road disruption and damage analysis

scripts/network_flow_model_disruption.py

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+import os
+
+from pathlib import Path
+import time
+import pandas as pd
+import geopandas as gpd  # type: ignore
+
+from nird.utils import load_config
+import nird.road as func
+
+
+import json
+import warnings
+
+warnings.simplefilter("ignore")
+
+base_path = Path(load_config()["paths"]["base_path"])
+disruption_path = base_path / "disruption_analysis"
+
+
+def network_flow_model(max_flow_speed_dict, event_key):
+    start_time = time.time()
+
+    # model parameters
+    with open(base_path / "parameters" / "flow_breakpoint_dict.json", "r") as f:
+        flow_breakpoint_dict = json.load(f)
+    with open(base_path / "parameters" / "flow_cap_dict.json", "r") as f:
+        flow_capacity_dict = json.load(f)
+    with open(base_path / "parameters" / "free_flow_speed_dict.json", "r") as f:
+        free_flow_speed_dict = json.load(f)
+    with open(base_path / "parameters" / "min_speed_cap.json", "r") as f:
+        min_speed_dict = json.load(f)
+    with open(base_path / "parameters" / "urban_speed_cap.json", "r") as f:
+        urban_speed_dict = json.load(f)
+
+    # road networks (urban_filter: mannual correction)
+    road_node_file = gpd.read_parquet(
+        base_path / "networks" / "road" / "road_node_file.geoparquet"
+    )
+    road_link_file = gpd.read_parquet(
+        base_path / "networks" / "road" / "road_link_file.geoparquet"
+    )
+
+    # O-D matrix (2021)
+    od_node_2021 = pd.read_csv(
+        base_path / "census_datasets" / "od_matrix" / "od_gb_oa_2021_node.csv"
+    )
+    od_node_2021 = od_node_2021[od_node_2021.Car21 > 1].reset_index(drop=True)
+    od_node_2021["Car21"] = od_node_2021["Car21"] * 2
+    od_node_2021 = od_node_2021.head(10)  #!!! for test
+    print(f"total flows: {od_node_2021.Car21.sum()}")
+
+    # generate OD pairs
+    list_of_origin_nodes, dict_of_destination_nodes, dict_of_origin_supplies = (
+        func.extract_od_pairs(od_node_2021)
+    )
+
+    # flow simulation
+    (
+        road_link_file,  # edge flow results
+        od_node_2021,  # edge flow validation & component costs
+        isolated_od_dict,  # isolated trips
+    ) = func.network_flow_model(
+        road_link_file,  # road
+        road_node_file,  # road
+        list_of_origin_nodes,  # od
+        dict_of_origin_supplies,  # od
+        dict_of_destination_nodes,  # od
+        free_flow_speed_dict,  # net
+        flow_breakpoint_dict,  # net
+        flow_capacity_dict,  # net
+        min_speed_dict,  # net
+        urban_speed_dict,  # net
+        od_node_2021,  # od_flow_matrix
+        max_flow_speed_dict,  # disruption analysis
+    )
+
+    # export files
+    road_link_file.to_parquet(
+        base_path.parent / "outputs" / f"gb_edge_flows_{event_key}.geoparquet"
+    )
+    od_node_2021.to_csv(
+        base_path.parent / "outputs" / f"od_costs_{event_key}.csv", index=False
+    )
+    isolated_od_df = pd.Series(isolated_od_dict).reset_index()
+    if isolated_od_df.shape[0] != 0:  # in case of empty df
+        isolated_od_df.columns = ["origin_node", "destination_node", "isolated_flows"]
+        isolated_od_df.to_csv(
+            base_path.parent / "outputs" / f"isolated_od_flows_{event_key}.csv",
+            index=False,
+        )
+    print(f"The network flow model is completed for {event_key}!")
+    print(f"The total simulation time: {time.time() - start_time}. ")
+
+
+def main():
+    flood_event_paths = []
+    for root, _, files in os.walk(disruption_path):
+        for file in files:
+            file_path = os.path.join(root, file)
+            flood_event_paths.append(file_path)
+    for event_path in flood_event_paths:
+        event_key = event_path.split("\\")[-1].split(".")[0]
+        flooded_road_max_speed = pd.read_csv(event_path)
+        # maximum vehicle speeds (mph) -> flooded roads (id/e_id)
+        max_flow_speed_dict = flooded_road_max_speed.set_index("id")[
+            "max_speed_mph_adjusted"
+        ].to_dict()
+        network_flow_model(max_flow_speed_dict, event_key)
+
+
+if __name__ == "__main__":
+    main()

