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Existing cognitively motivated path search models ignore that we are hardly ever alone when navigating through an environment. They neither account for traffic nor for the social costs that being routed through certain areas may incur. In this paper, we analyse the effects of “not being alone” on different path search models, in particular on fastest paths and least complex paths. We find a significant effect of aiming to avoid traffic on social costs, but interestingly only minor effects on path complexity when minimizing either traffic load or social costs. Further, we find that ignoring traffic in path search leads to significantly increased average traffic load for all tested models. We also present results of a combined model that accounts for complexity, traffic, and social costs at the same time. Overall, this research provides important insights into the behavior of path search models when optimizing for different aspects, and explores some ways of mitigating unwanted effects.

Navigation services are essential for daily navigation, providing turn-by-turn instructions to help wayfinders reach their destinations. These services often differ from the heuristics wayfinders use, resulting in a poor user experience. Researchers have attempted to address this issue by developing algorithms that find less complex routes, by integrating prominent locations along the route to make wayfinding easier and to improve a wayfinder’s knowledge about the environment. These approaches, however, have taken a bottom-up approach, involving a limited number of participants navigating in real or virtual environments which may limit generalisability of results. In this study, we took a top-down approach by analysing a large dataset of GPS-based trips in the real world. Using the Geolife dataset, we analysed individual heuristics for route selection in terms of complexity and prominent locations, and found that wayfinders prefer less complex routes, such as routes that require fewer turns or involve simpler intersections. Additionally, we found that wayfinders choose routes with fewer prominent locations, such as routes that bypass well-known landmarks or busy commercial areas. These findings suggest that simplicity and ease of use are prioritized when selecting a route, while overly complex routes or areas with many points of interest are avoided.

Today's navigation assistance systems provide turn-by-turn instructions, which only focus on the next decision to take without offering any larger context. Information presentation is uniform and disconnected, i.e., any local instruction is equally important and not linked to any other decisions or the overall route context. This differs from how people usually give instructions and hinders spatial learning. In order to (re-)establish this larger context, we present an approach to identifying those locations along a route that define its characteristics, termed route-defining locations. These are prominent, easily recognized locations, which help relating the route to an environment's overall structure and a navigator's existing knowledge about the environment. The approach allows for determining route-defining locations on different levels of detail. Thus, at the same time it offers a mechanism for simplifying (or abstracting) a route. In this paper, we particularly focus on the latter aspect, presenting in detail the approach for identifying route-defining locations. For a given route, we, first, simplify its shape to extract those turns along the route that characterize its overall shape. Then, we rank landmarks and streets along the route based on their prominence. Finally, we include in the simplified route those turns that correspond to the locations of the most prominent landmarks and streets. The result is a set of route-defining locations extracted from the route shape, and prominent landmarks and streets along the route. In an agent-based simulation, we then evaluate the approach's ability to abstract a route to its characteristics, i.e., the defining locations. Results show that, indeed, our approach is effective in that respect, but success depends on ‘matching’ abstraction to an agent's knowledge about the environment.

Navigation services play a crucial role in everyday wayfinding. One of their key features is the turn-by-turn guidance, enabling wayfinders to navigate efficiently between locations. However, over-reliance on turn-by-turn guidance can hinder wayfinders' ability to perceive their surroundings and develop spatial awareness, negatively impacting user experience. Previous research has addressed these issues by modifying instructions, user interaction, or both. However, the applicability of these methods beyond specific studies remains unclear. This paper presents a human-centred study by evaluating an algorithm that automatically generates navigation instructions. The instructions are generated by adding route-defining locations to ‘standard’ turn-by-turn instructions to enhance wayfinder's spatial knowledge acquisition, and improve user experience. Route-defining locations, identified using a previously proposed algorithm, serve as anchors for the wayfinder to form a spatial understanding of the environment. The study involved 36 participants divided into two groups: one received ‘standard’ turn-by-turn (TBT) instructions, while the other received instructions that included route-defining locations (RDL). Participants navigated in an unfamiliar virtual environment, and their wayfinding and route learning were compared between the two groups. Results show that RDL instructions led to better wayfinding and route learning performance compared to TBT instructions, suggesting the potential to improve navigation systems and user experiences.

Anecdotal evidence shows that, sometimes, the instructions generated by a navigation service do not seem to match with how a wayfinder understands the given wayfinding situation. Such issues may make processing instructions harder for the wayfinder, and it may be a potential source of wayfinding errors and dangerous behaviour. These mismatches may be caused by several issues, ranging from errors in the base data to issues with the instruction generating system – the service’s inference system. In this work, we focus on the latter. We empirically investigate how much people agree with navigation instructions usefully describing a given wayfinding situation. To this end, we collected both quantitative (ratings) and qualitative data (comments and alternative instructions). Quantitative analysis supports the assumption that, sometimes, instructions just do not seem to fit. Qualitative analysis points to two main sources for the mismatches: 1) the language used in the instructions; 2) how the navigation service represents and reasons about a wayfinding situation.