Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: TrafPS: A Visual Analysis System Interpreting Traffic Prediction in
  Shapley   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.

22
The categories of this paper are: chao-dyn
31
The categories of this paper are: ao-sci
35
The categories of this paper are: mtrl-th
16
The categories of this paper are: stat
10
The categories of this paper are: q-bio
23
The categories of this paper are: solv-int
14
The categories of this paper are: math-ph
21
The categories of this paper are: adap-org
3
The categories of this paper are: physics
11
The categories of this paper are: hep-lat
19
The categories of this paper are: eess
26
The categories of this paper are: bayes-an
29
The categories of this paper are: funct-an
18
The categories of this paper are: econ
25
The categories of this paper are: patt-sol
12
The categories of this paper are: nucl-th