论文标题
Trafps：视觉分析系统解释Shapley的流量预测
TrafPS: A Visual Analysis System Interpreting Traffic Prediction in Shapley
论文作者
论文摘要
近年来，深度学习方法已被证明在交通流量预测中的表现良好，已经提出了许多复杂模型，以使交通流量的预测更加准确。但是，缺乏透明度会限制域专家了解输入数据何时何地影响结果。大多数城市专家和规划师只能根据自己的经验调整流量，并且无法对潜在的流量堵塞有效反应。为了解决这个问题，我们调整了沙普利的价值并提出一个可视化分析系统，该系统可以为专家提供交通流量预测的解释。 Trafps由三层组成，从数据过程到结果计算和可视化。我们在Trafps中设计了三种可视化视图，以支持预测分析过程。一个演示表明，TRAFPS支持有效的分析管道，以解释向用户的预测流，并为决策提供了直观的可视化。
In recent years, deep learning approaches have been proved good performance in traffic flow prediction, many complex models have been proposed to make traffic flow prediction more accurate. However, lacking transparency limits the domain experts on understanding when and where the input data mainly impact the results. Most urban experts and planners can only adjust traffic based on their own experience and can not react effectively toward the potential traffic jam. To tackle this problem, we adapt Shapley value and present a visualization analysis system , which can provide experts with the interpretation of traffic flow prediction. TrafPS consists of three layers, from data process to results computation and visualization. We design three visualization views in TrafPS to support the prediction analysis process. One demonstration shows that the TrafPS supports an effective analytical pipeline on interpreting the prediction flow to users and provides an intuitive visualization for decision making.
