"""Map flows on national networks
Purpose
-------
Mapping the OD node level matrix values to network paths
For all transport modes at national scale: ['road', 'rail', 'air', 'inland', 'coastal']
The code estimates 2 values - A MIN and a MAX value of flows between each selected OD node pair
- Based on MIN-MAX generalised costs estimates
Input data requirements
-----------------------
1. Correct paths to all files and correct input parameters
2. Excel file with mode sheets containing network graph structure and attributes
- edge_id - String Edge ID
- from_node - String node ID that should be present in node_id column
- to_node - String node ID that should be present in node_id column
- length - Float length of edge in km
- min_time - Float minimum time of travel in hours on edge
- max_time - Float maximum time of travel in hours on edge
- min_time_cost - Float minimum cost of time in USD on edge
- max_time_cost - Float maximum cost of time in USD on edge
- min_tariff_cost - Float minimum tariff cost in USD on edge
- max_tariff_cost - Float maximum tariff cost in USD on edge
3. Edge shapefiles for all national-scale networks with attributes:
- edge_id - String Edge ID
- geometry - Shapely LineString geometry of edges
4. Excel file with mode sheets containing node-level OD values with attributes:
- origin - String node ID of Origin
- destination - String node ID of Destination
- min_tons - Float values of minimum daily OD in tons
- max_tons - Float values of maximum daily OD in tons
- Names of the industry columns specified in the inputs
Results
-------
1. Excel sheets with results of flow mapping based on MIN-MAX generalised costs estimates:
- origin - String node ID of Origin
- destination - String node ID of Destination
- o_region - String name of Province of Origin node ID
- d_region - String name of Province of Destination node ID
- min_edge_path - List of string of edge ID's for paths with minimum generalised cost flows
- max_edge_path - List of string of edge ID's for paths with maximum generalised cost flows
- min_distance - Float values of estimated distance for paths with minimum generalised cost flows
- max_distance - Float values of estimated distance for paths with maximum generalised cost flows
- min_time - Float values of estimated time for paths with minimum generalised cost flows
- max_time - Float values of estimated time for paths with maximum generalised cost flows
- min_gcost - Float values of estimated generalised cost for paths with minimum generalised cost flows
- max_gcost - Float values of estimated generalised cost for paths with maximum generalised cost flows
- min_vehicle_nums - Float values of estimated vehicle numbers for paths with minimum generalised cost flows
- max_vehicle_nums - Float values of estimated vehicle numbers for paths with maximum generalised cost flows
- industry_columns - All daily tonnages of industry columns given in the OD matrix data
2. Shapefiles
- edge_id - String/Integer/Float Edge ID
- geometry - Shapely LineString geomtry of edges
- min_{industry} - Float values of estimated minimum daily industries/commodities/total volumes in tons on edges
- max_{industry} - Float values of estimated maximum daily industries/commodities/total volumes in tons on edges
References
----------
1. Pant, R., Koks, E.E., Russell, T., Schoenmakers, R. & Hall, J.W. (2018).
Analysis and development of model for addressing climate change/disaster risks in multi-modal transport networks in Vietnam.
Final Report, Oxford Infrastructure Analytics Ltd., Oxford, UK.
