  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.
