Module sumo_operation
Include the functions for reading SUMO inputs, writing outputs, running simulations.
@author: Qing-Long Lu (qinglong.lu@tum.de)
Expand source code
# -*- coding: utf-8 -*-
"""
Include the functions for reading SUMO inputs, writing outputs, running simulations.
@author: Qing-Long Lu (qinglong.lu@tum.de)
"""
import pandas as pd
import numpy as np
from preparation import DataPreparation
import subprocess
class SUMOOperation(DataPreparation):
def __init__(self, paths, sumo_var):
'''
Initialize a SUMOOperation object. This class inherits the functions from the DataPreparation class.
Parameters
----------
paths : dict
A dict of paths to SUMO, network, measurements, demand and cache, including:
<table>
<thead>
<tr>
<th align="left">Variable</th>
<th align="left">Description</th>
</tr>
</thead>
<tbody>
<tr>
<td>'sumo'</td>
<td>path to the SUMO installation location.</td>
</tr>
<tr>
<td>'network'</td>
<td>path to the SUMO network files.</td>
</tr>
<tr>
<td>'demand'</td>
<td>path to the prior OD estimates in the [O-format (VISUM/VISSUM)](https://sumo.dlr.de/docs/Demand/Importing_O/D_Matrices.html).</td>
</tr>
<tr>
<td>'measurements'</td>
<td>path to the true traffic measurements (in `.csv` format).</td>
</tr>
<tr>
<td>'cache'</td>
<td>path to cache folder.</td>
</tr>
</tbody>
</table>
sumo_var : dict
A dict of SUMO simulation setups, including:
<table>
<thead>
<tr>
<th align="left">Variable</th>
<th align="left">Description</th>
</tr>
</thead>
<tbody>
<tr>
<td>'network'</td>
<td>name of the network file.</td>
</tr>
<tr>
<td>'tazname'</td>
<td>name of the traffic analysis zone (TAZ) file.</td>
</tr>
<tr>
<td>'add_file'</td>
<td>name of the additional file, which includes the detectors information.</td>
</tr>
<tr>
<td>'starttime'</td>
<td>when the simulation should start.</td>
</tr>
<tr>
<td>'endtime'</td>
<td>when the simulation should stop.</td>
</tr>
<tr>
<td>'objective'</td>
<td>indicate the traffic measurements to use, 'counts' or 'time'.</td>
</tr>
<tr>
<td>'interval'</td>
<td>calibration interval (in common with the resolution of traffic measurements).</td>
</tr>
</tbody>
</table>
Returns
-------
None.
'''
super().__init__(paths=paths, sumo_var=sumo_var)
def write_od(self, od_matrix, ga=0):
'''
Convert the OD matrix from DataFrame format to SUMO format.
Parameters
----------
od_matrix : DataFrame
The OD matrix to write.
ga : int or string, optional
An indicator of gradient sample.
Returns
-------
COUNTER : list
A list of indictors for simulation index (length is `n_sumo`)
for distinguishing different simulation instance in parallel computing.
seedNN_vector : array
An array of random seeds for simulation (length is `n_sumo`)
for generating different simulation outputs in parallel computing.
GENERATION : list
A list of `ga` (length is `n_sumo`).
'''
start = int(float(self.sumo_var["starttime"]))
end = int(float(self.sumo_var['endtime']))
cols = list(np.arange(start, end, self.sumo_var['interval']))
cols = [0, 1] + cols
od_matrix.columns = cols
for i in range(od_matrix.shape[1]-2):
tt = start+self.sumo_var['interval']*i
inter = int(tt)
fract = (tt - inter)*60
if fract == 0:
TheBeginText= ('$OR;D2 \n* From-Time To-Time \n' + str(inter) +'.00 '
+ str(inter)+'.' + str(int(fract+60*self.sumo_var['interval'])) + '\n* Factor \n1.00\n')
if fract == 45:
TheBeginText= ('$OR;D2 \n* From-Time To-Time \n' + str(inter) +'.' + str(45)+' '
+ str(inter+1)+ '.00' + '\n* Factor \n1.00\n')
else:
TheBeginText= ('$OR;D2 \n* From-Time To-Time \n' + str(inter) +'.' + str(int(fract))+' '
+ str(inter)+'.' + str(int(fract+60*self.sumo_var['interval'])) + '\n* Factor \n1.00\n')
ma = self.paths["cache"] + 'OD_updated_'+str(od_matrix.columns[i+2])+'.txt'
file = open(ma, 'w')
file.write(TheBeginText)
file.close()
od_matrix.iloc[:, [0,1,i+2]].to_csv(ma, header=False, index=False, sep=' ', mode='a')
# replicate the variables
seedNN_vector = np.random.normal(0, 10000, self.sumo_var["n_sumo"]).tolist()
COUNTER = list(range(0,self.sumo_var['n_sumo']))
GENERATION =[]
for num in range(self.sumo_var['n_sumo']):
GENERATION.append(ga)
return COUNTER, seedNN_vector, GENERATION
def sumo_run(self, k, seed, ga):
'''
Run sumo simulation including: `od2trips` and `sumo`.
