Losses

Rerouting losses compare initial allocated paths with disrupted allocated paths for each OD pair. The loss value is the difference between summed disrupted cost and summed initial cost.

Grouped path costs

compute_losses groups rows by origin_id and destination_id. This is useful when one OD pair has multiple allocated rows.

>>> import pandas as pd
>>> from transport_flow_model.model import ODFlows, compute_losses
>>> initial = ODFlows(
...     pd.DataFrame(
...         {
...             "origin_id": ["A", "A", "B"],
...             "destination_id": ["C", "C", "D"],
...             "flow": [4, 6, 2],
...             "edge_path": [["AC1"], ["AC2"], ["BD"]],
...             "cost": [2, 3, 7],
...         }
...     )
... )
>>> disrupted = ODFlows(
...     pd.DataFrame(
...         {
...             "origin_id": ["A", "A", "B"],
...             "destination_id": ["C", "C", "D"],
...             "flow": [4, 6, 2],
...             "edge_path": [["AC1"], ["AE", "EC"], ["BE", "ED"]],
...             "cost": [2, 5, 12],
...         }
...     )
... )
>>> losses = compute_losses(initial, disrupted)
>>> losses.to_dataframe().sort_values(
...     ["origin_id", "destination_id"]
... ).to_dict("records")
[{'origin_id': 'A', 'destination_id': 'C', 'flow': 10, 'initial_cost': 5, 'disrupted_cost': 7, 'rerouting_loss': 2}, {'origin_id': 'B', 'destination_id': 'D', 'flow': 2, 'initial_cost': 7, 'disrupted_cost': 12, 'rerouting_loss': 5}]

For A -> C, the initial costs 2 and 3 sum to 5. The disrupted costs 2 and 5 sum to 7, so rerouting_loss is 2.