Wayfinding signs play an important role in guiding users to navigate in a virtual environment and in helping pedestrians to find their ways in a real-world architectural site. Conventionally, the wayfinding design of a virtual environment is created manually, so as the wayfinding design of a real-world architectural site. The many possible navigation scenarios, as well as the interplay between signs and human navigation, can make the manual design process overwhelming and non-trivial. As a result, creating a wayfinding design for a typical layout can take months to several years. In this paper, we introduce the Way to Go! approach for automatically generating a wayfinding design for a given layout. The designer simply has to specify some navigation scenarios; our approach will automatically generate an optimized wayfinding design with signs properly placed considering human agents' visibility and possibility of making mistakes during a navigation. We demonstrate the effectiveness of our approach in generating wayfinding designs for different layouts such as a train station, a downtown and a canyon. We evaluate our results by comparing different wayfinding designs and show that our optimized wayfinding design can guide pedestrians to their destinations effectively and efficiently. Our approach can also help the designer visualize the accessibility of a destination from different locations, and correct any "blind zone" with additional signs.
An important consideration in designing a layout is the navigation experience of the pedestrians. Recently, proposed an approach @cite_26 which uses crowd simulation to generate mid-scale layouts optimal with respect to human navigation properties such as mobility, accessibility and coziness. However, concerning navigation, one important consideration is missing: the wayfinding experience of the pedestrians in the generated environments. We argue that their generated layouts are navigation-aware only if wayfinding signs are properly placed in the layouts.
@cite_26
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We propose a novel approach for designing mid-scale layouts by optimizing with respect to human crowd properties. Given an input layout domain such as the boundary of a shopping mall, our approach synthesizes the paths and sites by optimizing three metrics that measure crowd flow properties: mobility, accessibility, and coziness. While these metrics are straightforward to evaluate by a full agent-based crowd simulation, optimizing a layout usually requires hundreds of evaluations, which would require a long time to compute even using the latest crowd simulation techniques. To overcome this challenge, we propose a novel data-driven approach where nonlinear regressors are trained to capture the relationship between the agent-based metrics, and the geometrical and topological features of a layout. We demonstrate that by using the trained regressors, our approach can synthesize crowd-aware layouts and improve existing layouts with better crowd flow properties. Humans navigate crowded spaces such as a university campus by following common sense rules based on social etiquette. In this paper, we argue that in order to enable the design of new target tracking or trajectory forecasting methods that can take full advantage of these rules, we need to have access to better data in the first place. To that end, we contribute a new large-scale dataset that collects videos of various types of targets (not just pedestrians, but also bikers, skateboarders, cars, buses, golf carts) that navigate in a real world outdoor environment such as a university campus. Moreover, we introduce a new characterization that describes the “social sensitivity” at which two targets interact. We use this characterization to define “navigation styles” and improve both forecasting models and state-of-the-art multi-target tracking–whereby the learnt forecasting models help the data association step. Navigation services communicate optimal routes to users by providing sequences of instructions for these routes. Each single instruction guides the wayfinder from one decision point to the next. The instructions are based on geometric data from the street network, which is typically the only dataset available. This paper addresses the question of enriching such wayfinding instructions with local landmarks. We propose measures to formally specify the landmark saliency of a feature. Values for these measures are subject to hypothesis tests in order to define and extract landmarks from datasets. The extracted landmarks are then integrated in the wayfinding instructions. A concrete example from the city of Vienna demonstrates the applicability and usefulness of the method. The objective of this study is the development of a computer simulation model for pedestrian movement in architectural and urban space. The characteristic of the model is the ability to visualize the movement of each pedestrian in a plan as an animation. So architects and designers can easily find and understand the problems in their design projects. In this model, the movement of each pedestrian is simulated by the motion of a magnetized object in a magnetic field. Positive magnetic pole is given to each pedestrian and obstacles like walls and columns. Negative magnetic pole is located at the goal of pedestrians. Each pedestrian moves to his goal by the attractive force caused by the negative magnetic pole at his goal, avoiding collisions with other pedestrians and obstacles by repulsive forces caused by the positive magnetic poles. The effectiveness of the simulation model is shown by the following two kinds of simulation examples. (1) Evacuation from an office building In this model pedestrians walk along the route from each starting point to the exit in case of evacuation. The example shows the places where stagnations and heavy congestions occur, and designers can see if the evacuation routes are appropriate. (2) Movement of pedestrians in queue spaces Three types of queuing behavior is classified in this model: movement in front of counters, movement passing through ratches, and movement of getting on and off in elevator halls. Simulation examples in a railway station and in a main floor of a resort hotel are shown where several kinds of queue spaces are included and complicated movements of hundreds of pedestrians occur.