A Unified Framework to Predict Movement
A Unified Framework to Predict Movement
复制标题
预测运动的统一框架
DOI：
10.1007/978-3-319-64367
复制
发表时间：
2017
期刊：
影响因子：
--
通讯作者：
Zuefle, Andreas
中科院分区：
文献类型：
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作者：
Gkountouna, Olga;Pfoser, Dieter;Wenk, Carola;Zuefle, Andreas
在当前以数据为中心的时代，有许多高度多样化的数据源提供有关网络上运动的信息，例如GPS轨迹、交通流量测量、票价卡数据、行人摄像头、自行车共享数据甚至地理社交运动轨迹。本愿景文件中确定的挑战是创建一个统一的框架，用于聚合和分析网络上此类多样化和不确定的移动数据。这需要概率模型来捕获网络上随时间推移的流量/体积和移动概率。需要新的算法来从具有不同不确定性水平的数据集训练这些模型。通过结合来自不同网络的信息，这种统一的移动模型的直接应用包括优化站点规划、地图构建、交通管理和应急管理。
In the current data-centered era, there are many highly diverse data sources that provide information about movement on networks, such as GPS trajectories, traffic flow measurements, farecard data, pedestrian cameras, bike-share data and even geo-social movement trajectories. The challenge identified in this vision paper is to create a unified framework for aggregating and analyzing such diverse and uncertain movement data on networks. This requires probabilistic models to capture flow/volume and movement probabilities on a network over time. Novel algorithms are required to train these models from datasets with varying levels of uncertainty. By combining information from different networks, immediate applications of such a unifying movement model include optimal site planning, map construction, traffic management, and emergency management.