure 9.

Recall of landmarks: a) After the RIO task, 44.6% of the map’s point-like landmarks were recalled by non-schematic map type users, while 48.9% by the schematic map type users. For polygonal landmarks, 36.6% by non-schematic users and 35.9% by schematic users. b) After the driving task, 43.4% of the map’s point-like landmarks were recalled by non-schematic map type users, while 50.9% by the schematic map type users. For polygonal landmarks, 20.4% by non-schematic users and 15.9% by schematic users. c) After the driving task, 7.1% of the point-like and 8.4% of the polygonal foils were (erroneously) recalled. After the RIO task, 4.9% of the point-like and 7.2% of the polygonal foils were (erroneously) recalled

Figure 10.

Topological subdivisions are formed by the street network and the city’s boundaries. a) Route 1 and b) Route 2

Figure 11.

Topologically correct placement: Percentage of the correctly recalled global landmarks that were placed in the correct topological region, aggregated by the map type. Excluding responses for which participants reported 0% of confidence

Figure 12.

The difference in the landmarks’ relative visibility between non-schematic (orange) and schematic (blue) map type in the driving task for Route 1 (similar results were found for Route 2 and in the RIO task). The bars show each landmark’s mean visible time divided by the map use time, calculated as $t_i\times 100/T$. Where $T$ is map use time, and $t_i$ is the time for which the given landmark $i$ was present within the map’s viewport