Comments (4)
Hi kyungeuuun,
The distance between two locations/nodes are calculated using A* search on the road network. A map is needed for this calculation.
Besides, the discrepancy of the distance may because of the following two aspects:
- The provided is based on a map is pretty old (e.g., 2012), thus there might be road closure.
- The error in mapmatching, i.e., mapping a point to a node/edge on the road network.
You may the Euclidean distance between two nodes as a starting point.
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Imagine we have one sensor on a northbound highway i
and one on a southbound highway j
at roughly the same exit. Would the "road network distance" for i,j
be how long it takes to get off the highway, get back on the highway and drive south to the other sensor (in meters)?
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Hi @sshleifer , the road network distance is shortest distance the vehicle has to travel from the source to the destination under the constraint imposed by the network. If
get off the highway, get back on the highway and drive south to the other sensor
is the shortest distance, it will be the case.
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Here's how I created my own distance matrix: https://github.com/ThomasAFink/osmnx_adjacency_matrix_for_graph_convolutional_networks
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Related Issues (20)
- About the graph of the paper
- About the graph of the paper
- Why are some speed data negative?
- scaler transform in load_dataset function Causing speed data negative
- sensors correlations or node interactions and how to interpret the model's output
- Input 'b' of 'SparseTensorDenseMatMul' Op has type float32 that does not match type float64 of argument 'a_values'. HOT 2
- Wrong sensor IDs for MetrLA? HOT 8
- Predictions near mean value
- 关于数据的输入问题
- train.py
- Sensor id and data series
- Result Charts - One Example Sensor or Mean of the entire dataset
- Tensorflow 2 for DCRNN models HOT 2
- reproduce results HOT 13
- nothing HOT 1
- A question about the code HOT 1
- Diffusion convolution is not found in code
- A question about changing predicting time interval
- How to train model use different dataset
- isolated nodes
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