Parameters
----------
k : int
An indicator of simulation instance.
seed : int
A random value.
ga : int or string
An indicator of gradient sample.
Returns
-------
None.
'''
start = int(float(self.sumo_var["starttime"]))
end = int(float(self.sumo_var['endtime']))
rerouteProb = 0.1
cols = list(np.arange(start, end, self.sumo_var['interval']))
# OD2TRIPS, generate upd_ODTrips.trips.xml
prefx = str(ga)+'_'+str(k)+'_'
seed = int(seed)
oD2TRIPS = (self.paths["sumo"]+'bin/od2trips --no-step-log --output-prefix '+prefx+
' --spread.uniform --taz-files '+ self.paths["cache"] + self.sumo_var["tazname"] +' -d ')
for i in range(len(cols)):
oD2TRIPS = oD2TRIPS + self.paths["cache"] + 'OD_updated_'+str(cols[i])+'.txt,'
oD2TRIPS = oD2TRIPS[:-1]
oD2TRIPS = oD2TRIPS + ' -o '+ self.paths["cache"]+'upd_ODTrips.trips.xml --seed '+str(seed)
'''
If passing a single string, either shell must be True (see below) or else the string must simply
name the program to be executed without specifying any arguments.
'''
subprocess.run(oD2TRIPS, shell=True)
# generate out.xml, vehroutes.xml
sumoRUN = (self.paths["sumo"]+'bin/sumo --mesosim --no-step-log -W --output-prefix '+
prefx+' -n '+self.paths["cache"]+self.sumo_var["network"]+
' -b '+self.sumo_var['beginSimTime']+
' -e '+ self.sumo_var['endSimTime']+
' -r '+self.paths["cache"]+prefx+'upd_ODTrips.trips.xml'+
' --vehroutes ' + self.paths["cache"] + 'network.vehroutes.xml'+
' --additional-files '+ self.paths["cache"] + self.sumo_var["add_file"] +
' --xml-validation never'+
' --device.rerouting.probability '+str(rerouteProb)+
' --seed '+str(seed))
subprocess.run(sumoRUN, shell=True)
def sumo_aver(self, ga):
'''
Aggregate the simulation outputs.
Parameters
----------
ga : int
An indicator of gradient sample.
Returns
-------
measurements_agg : DataFrame
Simulation ouputs after proper aggregration based on the calibration interval.
'''
fract = float(self.sumo_var["starttime"])
start = int(fract)
end = int(float(self.sumo_var['endtime']))
fract = round(fract-start, 2)
agg_inter = self.sumo_var['interval']
# traffic counts
if self.sumo_var['objective'] == 'counts':
counts = pd.DataFrame()
for counter in range(self.sumo_var['n_sumo']):
prefx = str(ga)+'_'+str(counter)+'_'
loopDataNameK = (prefx + 'out.xml')
Loopdata_csv = self.paths["cache"] + prefx+'loopDataName.csv'
Data2csv = ('python ' + self.paths["sumo"] + 'tools/xml/xml2csv.py ' +
self.paths["cache"] + loopDataNameK +
' -o ' + Loopdata_csv)
subprocess.run(Data2csv, shell=True)
simulated_tripsInTable= pd.read_csv(Loopdata_csv, sep=";", header=0)
all_counts = pd.DataFrame()
for clock in np.arange(start, end, agg_inter):
startSimTime = clock*60*60 + fract*60
endSimTime = (clock+agg_inter)*60*60 + fract*60
interval = (simulated_tripsInTable['interval_begin']>=startSimTime)&(simulated_tripsInTable['interval_end'] <= endSimTime)
simulated_trips = simulated_tripsInTable.loc[interval,:].copy()
simulated_trips.loc[:,'EdgeID'] = simulated_trips.apply(lambda x: x['interval_id'].split('_')[1], axis=1)
simulated_trips = simulated_trips[['EdgeID', 'interval_entered']]
simulated_trips.columns = ['EdgeID', 'Counts_'+str(clock)+'_'+str(clock+agg_inter)]
grouped = simulated_trips.groupby('EdgeID').agg(np.sum)
all_counts = pd.concat([all_counts, grouped], axis=1)
counts = pd.concat([counts, all_counts], axis=1)
measurements_agg = counts.groupby(by=counts.columns, axis=1).apply(lambda f: f.mean(axis=1))
# travel time
if self.sumo_var['objective'] == 'time':
time = pd.DataFrame()
for counter in range(self.sumo_var['n_sumo']):
prefx = str(ga)+'_'+str(counter)+'_'
route_xml = self.paths["cache"] + prefx +'network.vehroutes.xml'
route_csv = self.paths["cache"] + prefx +'network.vehroutes.csv'
Data2csv = ('python ' + self.paths["sumo"] + 'tools/xml/xml2csv.py ' +
route_xml + ' -x ' + self.paths["sumo"] + 'data/xsd/routes_file.xsd -o ' + route_csv)
subprocess.run(Data2csv, shell=True)
TT_output = pd.read_csv(route_csv, sep=';')
TT_output = TT_output.loc[:,['vehicle_arrival', 'vehicle_depart', 'vehicle_fromTaz','vehicle_toTaz']]
TT_output["Travel_time"] = (TT_output["vehicle_arrival"] - TT_output["vehicle_depart"])
all_tt = pd.DataFrame()
for clock in np.arange(start, end, agg_inter):
startSimTime = clock*60*60 + fract*60
endSimTime = (clock+agg_inter)*60*60 + fract*60
interval = (TT_output['vehicle_arrival']>=startSimTime)&(TT_output['vehicle_depart'] <= endSimTime)
simulated_tt = TT_output[interval]
simulated_tt = simulated_tt.loc[:, ['vehicle_fromTaz','vehicle_toTaz', 'Travel_time']]
grouped = simulated_tt.groupby(['vehicle_fromTaz','vehicle_toTaz']).mean()
grouped.columns = ['tt_'+str(clock)+'_'+str(clock+agg_inter)]
all_tt = pd.concat([all_tt, grouped], axis=1)
time = pd.concat([time, all_tt], axis=1)
measurements_agg = time.groupby(by=time.columns, axis=1).apply(lambda f: f.mean(axis=1))
return measurements_aggClasses
class SUMOOperation (paths, sumo_var)-
Initialize a SUMOOperation object. This class inherits the functions from the DataPreparation class.
Parameters
paths:dict- A dict of paths to SUMO, network, measurements, demand and cache, including:
Variable Description 'sumo' path to the SUMO installation location. 'network' path to the SUMO network files. 'demand' path to the prior OD estimates in the O-format (VISUM/VISSUM). 'measurements' path to the true traffic measurements (in .csvformat).'cache' path to cache folder. sumo_var:dict- A dict of SUMO simulation setups, including:
Variable Description 'network' name of the network file. 'tazname' name of the traffic analysis zone (TAZ) file. 'add_file' name of the additional file, which includes the detectors information. 'starttime' when the simulation should start. 'endtime' when the simulation should stop. 'objective' indicate the traffic measurements to use, 'counts' or 'time'. 'interval' calibration interval (in common with the resolution of traffic measurements).
Returns
None.
