  Real time traffic navigation is an important capability in smart
transportation technologies, which has been extensively studied these years.
Due to the vast development of edge devices, collecting real time traffic data
is no longer a problem. However, real traffic navigation is still considered to
be a particularly challenging problem because of the time-varying patterns of
the traffic flow and unpredictable accidents/congestion. To give accurate and
reliable navigation results, predicting the future traffic
flow(speed,congestion,volume,etc) in a fast and accurate way is of great
importance. In this paper, we adopt the ideas of ensemble learning and develop
a two-stage machine learning model to give accurate navigation results. We
model the traffic flow as a time series and apply XGBoost algorithm to get
accurate predictions on future traffic conditions(1st stage). We then apply the
Top K Dijkstra algorithm to find a set of shortest paths from the give start
point to the destination as the candidates of the output optimal path. With the
prediction results in the 1st stage, we find one optimal path from the
candidates as the output of the navigation algorithm. We show that our
navigation algorithm can be greatly improved via EOPF(Enhanced Optimal Path
Finding), which is based on neural network(2nd stage). We show that our method
can be over 7% better than the method without EOPF in many situations, which
indicates the effectiveness of our model.
