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Multi-Sensor Data Fusion for Accurate Traffic Speed and Travel Time Reconstruction
A novel fusion approach is developed which extends existing speed reconstruction methods to integrate low-resolution travel time data.
Estimating Traffic Speeds using Probe Data: A Deep Neural Network Approach
The results show that the DNN is able to apply learned patterns, and reconstructs moving as well as stationary congested traffic with high accuracy; even given highly sparse input data.
Feature Engineering for Data-driven Traffic State Forecast in Urban Road Networks
Most traffic state forecast algorithms when applied to urban road networks consider only the links in close proximity to the target location.
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