Integrating perspectives of mobility for spatially-explicit Agent-Based Modelling

基于Agent的移动的对象的模拟非常普遍，从建筑物内的人的建模运动到栖息地中的动物（和动物群体）。通过基于代理的模型（ABM）模拟运动的一个集成因素是寻路算法，尽管实现方式因学科而异。鉴于最近有人呼吁采用综合方法来解决移动性问题，该提案提出了3种多学科策略来改善ABM中的寻路：（1）利用最小成本表面来生成3D拓扑结构;（2）利用高频移动数据来提取引导移动的概率表面;以及（3）联合收割机上述方法与来自行人模拟的视觉模型（例如，外体视觉架构）来考虑控制运动的多个因素。对于第一步，最近的发展，包括高分辨率卫星图像和3D建筑模型，提供了创建成本最低的表面，可用于开发具有非常高的空间和时间精度的详细寻路算法，这也是有效的。 在这个方向上，GIScience研究已经利用3D建筑模型来开发3D拓扑结构，以支持紧急疏散规划，并利用3D拓扑结构进行多层建筑物的紧急疏散是几个期刊出版物的经常性主题。它们主要通过在这个3D网络中识别最短路径（使用标准算法，如Djisktra）来工作，目的是向用户显示从当前位置到建筑物出口点的有效出口路线。最低成本的表面可以用来开发一个全球模型的空间距离之间的点的起点和目的地，然后转换成基于矢量的三维拓扑结构。下一步是设计一种生成“概率表面”的方法，该方法利用动物和人类的高频运动数据。这些数据的数量，加上高分辨率遥感数据，为使用地理空间运算符和机器学习提供了一个机会，以提取可能发生移动的位置的概率（以连续栅格层的形式呈现）。最后一步是将行为和视觉概念联系起来（例如，例如，通过从高分辨率卫星数据导出虚拟3D环境和基于正面视角改进动物行为模拟，我们可以从行人模拟扩展到运动生态学（Exosomatic Visual Architecture）。预计新的算法将被证明是有用的，在建设移动的代理在广泛的应用。这些包括使用高度详细的3D建筑模型的建筑物疏散ABM模型，详细的街区中的行人以及栖息地中的动物运动。结合更高的空间分辨率数据集和3D建筑模型，这些改进几乎是提高ABM模拟精度以及减少计算负荷的必要条件。

Agent--Based simulations of mobile objects are widespread, from modeling movement of people inside buildings to animals (and animal groups) in habitats. One integrating factor in simulating movement via Agent--Based Models (ABMs) are wayfinding algorithms, although implementation varies by disciplines. Given that there have been recent calls for integrated approach to mobility, this proposal suggests 3 multidisciplinary strategies to improve wayfinding in ABMs: (1) Utilize least cost -surfaces to generate 3D topology (2) Utilize high frequency movement data to extract probability surfaces guiding movement, and (3) Combine the methods developed above with visual models from pedestrian simulations (e.g., exosomatic visual architecture) to consider multiple factors governing motion. For step one, recent developments, including high resolution satellite imagery and 3D building models, afford the creation of least cost surfaces that can be used to develop detailed wayfinding algorithms with very high spatial and temporal accuracies which are also efficient. Moving in this direction, GIScience research has utilized 3D building models to develop 3D topology to support emergency evacuation planning, and making use of 3D topology for emergency evacuations from multi-storied buildings is a recurrent theme of several journal publications. They primarily work by identifying the shortest path (using standard algorithm such as Djisktra's) in this 3D network, with the goal of showing the user an efficient egress route from their current location to a building exit point. Least cost surfaces can be utilized to develop a global model of spatial distance between points of origin and destination, and then converted into a vector--based 3D topology. The next step is to devise a method of generating "probability surfaces", which utilizes high frequency movement data for both animals and humans. The volume of this data, combined with high resolution remote sensing data, provides an opportunity for the use of geospatial operators and machine learning to extract probabilities of locations where movement may occur (presented as a continous raster layer). The final step will be to link behavioural and visual concepts (e.g., exosomatic visual architecture) from pedestrian simulation and extend to movement ecology, for example, by deriving virtual 3D environments from high resolution satellite data and improving animal behavioural simulations based on a frontal perspective. It is expected that the new algorithms will prove useful in building mobile agents across a wide spectrum of applications. These include ABM models of building evacuation using highly detailed 3D building models, pedestrians in a detailed neighbourhood, and animal movement in their habitats. Combined with higher spatial resolution datasets and 3D building models, such improvements are almost a necessity to improve ABM simulation accuracy as well as reduce computational loads.