RA2CE feature: Single link losses analysis#
Single link losses Analysis#
[1]:
from pathlib import Path
from ra2ce.ra2ce_handler import Ra2ceHandler
import geopandas as gpd
# Import config data
root_dir = Path("data", "single_link_losses")
assert root_dir.exists()
# Load network data.
from ra2ce.network.network_config_data.network_config_data_reader import NetworkConfigDataReader
_network_config_data = NetworkConfigDataReader().read(root_dir.joinpath("network.ini"))
# Load analysis data.
from ra2ce.analysis.analysis_config_data.analysis_config_data_reader import AnalysisConfigDataReader
_analysis_config_data = AnalysisConfigDataReader().read(root_dir.joinpath("analysis.ini"))
c:\Users\hauth\anaconda3\envs\ra2ce_env\lib\site-packages\tqdm\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
from .autonotebook import tqdm as notebook_tqdm
Modify the analysis config data with custom properties#
[2]:
from ra2ce.analysis.analysis_config_data.analysis_config_data import AnalysisSectionLosses
# Modify the first analysis (assuming it's the only one)
_found_analysis: AnalysisSectionLosses = _analysis_config_data.analyses[0]
_input_analysis_path = root_dir.joinpath("input_analysis_data")
_found_analysis.traffic_intensities_file = _input_analysis_path.joinpath("traffic_intensities.csv")
_found_analysis.resilience_curves_file = _input_analysis_path.joinpath("resilience_curve.csv")
_found_analysis.values_of_time_file = _input_analysis_path.joinpath("values_of_time.csv")
Configure network#
Let’s have a look first at the hazard overlay to locate the road segments for which losses will be expected. We run only the network configuration.
[3]:
handler = Ra2ceHandler.from_config(_network_config_data, _analysis_config_data)
handler.configure()
[4]:
hazard_output = root_dir / "static" / "output_graph" / "base_graph_hazard_edges.gpkg"
hazard_gdf = gpd.read_file(hazard_output, driver = "GPKG")
hazard_gdf.explore(column="EV1_ma", cmap = "viridis")
[4]:
Make this Notebook Trusted to load map: File -> Trust Notebook
Run losses analysis#
[17]:
handler.run_analysis()
c:\Users\hauth\anaconda3\envs\ra2ce_env\lib\site-packages\geopandas\geodataframe.py:1543: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
super().__setitem__(key, value)
[17]:
[AnalysisResultWrapper(analysis_result= ID u v key EV1_fr EV1_ma \
0 86 2 42 0 0.014318 1.369213
1 30 29 31 0 0.079975 0.345044
2 100 30 82 0 0.100808 0.654458
3 85 42 73 0 0.027676 0.409945
4 264 46 51 0 0.002634 0.766717
.. ... ... ... ... ... ...
225 253 153 156 0 0.000000 0.000000
226 246 154 155 0 0.000000 0.000000
227 249 154 156 0 0.000000 0.000000
228 248 156 157 0 0.000000 0.000000
229 257 157 158 0 0.000000 0.000000
alt_nodes alt_time avgspeed \
0 [2, 0, 11, 12, 10, 7, 6, 9, 75, 121, 118, 5, 3... 0.099722 59.0
1 [29, 30, 82, 31] 0.009356 59.0
2 [30, 29, 31, 82] 0.005288 30.0
3 nan NaN 60.0
4 [46, 48, 47, 160, 51] 0.008829 60.0
.. ... ... ...
225 [153, 155, 154, 156] 0.000933 60.0
226 [154, 156, 153, 155] 0.000933 60.0
227 [154, 155, 153, 156] 0.001217 60.0
228 [156, 154, 89, 157] 0.003383 60.0
229 nan NaN 60.0
bridge ... node_A node_B rfid rfid_c time vlh_business_EV1_ma \
0 yes ... 2 42 125 127 0.043763 433.125000
1 yes ... 29 31 91 93 0.003898 5.156250
2 yes ... 30 82 233 241 0.007967 -40.781250
3 yes ... 42 73 208 216 0.016917 46.375000
4 nan ... 46 51 151 153 0.002183 39.785156
.. ... ... ... ... ... ... ... ...
