Fig 1.
RBM Structure.
Fig 2.
State Space Neural Network Structure.
Fig 3.
BackPropagation Through Time for RNN.
Fig 4.
RBM-RNN Architecture.
Table 1.
Comparison of traffic congestion prediction performance with different data aggregation levels.
Fig 5.
Training Accuracy Changing Curves with Different Data Aggregation Levels.
Fig 6.
Predicted Network Congestion Evolution Patterns on May 09, 2014 with Varying Times of Day.
(a) Spatial Distribution of Congestion from 5AM to 6AM; (b) Spatial Distribution of Congestion from 9AM to 10AM; (c) Spatial Distribution of Congestion from 5PM to 6PM; (d) Spatial Distribution of Congestion from 11PM to 12PM (Red line indicates congested traffic condition; green line indicated uncongested traffic condition).
Table 2.
Statistics for number of congested links on May 9, 2014.
Fig 7.
Temporal distribution for number of congested links on May 9, 2014.
Table 3.
Comparison of traffic congestion prediction performance for different algorithms with 60-minute data aggregation level.
Table 4.
Sensitivity analysis of congestion evolution prediction performance with various speed thresholds.