scripts/network_flow_model_distributed.py

@@ -0,0 +1,133 @@
+# %%
+import os
+import sys
+import math
+import itertools
+
+from pathlib import Path
+import time
+import pandas as pd
+import geopandas as gpd  # type: ignore
+
+from nird.utils import load_config
+import nird.road as func
+
+
+import json
+import warnings
+
+warnings.simplefilter("ignore")
+
+base_path = Path(load_config()["paths"]["base_path"])
+disruption_path = Path(load_config()["paths"]["disruption_path"])
+# %%
+
+
+def network_flow_model(max_flow_speed_dict, event_key):
+    start_time = time.time()
+
+    # model parameters
+    with open(base_path / "parameters" / "flow_breakpoint_dict.json", "r") as f:
+        flow_breakpoint_dict = json.load(f)
+    with open(base_path / "parameters" / "flow_cap_dict.json", "r") as f:
+        flow_capacity_dict = json.load(f)
+    with open(base_path / "parameters" / "free_flow_speed_dict.json", "r") as f:
+        free_flow_speed_dict = json.load(f)
+    with open(base_path / "parameters" / "min_speed_cap.json", "r") as f:
+        min_speed_dict = json.load(f)
+    with open(base_path / "parameters" / "urban_speed_cap.json", "r") as f:
+        urban_speed_dict = json.load(f)
+
+    # road networks (urban_filter: mannual correction)
+    road_node_file = gpd.read_parquet(
+        base_path / "networks" / "road" / "road_node_file.geoparquet"
+    )
+    road_link_file = gpd.read_parquet(
+        base_path / "networks" / "road" / "road_link_file.geoparquet"
+    )
+
+    # O-D matrix (2021)
+    od_node_2021 = pd.read_csv(
+        base_path / "census_datasets" / "od_matrix" / "od_gb_oa_2021_node.csv"
+    )
+    # od_node_2021 = od_node_2021[od_node_2021.Car21 > 1].reset_index(drop=True)
+    od_node_2021["Car21"] = od_node_2021["Car21"] * 2
+    # od_node_2021 = od_node_2021.head(10)  #!!! for test
+    print(f"total flows: {od_node_2021.Car21.sum()}")
+
+    # generate OD pairs
+    list_of_origin_nodes, dict_of_destination_nodes, dict_of_origin_supplies = (
+        func.extract_od_pairs(od_node_2021)
+    )
+
+    # flow simulation
+    (
+        road_link_file,  # edge flow results
+        od_node_2021,  # edge flow validation & component costs
+        isolated_od_dict,  # isolated trips
+    ) = func.network_flow_model(
+        road_link_file,  # road
+        road_node_file,  # road
+        list_of_origin_nodes,  # od
+        dict_of_origin_supplies,  # od
+        dict_of_destination_nodes,  # od
+        free_flow_speed_dict,  # net
+        flow_breakpoint_dict,  # net
+        flow_capacity_dict,  # net
+        min_speed_dict,  # net
+        urban_speed_dict,  # net
+        od_node_2021,  # od_flow_matrix
+        max_flow_speed_dict,  # disruption analysis
+    )
+
+    # export files
+    road_link_file.to_parquet(
+        base_path.parent / "outputs" / f"gb_edge_flows_{event_key}.geoparquet"
+    )
+    od_node_2021.to_csv(
+        base_path.parent / "outputs" / f"od_costs_{event_key}.csv", index=False
+    )
+    isolated_od_df = pd.Series(isolated_od_dict).reset_index()
+    if isolated_od_df.shape[0] != 0:  # in case of empty df
+        isolated_od_df.columns = ["origin_node", "destination_node", "isolated_flows"]
+        isolated_od_df.to_csv(
+            base_path.parent / "outputs" / f"isolated_od_flows_{event_key}.csv",
+            index=False,
+        )
+    print(f"The network flow model is completed for {event_key}!")
+    print(f"The total simulation time: {time.time() - start_time}. ")
+
+
+def main(task_id: int, task_count: int):
+    flood_event_paths = []
+    for root, _, files in os.walk(disruption_path):
+        for file in files:
+            file_path = Path(root) / file
+            flood_event_paths.append(file_path)
+
+    n_events = len(flood_event_paths)
+    event_per_task = math.ceil(n_events / task_count)
+    event_batches = list(itertools.batched(flood_event_paths, event_per_task))
+    try:
+        event_paths_to_run = event_batches[task_id]
+    except IndexError:
+        print(f"No events for {task_id=} {task_count=} {n_events=}")
+        sys.exit()
+
+    for event_path in event_paths_to_run:
+        event_key = event_path.stem
+        flooded_road_max_speed = pd.read_csv(event_path)
+        # maximum vehicle speeds (mph) -> flooded roads (id/e_id)
+        max_flow_speed_dict = flooded_road_max_speed.set_index("id")[
+            "max_speed_mph_adjusted"
+        ].to_dict()
+        network_flow_model(max_flow_speed_dict, event_key)
+
+
+if __name__ == "__main__":
+    try:
+        task_id = int(sys.argv[1])
+        task_count = int(sys.argv[2])
+    except IndexError:
+        print(f"Usage: python {__file__} <task_id> <task_count>")
+    main(task_id, task_count)