225 nan ... 153 156 448 458 0.000500 0.000000
226 nan ... 154 155 447 457 0.000500 0.000000
227 nan ... 154 156 449 459 0.000217 0.000000
228 nan ... 156 157 452 462 0.002950 0.000000
229 nan ... 157 158 454 464 0.025067 0.000000
vlh_commute_EV1_ma vlh_freight_EV1_ma vlh_other_EV1_ma vlh_EV1_ma_total
0 303.333333 5775.000000 5133.333333 11644.791667
1 3.750000 5.000000 35.833333 49.739583
2 -18.437500 -81.250000 -106.250000 -246.718750
3 51.187500 24.500000 210.000000 332.062500
4 13.125000 80.208333 69.270833 202.389323
.. ... ... ... ...
225 0.000000 0.000000 0.000000 0.000000
226 0.000000 0.000000 0.000000 0.000000
227 0.000000 0.000000 0.000000 0.000000
228 0.000000 0.000000 0.000000 0.000000
229 0.000000 0.000000 0.000000 0.000000
[230 rows x 27 columns], analysis=<ra2ce.analysis.losses.single_link_losses.SingleLinkLosses object at 0x000001D5ECECAAD0>)]
Let’s inspect the results of the analysis. For every segment in the network, the losses are expressed in vehicle loss hour (vlh) for all the types of trip defined in the input files: business, commute, freight and other.
[18]:
analysis_output_folder = root_dir / "output" / "single_link_losses" # specify path to output folder
losses_gdf = gpd.read_file(analysis_output_folder/"beira_single_losses.gpkg") #specify the name of the geopackage holding your results (can be found in the analysis output folder)
losses_gdf.head() #display the attributes of the file
[18]:
| ID | u | v | key | EV1_fr | EV1_ma | alt_nodes | alt_time | avgspeed | bridge | ... | node_B | rfid | rfid_c | time | vlh_business_EV1_ma | vlh_commute_EV1_ma | vlh_freight_EV1_ma | vlh_other_EV1_ma | vlh_EV1_ma_total | geometry | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 86 | 2 | 42 | 0 | 0.014318 | 1.369213 | [2, 0, 11, 12, 10, 7, 6, 9, 75, 121, 118, 5, 3... | 0.099722 | 59.0 | yes | ... | 42 | 125 | 127 | 0.043763 | 433.125000 | 303.333333 | 5775.000000 | 5133.333333 | 11644.791667 | LINESTRING (34.86702 -19.83164, 34.86711 -19.8... |
| 1 | 30 | 29 | 31 | 0 | 0.079975 | 0.345044 | [29, 30, 82, 31] | 0.009356 | 59.0 | yes | ... | 31 | 91 | 93 | 0.003898 | 5.156250 | 3.750000 | 5.000000 | 35.833333 | 49.739583 | LINESTRING (34.83997 -19.83592, 34.84019 -19.8... |
| 2 | 100 | 30 | 82 | 0 | 0.100808 | 0.654458 | [30, 29, 31, 82] | 0.005288 | 30.0 | yes | ... | 82 | 233 | 241 | 0.007967 | -40.781250 | -18.437500 | -81.250000 | -106.250000 | -246.718750 | LINESTRING (34.84149 -19.83431, 34.84137 -19.8... |
| 3 | 85 | 42 | 73 | 0 | 0.027676 | 0.409945 | nan | NaN | 60.0 | yes | ... | 73 | 208 | 216 | 0.016917 | 46.375000 | 51.187500 | 24.500000 | 210.000000 | 332.062500 | LINESTRING (34.87145 -19.82349, 34.87126 -19.8... |
| 4 | 264 | 46 | 51 | 0 | 0.002634 | 0.766717 | [46, 48, 47, 160, 51] | 0.008829 | 60.0 | nan | ... | 51 | 151 | 153 | 0.002183 | 39.785156 | 13.125000 | 80.208333 | 69.270833 | 202.389323 | LINESTRING (34.85650 -19.83019, 34.85714 -19.8... |
5 rows × 27 columns
The map below displays the total loss for all trip types per link
[19]:
losses_roads_map = losses_gdf.explore(column='vlh_EV1_ma_total', tiles="CartoDB positron",)
display(losses_roads_map)
Make this Notebook Trusted to load map: File -> Trust Notebook
[ ]:
[ ]: