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Found 5 papers, 2 papers with code
UniTE -- The Best of Both Worlds: Unifying Function-Fitting and Aggregation-Based Approaches to Travel Time and Travel Speed Estimation
An empirical study finds that an instance of UniTE can improve the accuracies of travel speed distribution and travel time estimation by $40-64\%$ and $3-23\%$, respectively, compared to using function fitting or aggregation alone
Scalable Unsupervised Multi-Criteria Trajectory Segmentation and Driving Preference Mining
We present analysis techniques for large trajectory data sets that aim to provide a semantic understanding of trajectories reaching beyond them being point sequences in time and space.
Relational Fusion Networks: Graph Convolutional Networks for Road Networks
The application of machine learning techniques in the setting of road networks holds the potential to facilitate many important intelligent transportation applications.
On Network Embedding for Machine Learning on Road Networks: A Case Study on the Danish Road Network
This is problematic for analysis tasks that rely on such information for machine learning.
Graph Convolutional Networks for Road Networks
In addition, we provide experimental evidence of the short-comings of state-of-the-art GCNs in the context of road networks: unlike our method, they cannot effectively leverage the road network structure for road segment classification and fail to outperform a regular multi-layer perceptron.
