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.

摘要

当今的导航系统使用地形图来传达路线信息。作为通用地图，地形图缺乏对路线读取和导航等特定任务的最佳支持。在公共交通领域，研究表明地形图不能像示意图那样支持路线规划。我们当前的论文将这个想法应用到车载导航领域。示意图通过突出显示与导航操作和方向相关的信息来强调地理和方向信息的功能方面。然而，缺乏系统的研究来研究示意性制图在寻路任务中的可用性。本文评估了使用我们之前工作中开发的算法创建的示意性路线图，涉及用户交互、导航性能和空间记忆性。我们在两个不同的任务中将这些示意图与相应的非示意图进行比较：前瞻性和情境（驾驶模拟器）路线读取。示意图和相应的非示意图在元素和拓扑方面是相同的；它们仅在几何形状上有所不同：在示意图上，特征是高度概括的，为了清晰起见，遵循示意图简化规则。实验数据表明，使用示意性路线图的参与者需要更少的地图交互来完成任务，方向信息更明显，从而获得更准确的空间知识。这一结果有助于更好地理解示意性路线可视化在支持用户寻路和定向任务方面的优势。