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scripts_chicago.py
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scripts_chicago.py
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import numpy as np
from process_data import extract_features, process_links, geojson_link, \
process_trips, process_net, process_node, array_to_trips, process_results
from metrics import average_cost, cost_ratio, save_metrics
# from frank_wolfe import solver, solver_2, solver_3
# from heterogeneous_solver import gauss_seidel, jacobi
from multi_types_solver import parametric_study
from frank_wolfe_2 import solver, solver_3
from utils import multiply_cognitive_cost
from metrics import OD_routed_costs, OD_non_routed_costs
def process_chicago_network():
'''
prepare .csv files for our algorithm from .txt files available in
http://www.bgu.ac.il/~bargera/tntp/
'''
process_trips('data/ChicagoSketch_trips.txt', 'data/Chicago_od.csv')
process_net('data/ChicagoSketch_net.txt', 'data/Chicago_net.csv')
input = 'data/ChicagoSketch_node.csv'
output = 'data/Chicago_node.csv'
max_X = -87.110063
min_X = -88.9242653
max_Y = 42.711908
min_Y = 40.946233
process_node(input, output, min_X, max_X, min_Y, max_Y)
def load_chicago():
net = np.loadtxt('data/Chicago_net.csv', delimiter=',', skiprows=1)
demand = np.loadtxt('data/Chicago_od.csv', delimiter=',', skiprows=1)
node = np.loadtxt('data/Chicago_node.csv', delimiter=',', skiprows=1)
geometry = extract_features('data/ChicagoSketch_net.txt')
return net, demand, node, geometry
def capacities_of_chicago():
'''
visualize capacities in the network of Chicago
'''
net, demand, node, features = load_chicago()
links = process_links(net, node, features)
color = features[:, 0] / 2000. # we choose the capacity
new_links = []
new_color = []
# remove the high capacity links
for row in range(links.shape[0]):
if features[row, 0] < 49500.:
new_links.append(links[row, :].tolist())
new_color.append(color[row])
color = np.array(new_color)
weight = (color <= 1.0) + 4. * (color > 1.0)
geojson_link(np.array(new_links), ['capacity', 'length', 'fftt'],
color, weight)
def multiply_demand_by_2():
'''
Multiply demand in Chicago by 2 and generate input file for Bar-Gera
'''
demand = np.loadtxt('data/Chicago_od.csv', delimiter=',', skiprows=1)
demand[:, 2] = 2. * demand[:, 2]
array_to_trips(demand, 'data/ChicagoSketch_trips_2.txt')
def results_for_chicago():
'''
Take Bar-gera results and check the link flows
are aligned with Chicago_net.csv
'''
process_results('data/Chicago_raw_results_2.csv',
'data/Chicago_results_2.csv',
'data/Chicago_net.csv')
def frank_wolfe_on_chicago():
'''
Frank-Wolfe on Chicago with the inputs processed from:
http://www.bgu.ac.il/~bargera/tntp/
'''
graph, demand, node, features = load_chicago()
results = np.loadtxt('data/Chicago_results.csv', delimiter=',', skiprows=1)
demand[:, 2] = demand[:, 2] / 4000
f = solver(graph, demand, max_iter=1000, display=1)
# error: 0.00647753330249, time: 664.151s
# f = solver_2(graph, demand, max_iter=1000, q=100, display=1)
# error: 0.00646125552755, time: 664.678s
# f = solver_3(graph, demand, max_iter=1000, q=200, display=1)
# error: 0.00648532089623, time: 665.074s
print np.linalg.norm(f * 4000 - results[:, 2]) / np.linalg.norm(
results[:, 2])
def frank_wolfe_on_chicago_2():
'''
Frank-Wolfe on Chicago with the inputs processed from:
http://www.bgu.ac.il/~bargera/tntp/
but we multiply the demand by 2 to have more congestion
'''
graph, demand, node, features = load_chicago()
results = np.loadtxt('data/Chicago_results_2.csv', delimiter=',')
demand[:, 2] = demand[:, 2] / 2000 # technically, it's 2*demand/4000
# f = solver(graph, demand, max_iter=1000, display=1, stop=1e-2)
# f = solver_2(graph, demand, max_iter=1000, q=100, display=1, stop=1e-2)
f = solver_3(graph, demand, max_iter=1000, q=50, display=1, stop=1e-2)
print np.linalg.norm(f * 4000 - results) / np.linalg.norm(results)
print average_cost(f, graph, demand)
def visualize_equilibrium_in_chicago():
'''
visualize costs in the network of Chicago
'''
net, demand, node, f = load_chicago()
flow = np.loadtxt('data/Chicago_results_2.csv', delimiter=',')
# flow = np.loadtxt(
# 'data/Chicago_results.csv', delimiter=',', skiprows=1)[:,2]
print average_cost(flow / 4000., net, demand)
costs = cost_ratio(flow / 4000., net)
features = np.zeros((f.shape[0], 4))
features[:, :3] = f
features[:, 3] = costs
links = process_links(net, node, features)
color = features[:, 3] - 1.0 # we choose the costs
geojson_link(links, ['capacity', 'length', 'fftt', 'tt_over_fftt'], color)
def add_cognitive_cost(net, feat, thres, cog_cost):
net2 = np.copy(net)
small_capacity = np.zeros((net2.shape[0],))
for row in range(net2.shape[0]):
if feat[row, 0] < thres:
small_capacity[row] = 1.0
net2[row, 3] = net2[row, 3] + cog_cost
return net2, small_capacity
def chicago_ratio_r_nr():
'''
study the test_*.csv files generated by chicago_parametric_study()
in particular, visualize the ratio each type of users on each link
'''
fs = np.loadtxt('data/test_3.csv', delimiter=',', skiprows=0)
ratio = np.divide(fs[:, 0], np.maximum(np.sum(fs, axis=1), 1e-8))
net, demand, node, geometry = load_chicago()
features = np.zeros((fs.shape[0], 4))
features[:, :3] = geometry
features[:, 3] = ratio
links = process_links(net, node, features)
color = 5. * ratio # we choose the ratios of nr over r+nr
geojson_link(links, ['capacity', 'length', 'fftt', 'r_non_routed'], color)
def chicago_tt_over_fftt():
'''
study the test_*.csv files generated by chicago_parametric_study()
visualize tt/fftt for each edge
'''
net, demand, node, geometry = load_chicago()
g_nr, small_capacity = add_cognitive_cost(net, geometry, 2000., 1000.)
