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create_tms.py
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create_tms.py
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import numpy as np
import pandas as pd
import os
import networkx as nx
from create_graph import get_geographical_nx_graph_from_json
from src.constants import Constants
def simulate_bimodal_traffic(G, num_intervals, K, output_dir):
nodes = list(G.nodes())
num_nodes = len(nodes)
for interval in range(num_intervals):
traffic_matrix = np.zeros((num_nodes, num_nodes))
for i in range(num_nodes):
for j in range(num_nodes):
if i != j:
s = np.random.uniform(0, 1)
if s > 0.8:
traffic_matrix[i, j] = np.random.normal(400, 100)
else:
traffic_matrix[i, j] = np.random.normal(800, 100)
df = pd.DataFrame(traffic_matrix, index=nodes, columns=nodes)
file_path = os.path.join(output_dir, f"bimodal_tm_{interval}.dat")
df.to_csv(file_path, sep=',', header=False, index=False)
def simulate_gravity_traffic(G, num_intervals, K, output_dir, cyclical=True, q=5):
nodes = list(G.nodes())
num_nodes = len(nodes)
def gravity_demand():
traffic_matrix = np.zeros((num_nodes, num_nodes))
for i in range(num_nodes):
for j in range(num_nodes):
if i != j:
Mi = sum([G[nodes[i]][k]['bw'] for k in G[nodes[i]]])
Mj = sum([G[k][nodes[j]]['bw'] for k in G[nodes[j]]])
Dij = G[nodes[i]][nodes[j]]['dist'] if G.has_edge(nodes[i], nodes[j]) else 1
traffic_matrix[i, j] = K * Mi * Mj / (Dij ** 2)
return traffic_matrix
if cyclical:
base_matrices = [gravity_demand() for _ in range(q)]
for interval in range(num_intervals):
traffic_matrix = base_matrices[interval % q]
df = pd.DataFrame(traffic_matrix, index=nodes, columns=nodes)
file_path = os.path.join(output_dir, f"gravity_cyclical_tm_{interval}.dat")
df.to_csv(file_path, sep=',', header=False, index=False)
else:
traffic_matrices = [gravity_demand() for _ in range(num_intervals)]
for interval in range(num_intervals):
avg_matrix = np.mean(traffic_matrices[max(0, interval - q + 1):interval + 1], axis=0)
df = pd.DataFrame(avg_matrix, index=nodes, columns=nodes)
file_path = os.path.join(output_dir, f"gravity_averaging_tm_{interval}.dat")
df.to_csv(file_path, sep=',', header=False, index=False)
def sparsify_traffic_matrix(matrix, p):
sparsified_matrix = np.copy(matrix)
num_nodes = matrix.shape[0]
for i in range(num_nodes):
for j in range(num_nodes):
if np.random.uniform(0, 1) > p:
sparsified_matrix[i, j] = 0
return sparsified_matrix
def simulate_sparsified_gravity_traffic(G, num_intervals, K, output_dir, p, cyclical=True, q=5):
nodes = list(G.nodes())
num_nodes = len(nodes)
def gravity_demand():
traffic_matrix = np.zeros((num_nodes, num_nodes))
for i in range(num_nodes):
for j in range(num_nodes):
if i != j:
Mi = sum([G[nodes[i]][k]['bw'] for k in G[nodes[i]]])
Mj = sum([G[k][nodes[j]]['bw'] for k in G[nodes[j]]])
Dij = G[nodes[i]][nodes[j]]['dist'] if G.has_edge(nodes[i], nodes[j]) else 1
traffic_matrix[i, j] = K * Mi * Mj / (Dij ** 2)
return traffic_matrix
if cyclical:
base_matrices = [sparsify_traffic_matrix(gravity_demand(), p) for _ in range(q)]
for interval in range(num_intervals):
traffic_matrix = base_matrices[interval % q]
df = pd.DataFrame(traffic_matrix, index=nodes, columns=nodes)
file_path = os.path.join(output_dir, f"sparsified_gravity_cyclical_tm_{interval}.dat")
df.to_csv(file_path, sep=',', header=False, index=False)
else:
traffic_matrices = [sparsify_traffic_matrix(gravity_demand(), p) for _ in range(num_intervals)]
for interval in range(num_intervals):
avg_matrix = np.mean(traffic_matrices[max(0, interval - q + 1):interval + 1], axis=0)
df = pd.DataFrame(avg_matrix, index=nodes, columns=nodes)
file_path = os.path.join(output_dir, f"sparsified_gravity_averaging_tm_{interval}.dat")
df.to_csv(file_path, sep=',', header=False, index=False)
# Initializes graph structure
G = get_geographical_nx_graph_from_json(Constants.geographical_data_filename)
# Sets output directory
output_dir = "./traffic_matrices"
os.makedirs(output_dir, exist_ok=True)
# Create one month's worth of TMs with cycles of 7 days
simulate_bimodal_traffic(G, 8640, 1, output_dir)
simulate_gravity_traffic(G, 8640, 1, output_dir, cyclical=True, q=2016)
simulate_gravity_traffic(G, 8640, 1, output_dir, cyclical=False, q=2016)
simulate_sparsified_gravity_traffic(G, 8640, 1, output_dir, p=0.5, cyclical=True, q=2016)
simulate_sparsified_gravity_traffic(G, 8640, 1, output_dir, p=0.5, cyclical=False, q=2016)