2. All input data folders and files referred to in the code below.
"""
import os
import subprocess
import sys
import copy
import geopandas as gpd
import igraph as ig
import numpy as np
import pandas as pd
from shapely import wkt
from scipy.spatial import Voronoi
from shapely.geometry import Point, Polygon
from vtra.transport_flow_and_failure_functions import *
from vtra.utils import *
[docs]def network_od_paths_assembly_national(points_dataframe, graph, vehicle_wt, transport_mode,
excel_writer=''):
"""Assemble estimates of OD paths, distances, times, costs and tonnages on networks
Parameters
----------
points_dataframe : pandas.DataFrame
OD nodes and their tonnages
graph
igraph network structure
vehicle_wt : float
unit weight of vehicle
transport_mode : str
name of modes
excel_writer
Name of the excel writer to save Pandas dataframe to Excel file
Returns
-------
save_paths_df : pandas.DataFrame
- origin - String node ID of Origin
- destination - String node ID of Destination
- min_edge_path - List of string of edge ID's for paths with minimum generalised cost flows
- max_edge_path - List of string of edge ID's for paths with maximum generalised cost flows
- min_distance - Float values of estimated distance for paths with minimum generalised cost flows
- max_distance - Float values of estimated distance for paths with maximum generalised cost flows
- min_time - Float values of estimated time for paths with minimum generalised cost flows
- max_time - Float values of estimated time for paths with maximum generalised cost flows
- min_gcost - Float values of estimated generalised cost for paths with minimum generalised cost flows
- max_gcost - Float values of estimated generalised cost for paths with maximum generalised cost flows
- min_vehicle_nums - Float values of estimated vehicle numbers for paths with minimum generalised cost flows
- max_vehicle_nums - Float values of estimated vehicle numbers for paths with maximum generalised cost flows
- industry_columns - All tonnages of industry columns given in the OD matrix data
"""
save_paths = []
for iter_, row in points_dataframe.iterrows():
try:
origin = row['origin']
destinations = [row['destination']]
tons = row['min_tons']
get_min_path, get_min_dist, get_min_time, get_min_gcost = network_od_path_estimations(
graph, origin, destinations, tons, vehicle_wt, 'min_gcost', 'min_time')
tons = row['max_tons']
get_max_path, get_max_dist, get_max_time, get_max_gcost = network_od_path_estimations(
graph, origin, destinations, tons, vehicle_wt, 'max_gcost', 'max_time')
save_paths += list(zip([origin]*len(destinations), destinations, get_min_path, get_max_path,
get_min_dist, get_max_dist, get_min_time, get_max_time, get_min_gcost, get_max_gcost))
print("done with {} in network {}".format(origin, transport_mode))
except:
print('* no path between {}-{}'.format(origin,destinations))
cols = ['origin', 'destination', 'min_edge_path', 'max_edge_path',
'min_distance', 'max_distance', 'min_time', 'max_time', 'min_gcost', 'max_gcost']
save_paths_df = pd.DataFrame(save_paths, columns=cols)
points_dataframe = points_dataframe.reset_index()
save_paths_df = pd.merge(save_paths_df, points_dataframe, how='left', on=[
'origin', 'destination']).fillna(0)
save_paths_df = save_paths_df[(save_paths_df['max_tons'] > 0)
& (save_paths_df['origin'] != 0)]
if transport_mode != 'air':
save_paths_df['min_vehicle_nums'] = np.maximum(
1, np.ceil(save_paths_df['min_tons']/vehicle_wt))
save_paths_df['max_vehicle_nums'] = np.maximum(
1, np.ceil(save_paths_df['max_tons']/vehicle_wt))
save_paths_df.to_excel(excel_writer, transport_mode, index=False)
excel_writer.save()
del save_paths
return save_paths_df
[docs]def main():
"""Estimate flows
1. Specify the paths from where you want to read and write:
- Input data
- Intermediate calcuations data
- Output results
2. Supply input data and parameters