# f = np.loadtxt('data/test_0.csv', delimiter=',', skiprows=0)
alpha = 0.01
fs = np.loadtxt('data/test_{}.csv'.format(int(100. * alpha)),
delimiter=',', skiprows=0)
# f = np.loadtxt('data/test_100.csv', delimiter=',', skiprows=0)
f = np.sum(fs, axis=1)
costs = cost_ratio(f, net)
features = np.zeros((f.shape[0], 4))
features[:, :3] = geometry
features[:, 3] = costs
links = process_links(net, node, features)
color = (costs - 1.0) * 2.0 + 1.0
geojson_link(links, ['capacity', 'length', 'fftt', 'tt_over_fftt'], color)
def chicago_flow_over_capacity():
'''
study the test_*.csv files generated by chicago_parametric_study()
visualize flow/calacity for each edge
'''
net, demand, node, geometry = load_chicago()
# f = np.loadtxt('data/test_1.csv', delimiter=',', skiprows=0)
fs = np.loadtxt('data/test_2.csv', delimiter=',', skiprows=0)
# fs = np.loadtxt('data/test_3.csv', delimiter=',', skiprows=0)
# fs = np.loadtxt('data/test_4.csv', delimiter=',', skiprows=0)
# f = np.loadtxt('data/test_5.csv', delimiter=',', skiprows=0)
f = np.sum(fs, axis=1)
features = np.zeros((f.shape[0], 4))
features[:, :3] = geometry
f = np.divide(f, features[:, 0] / 4000.0)
features[:, 3] = f
links = process_links(net, node, features)
color = 2.0 * f + 1.0
geojson_link(links, ['capacity', 'length',
'fftt', 'flow_over_capacity'], color)
def chicago_parametric_study_2(alpha):
'''
study the test_*.csv files generated by chicago_parametric_study()
in particular, display the average costs for each type of users
alpha = 0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0
'''
# graphs
g, d, node, feat = load_chicago()
d[:, 2] = d[:, 2] / 2000 # technically, it's 2*demand/4000
parametric_study(alpha, g, d, node, feat, 2000.,
1000., 'data/chicago/test_{}.csv')
def chicago_metrics(alphas):
'''
study the test_*.csv files generated by chicago_parametric_study()
in particular, display the average costs for each type of users
'''
net, d, node, features = load_chicago()
d[:, 2] = d[:, 2] / 2000. # technically, it's 2*demand/4000
net2, small_capacity = multiply_cognitive_cost(net, features, 2000., 1000.)
save_metrics(alphas, net, net2, d, features, small_capacity,
'data/chicago/test_{}.csv', 'data/chicago/out.csv',
skiprows=1)
def chicago_routed_costs(alphas):
net, demand, node, features = load_chicago()
OD_routed_costs(alphas, net, demand, 'data/chicago/test_{}.csv',
'data/chicago/routed_costs.csv')
# (240,59) and (240,64) seem pretty parabolic to me...
def chicago_non_routed_costs(alphas):
net, demand, node, features = load_chicago()
net2, small_capacity = multiply_cognitive_cost(net, features, 2000., 1000.)
OD_non_routed_costs(alphas, net, net2, demand, 'data/chicago/test_{}.csv',
'data/chicago/non_routed_costs.csv')
def main():
# process_chicago_network()
# capacities_of_chicago()
visualize_equilibrium_in_chicago()
# multiply_demand_by_2()
# results_for_chicago()
# frank_wolfe_on_chicago()
# frank_wolfe_on_chicago_2()
# chicago_parametric_study()
# chicago_ratio_r_nr()
# chicago_tt_over_fftt()
# chicago_flow_over_capacity()
# chicago_parametric_study_2([.0,.1,.2,.3,.4,.5,.6,.7,.8,.9,1.0])
# chicago_metrics([.0,.1,.2,.3,.4,.5,.6,.7,.8,.9,1.0])
# chicago_routed_costs([.0,.1,.2,.3,.4,.5,.6,.7,.8,.9,1.0])
# chicago_non_routed_costs([.0,.1,.2,.3,.4,.5,.6,.7,.8,.9,1.0])
if __name__ == '__main__':
main()