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Mobilities in Network Topology and Simulation Reproducibility of Sightseeing Vehicle Detected by Low-Power Wide-Area Positioning System

机译:低功耗广域定位系统检测到的观光车辆网络拓扑和仿真再现性的移动性

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Vehicle mobilities for passengers in a city's downtown area or in the countryside are significant points to characterize their functions and outputs. We focus on commercial sightseeing vehicles in a Japanese city where many tourists enjoy sightseeing. Such mobilities and their visualizations make tourist activities smoother and richer. We design and install a low-power, wide-area positioning system on a rickshaw, which is a human-pulled, two- or three-wheeled cart, and monitor its mobility in Hikone City. All the spatial locations, which are recorded in a time sequence on a cloud server, are currently available as open data on the internet. We analyze such sequential data using graph topology, which reflects the information of corresponding geographical maps, and reproduce it in cyberspace using an agent-based model with similar probabilities to the accumulated rickshaw records from one spatial node to another. Although the numerical results of the agent traced in a simulated city are partially consistent with the rickshaw's record, we identify some significant differences. We conclude that the rickshaw's mobility observed at the actual sightseeing sites is partially in the random motion; some cases are strongly biased by memory routes. Such non-randomness in the rickshaw's mobility indicates the existence of specific features in tourism sources that are identified for each sightseeing activity and affected by local sightseeing resources.
机译:城市市中心或农村乘客的车辆移动性是其功能和产出的重要观点。我们专注于日本城市的商业观光车辆,许多游客享受观光。这样的司马和他们的可视化使旅游活动更加顺畅和更丰富。我们在人力车上设计和安装低功耗广域定位系统,该系统是人类拉动,两轮或三轮推车,并监控其在Hikone City的移动性。所有空间位置都在云服务器上的时间序列中记录,当前作为Internet上的打开数据可用。我们使用图形拓扑分析了这种顺序数据,这反映了相应地理地图的信息,并使用基于代理的模型在网络空间中重现它,该模型具有与从一个空间节点到另一个空间节点的累积人力车记录的类似概率。尽管在模拟城市中追踪的代理的数值结果与人力车的记录部分一致,但我们确定了一些显着差异。我们得出结论,在实际观光地点观察到的人力车的流动部分在随机运动中;一些情况受到内存路线的强烈偏见。人力车移动性的这种非随机性表明旅游来源的特定特征存在于每个观光活动和受当地观光资源的影响。

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