- Names of modes: List of strings
- Unit weight of vehicle assumed for each mode: List of float types
- Names of all industry sector and crops in VITRANSS2 and IFPRI datasets: List of string types
- Names of commodity/industry columns for which min-max tonnage column names already exist: List of string types
- Percentage of OD flow we want to send along path: FLoat type
3. Give the paths to the input data files:
- Network edges Excel file
- OD flows Excel file
- Costs of modes Excel file
- Road properties Excel file
4. Specify the output files and paths to be created
"""
data_path, calc_path, output_path = load_config()['paths']['data'], load_config()[
'paths']['calc'], load_config()['paths']['output']
# Supply input data and parameters
modes = ['road', 'rail', 'air', 'inland', 'coastal']
veh_wt = [20, 800, 0, 800, 1200]
ind_cols = ['cement', 'coal', 'constructi', 'fertilizer', 'fishery', 'manufactur', 'acof', 'cash', 'cass', 'maiz', 'pepp', 'rcof',
'rubb', 'swpo', 'teas', 'meat', 'rice', 'petroluem', 'steel', 'sugar', 'wood', 'tons']
min_max_exist = ['rice','tons']
percentage = [10,90,100]
# Give the paths to the input data files
network_data_path = os.path.join(data_path,'post_processed_networks')
network_data_excel = os.path.join(data_path,'post_processed_networks','national_edges.xlsx')
od_output_excel = os.path.join(
output_path, 'flow_ods', 'national_scale_flow_ods.xlsx')
md_prop_file = os.path.join(data_path, 'mode_properties', 'mode_costs.xlsx')
rd_prop_file = os.path.join(data_path, 'mode_properties', 'road_properties.xlsx')
# Specify the output files and paths to be created
flow_shp_dir = os.path.join(output_path, 'flow_mapping_shapefiles')
if os.path.exists(flow_shp_dir) == False:
os.mkdir(flow_shp_dir)
flow_csv_dir = os.path.join(output_path, 'flow_mapping_combined')
if os.path.exists(flow_csv_dir) == False:
os.mkdir(flow_csv_dir)
flow_paths_dir = os.path.join(output_path, 'flow_mapping_paths')
if os.path.exists(flow_paths_dir) == False:
os.mkdir(flow_paths_dir)
for perct in percentage:
flow_output_excel = os.path.join(
flow_paths_dir,
'national_scale_flow_paths_{}_percent.xlsx'.format(int(perct)))
excl_wrtr = pd.ExcelWriter(flow_output_excel)
# Start the OD flow mapping process
for m in range(len(modes)):
# Load mode igraph network and GeoDataFrame
print ('* Loading {} igraph network and GeoDataFrame'.format(modes[m]))
edges_in = pd.read_excel(network_data_excel,sheet_name = modes[m],encoding='utf-8')
G = ig.Graph.TupleList(edges_in.itertuples(index=False), edge_attrs=list(edges_in.columns)[2:])
del edges_in
gdf_edges = gpd.read_file(os.path.join(network_data_path,'{}_edges.shp'.format(modes[m])),encoding='utf-8')
gdf_edges = gdf_edges[['edge_id','geometry']]
# Load mode OD nodes pairs and tonnages
print ('* Loading {} OD nodes pairs and tonnages'.format(modes[m]))
ods = pd.read_excel(od_output_excel, sheet_name=modes[m])
ods = ods[ods['max_tons'] > 0.5]
all_ods = copy.deepcopy(ods)
all_ods_tons_cols = [col for col in all_ods.columns.values.tolist() if col not in ['origin','o_region','destination','d_region']]
all_ods[all_ods_tons_cols] = 0.01*perct*all_ods[all_ods_tons_cols]
# Calculate mode OD paths
print ('* Calculating {} OD paths'.format(modes[m]))
all_paths = network_od_paths_assembly_national(
all_ods, G, veh_wt[m], modes[m],excel_writer=excl_wrtr)
del all_ods
# Create network shapefiles with flows
print ('* Creating {} network shapefiles with flows'.format(modes[m]))
shp_output_path = os.path.join(flow_shp_dir,'weighted_flows_national_{}_{}_percent.shp'.format(modes[m],int(perct)))
csv_output_path = os.path.join(flow_csv_dir,'weighted_flows_national_{}_{}_percent.csv'.format(modes[m],int(perct)))
write_flow_paths_to_network_files(all_paths,
ind_cols,min_max_exist,gdf_edges,
save_csv=True, save_shapes=False, shape_output_path=shp_output_path,csv_output_path=csv_output_path)
if __name__ == '__main__':
main()