How do urban morphology, and individual traits shape real-world navigation behavior? To address this question, we analyzed data from everyday interactions with GPS-embedded mobile map applications, combining touchscreen based interaction data (i.e., taps) with graph-based street network metrics and self-reported measures of spatial anxiety (SA), sense of direction (SBSOD), and gender. We extracted interaction frequency (tap rate) and interaction regularity (inter-tap interval variability, CV–ITI) from smartphone map interaction patterns. Our analysis revealed that spatial anxiety consistently reduces both frequency and regularity of user engagement with these applications. Urban form also influences interaction; higher orientation entropy and greater degree centrality predict lower tap rates, while higher closeness centrality is associated with more regular interactions. This aligns with the literature on cognitive offloading suggesting passive navigation where users may simply follow the application’s instructions without active interaction. Moreover, urban form’s influence on interaction behavior varies with individual differences. Users with high spatial anxiety show more irregular interactions when navigating complex, winding layouts. In contrast, those with a good sense of direction benefit more from well-connected street networks, showing more regular patterns. Our findings highlight the value of integrating, urban networks, and individual traits to model real-world mobile map application use. By introducing a minimally invasive method for capturing map use in context, we provide critical insights for the design of adaptive navigation interfaces that facilitate more supportive and cognitively sustainable navigation experiences.
Large Language Models (LLMs) can be applied to various real-world tasks, many of which require spatial reasoning, like pattern-based spatial search, Point of Interest (POI) recommendation, and itinerary or travel planning. However, on their own LLMs have been shown to lack reliable spatial reasoning capabilities, especially about distances. Further, success at these tasks requires the LLM to have been exposed to the relevant places and to successfully disambiguate their spatial interpretations, which is affected by the popularity of the place name. To address these problems, we develop a novel approach using spatial Retrieval Augmented Generation (RAG) to enable an LLM to use relevant distance information not explicitly learned during training when answering spatial questions that involve distance reasoning. Our method encodes a spatial graph of geodesic and road network distances between cities and towns, as well as a mapping to their alternative names, and compares a variety of techniques to provide the LLM with the appropriate spatial context from those knowledge stores. By incorporating relevant spatial information, our method significantly outperforms the baseline LLM on several tasks requiring distance-based spatial reasoning, including nearest-neighbor questions and distance analogy questions. We further show through experiments which graph storage and retrieval methods perform best, and which are brittle when applied to more challenging questions. Given the vast array of possible places an LLM could be asked to reason about, our work offers a first step towards providing a rudimentary distance-oriented spatial ‘world model’ to complement the vast linguistic knowledge held in LLMs.
The rapid adoption of location-aware devices, such as smartphones, smartwatches, in-vehicle navigation systems, and asset-tracking tags, has resulted in massive volumes of spatio-temporal trajectory data. This data drives a wide range of commercial, transportation, and government applications. However, benchmarking these applications at scale remains difficult: existing network-based trajectory generators are batch-oriented, lack scalability, and cannot produce long-running, continuous trajectory streams that reflect realistic traffic dynamics. To overcome these limitations, we present TraSGen, a distributed, scalable, and continuous trajectory stream generator designed specifically for large-scale benchmarking of trajectory-driven systems. TraSGen produces realistic network-constrained trajectories using different traffic flow models that incorporate real-time road conditions, nearby moving and stationary objects, and other contextual factors. It accepts a GeoJSON-based road network as input, where nodes and edges may contain rich attributes. These attributes, combined with dynamic traffic information, guide the prediction of each subsequent trajectory point. TraSGen is adaptive; it can trade off the volume and synchronization level of traffic-data updates for higher generation speed, enabling users to benchmark systems under diverse load and realism requirements. Built on a shared-nothing architecture, it scales horizontally to support large and continuous trajectory workloads. Extensive experimental evaluation demonstrates TraSGen’s scalability, efficiency, and significant advantages over state-of-the-art trajectory generators.
Effective tourist route generation can substantially enhance visitor experiences in urban areas by offering a diverse set of relevant route options while promoting the efficient use of urban infrastructure. However, the generation of tourist routes poses substantial challenges, primarily due to a complex set of often conflicting optimization objectives, and the scarcity of historical user trajectories for training data-driven models. In this article, we propose DIRECT, a geospatially informed novel deep reinforcement learning-based approach for tourist route generation. DIRECT dynamically generates a diverse set of high-quality route alternatives, while respecting user-specified spatio-temporal constraints and category preferences. Crucially, DIRECT does not require any historical user trajectories; instead, it leverages publicly available point-of-interest (POI) data and road network information. The results of our extensive evaluation demonstrate that DIRECT outperforms baseline methods in generating diverse tourist routes.
Understanding human mobility patterns in urban areas is crucial for a wide range of applications, including urban planning, commercial area analysis, tourism strategies, and disease control measures. In response to these diverse needs, several area embedding techniques have been actively researched, aiming at modeling mobility patterns for individual urban areas as vector representations within latent spaces. On the other hand, word embeddings are known to exhibit a property called additive compositionality, where arithmetic operations on embeddings correspond to meaningful semantic manipulations (e.g., “king” - “man” + “woman” ≈ “queen”). However, it remains unclear whether this property also holds in area embeddings, and if so, how it can be practically applied. Therefore, this study introduces and formulates the concept of additive compositionality from linguistics into area embeddings, defining it as a frequency-weighted average where the weights correspond to the frequency of occurrence for each area in the mobility dataset. Furthermore, we propose practical methods leveraging this property for flexible post-hoc transformations of area embeddings into different spatial shapes without retraining, and quantitative modeling of temporal mobility shifts as arithmetic operations between embeddings from different periods. Our results demonstrate that additive compositionality in area embeddings can effectively support operations on mobility patterns, spatial embedding transformations, and analysis of temporal mobility dynamics.