Expand source code
class SUMOOperation(DataPreparation): def __init__(self, paths, sumo_var): ''' Initialize a SUMOOperation object. This class inherits the functions from the DataPreparation class. Parameters ---------- paths : dict A dict of paths to SUMO, network, measurements, demand and cache, including: <table> <thead> <tr> <th align="left">Variable</th> <th align="left">Description</th> </tr> </thead> <tbody> <tr> <td>'sumo'</td> <td>path to the SUMO installation location.</td> </tr> <tr> <td>'network'</td> <td>path to the SUMO network files.</td> </tr> <tr> <td>'demand'</td> <td>path to the prior OD estimates in the [O-format (VISUM/VISSUM)](https://sumo.dlr.de/docs/Demand/Importing_O/D_Matrices.html).</td> </tr> <tr> <td>'measurements'</td> <td>path to the true traffic measurements (in `.csv` format).</td> </tr> <tr> <td>'cache'</td> <td>path to cache folder.</td> </tr> </tbody> </table> sumo_var : dict A dict of SUMO simulation setups, including: <table> <thead> <tr> <th align="left">Variable</th> <th align="left">Description</th> </tr> </thead> <tbody> <tr> <td>'network'</td> <td>name of the network file.</td> </tr> <tr> <td>'tazname'</td> <td>name of the traffic analysis zone (TAZ) file.</td> </tr> <tr> <td>'add_file'</td> <td>name of the additional file, which includes the detectors information.</td> </tr> <tr> <td>'starttime'</td> <td>when the simulation should start.</td> </tr> <tr> <td>'endtime'</td> <td>when the simulation should stop.</td> </tr> <tr> <td>'objective'</td> <td>indicate the traffic measurements to use, 'counts' or 'time'.</td> </tr> <tr> <td>'interval'</td> <td>calibration interval (in common with the resolution of traffic measurements).</td> </tr> </tbody> </table> Returns ------- None. ''' super().__init__(paths=paths, sumo_var=sumo_var) def write_od(self, od_matrix, ga=0): ''' Convert the OD matrix from DataFrame format to SUMO format. Parameters ---------- od_matrix : DataFrame The OD matrix to write. ga : int or string, optional An indicator of gradient sample. Returns ------- COUNTER : list A list of indictors for simulation index (length is `n_sumo`) for distinguishing different simulation instance in parallel computing. seedNN_vector : array An array of random seeds for simulation (length is `n_sumo`) for generating different simulation outputs in parallel computing. GENERATION : list A list of `ga` (length is `n_sumo`). ''' start = int(float(self.sumo_var["starttime"])) end = int(float(self.sumo_var['endtime'])) cols = list(np.arange(start, end, self.sumo_var['interval'])) cols = [0, 1] + cols od_matrix.columns = cols for i in range(od_matrix.shape[1]-2): tt = start+self.sumo_var['interval']*i inter = int(tt) fract = (tt - inter)*60 if fract == 0: TheBeginText= ('$OR;D2 \n* From-Time To-Time \n' + str(inter) +'.00 ' + str(inter)+'.' + str(int(fract+60*self.sumo_var['interval'])) + '\n* Factor \n1.00\n') if fract == 45: TheBeginText= ('$OR;D2 \n* From-Time To-Time \n' + str(inter) +'.' + str(45)+' ' + str(inter+1)+ '.00' + '\n* Factor \n1.00\n') else: TheBeginText= ('$OR;D2 \n* From-Time To-Time \n' + str(inter) +'.' + str(int(fract))+' ' + str(inter)+'.' + str(int(fract+60*self.sumo_var['interval'])) + '\n* Factor \n1.00\n') ma = self.paths["cache"] + 'OD_updated_'+str(od_matrix.columns[i+2])+'.txt' file = open(ma, 'w') file.write(TheBeginText) file.close() od_matrix.iloc[:, [0,1,i+2]].to_csv(ma, header=False, index=False, sep=' ', mode='a') # replicate the variables seedNN_vector = np.random.normal(0, 10000, self.sumo_var["n_sumo"]).tolist() COUNTER = list(range(0,self.sumo_var['n_sumo'])) GENERATION =[] for num in range(self.sumo_var['n_sumo']): GENERATION.append(ga) return COUNTER, seedNN_vector, GENERATION def sumo_run(self, k, seed, ga): ''' Run sumo simulation including: `od2trips` and `sumo`. Parameters ---------- k : int An indicator of simulation instance. seed : int A random value. ga : int or string An indicator of gradient sample. Returns ------- None. ''' start = int(float(self.sumo_var["starttime"])) end = int(float(self.sumo_var['endtime'])) rerouteProb = 0.1 cols = list(np.arange(start, end, self.sumo_var['interval'])) # OD2TRIPS, generate upd_ODTrips.trips.xml prefx = str(ga)+'_'+str(k)+'_' seed = int(seed) oD2TRIPS = (self.paths["sumo"]+'bin/od2trips --no-step-log --output-prefix '+prefx+ ' --spread.uniform --taz-files '+ self.paths["cache"] + self.sumo_var["tazname"] +' -d ') for i in range(len(cols)): oD2TRIPS = oD2TRIPS + self.paths["cache"] + 'OD_updated_'+str(cols[i])+'.txt,' oD2TRIPS = oD2TRIPS[:-1] oD2TRIPS = oD2TRIPS + ' -o '+ self.paths["cache"]+'upd_ODTrips.trips.xml --seed '+str(seed) ''' If passing a single string, either shell must be True (see below) or else the string must simply name the program to be executed without specifying any arguments. ''' subprocess.run(oD2TRIPS, shell=True) # generate out.xml, vehroutes.xml sumoRUN = (self.paths["sumo"]+'bin/sumo --mesosim --no-step-log -W --output-prefix '+ prefx+' -n '+self.paths["cache"]+self.sumo_var["network"]+ ' -b '+self.sumo_var['beginSimTime']+ ' -e '+ self.sumo_var['endSimTime']+ ' -r '+self.paths["cache"]+prefx+'upd_ODTrips.trips.xml'+ ' --vehroutes ' + self.paths["cache"] + 'network.vehroutes.xml'+ ' --additional-files '+ self.paths["cache"] + self.sumo_var["add_file"] + ' --xml-validation never'+ ' --device.rerouting.probability '+str(rerouteProb)+ ' --seed '+str(seed)) subprocess.run(sumoRUN, shell=True) def sumo_aver(self, ga): ''' Aggregate the simulation outputs. Parameters ---------- ga : int An indicator of gradient sample. Returns ------- measurements_agg : DataFrame Simulation ouputs after proper aggregration based on the calibration interval. ''' fract = float(self.sumo_var["starttime"]) start = int(fract) end = int(float(self.sumo_var['endtime'])) fract = round(fract-start, 2) agg_inter = self.sumo_var['interval'] # traffic counts if self.sumo_var['objective'] == 'counts': counts = pd.DataFrame() for counter in range(self.sumo_var['n_sumo']): prefx = str(ga)+'_'+str(counter)+'_' loopDataNameK = (prefx + 'out.xml') Loopdata_csv = self.paths["cache"] + prefx+'loopDataName.csv' Data2csv = ('python ' + self.paths["sumo"] + 'tools/xml/xml2csv.py ' + self.paths["cache"] + loopDataNameK + ' -o ' + Loopdata_csv) subprocess.run(Data2csv, shell=True) simulated_tripsInTable= pd.read_csv(Loopdata_csv, sep=";", header=0) all_counts = pd.DataFrame() for clock in np.arange(start, end, agg_inter): startSimTime = clock*60*60 + fract*60 endSimTime = (clock+agg_inter)*60*60 + fract*60 interval = (simulated_tripsInTable['interval_begin']>=startSimTime)&(simulated_tripsInTable['interval_end'] <= endSimTime) simulated_trips = simulated_tripsInTable.loc[interval,:].copy() simulated_trips.loc[:,'EdgeID'] = simulated_trips.apply(lambda x: x['interval_id'].split('_')[1], axis=1) simulated_trips = simulated_trips[['EdgeID', 'interval_entered']] simulated_trips.columns = ['EdgeID', 'Counts_'+str(clock)+'_'+str(clock+agg_inter)] grouped = simulated_trips.groupby('EdgeID').agg(np.sum) all_counts = pd.concat([all_counts, grouped], axis=1) counts = pd.concat([counts, all_counts], axis=1) measurements_agg = counts.groupby(by=counts.columns, axis=1).apply(lambda f: f.mean(axis=1)) # travel time if self.sumo_var['objective'] == 'time': time = pd.DataFrame() for counter in range(self.sumo_var['n_sumo']): prefx = str(ga)+'_'+str(counter)+'_' route_xml = self.paths["cache"] + prefx +'network.vehroutes.xml' route_csv = self.paths["cache"] + prefx +'network.vehroutes.csv' Data2csv = ('python ' + self.paths["sumo"] + 'tools/xml/xml2csv.py ' + route_xml + ' -x ' + self.paths["sumo"] + 'data/xsd/routes_file.xsd -o ' + route_csv) subprocess.run(Data2csv, shell=True) TT_output = pd.read_csv(route_csv, sep=';') TT_output = TT_output.loc[:,['vehicle_arrival', 'vehicle_depart', 'vehicle_fromTaz','vehicle_toTaz']] TT_output["Travel_time"] = (TT_output["vehicle_arrival"] - TT_output["vehicle_depart"]) all_tt = pd.DataFrame() for clock in np.arange(start, end, agg_inter): startSimTime = clock*60*60 + fract*60 endSimTime = (clock+agg_inter)*60*60 + fract*60 interval = (TT_output['vehicle_arrival']>=startSimTime)&(TT_output['vehicle_depart'] <= endSimTime) simulated_tt = TT_output[interval] simulated_tt = simulated_tt.loc[:, ['vehicle_fromTaz','vehicle_toTaz', 'Travel_time']] grouped = simulated_tt.groupby(['vehicle_fromTaz','vehicle_toTaz']).mean() grouped.columns = ['tt_'+str(clock)+'_'+str(clock+agg_inter)] all_tt = pd.concat([all_tt, grouped], axis=1) time = pd.concat([time, all_tt], axis=1) measurements_agg = time.groupby(by=time.columns, axis=1).apply(lambda f: f.mean(axis=1)) return measurements_aggAncestors
- preparation.DataPreparation
Methods
def sumo_aver(self, ga)-
Aggregate the simulation outputs.
Parameters
ga:int- An indicator of gradient sample.
Returns
measurements_agg:DataFrame- Simulation ouputs after proper aggregration based on the calibration interval.
Expand source code
def sumo_aver(self, ga): ''' Aggregate the simulation outputs. Parameters ---------- ga : int An indicator of gradient sample. Returns ------- measurements_agg : DataFrame Simulation ouputs after proper aggregration based on the calibration interval. ''' fract = float(self.sumo_var["starttime"]) start = int(fract) end = int(float(self.sumo_var['endtime'])) fract = round(fract-start, 2) agg_inter = self.sumo_var['interval'] # traffic counts if self.sumo_var['objective'] == 'counts': counts = pd.DataFrame() for counter in range(self.sumo_var['n_sumo']): prefx = str(ga)+'_'+str(counter)+'_' loopDataNameK = (prefx + 'out.xml') Loopdata_csv = self.paths["cache"] + prefx+'loopDataName.csv' Data2csv = ('python ' + self.paths["sumo"] + 'tools/xml/xml2csv.py ' + self.paths["cache"] + loopDataNameK + ' -o ' + Loopdata_csv) subprocess.run(Data2csv, shell=True) simulated_tripsInTable= pd.read_csv(Loopdata_csv, sep=";", header=0) all_counts = pd.DataFrame() for clock in np.arange(start, end, agg_inter): startSimTime = clock*60*60 + fract*60 endSimTime = (clock+agg_inter)*60*60 + fract*60 interval = (simulated_tripsInTable['interval_begin']>=startSimTime)&(simulated_tripsInTable['interval_end'] <= endSimTime) simulated_trips = simulated_tripsInTable.loc[interval,:].copy() simulated_trips.loc[:,'EdgeID'] = simulated_trips.apply(lambda x: x['interval_id'].split('_')[1], axis=1) simulated_trips = simulated_trips[['EdgeID', 'interval_entered']] simulated_trips.columns = ['EdgeID', 'Counts_'+str(clock)+'_'+str(clock+agg_inter)] grouped = simulated_trips.groupby('EdgeID').agg(np.sum) all_counts = pd.concat([all_counts, grouped], axis=1) counts = pd.concat([counts, all_counts], axis=1) measurements_agg = counts.groupby(by=counts.columns, axis=1).apply(lambda f: f.mean(axis=1)) # travel time if self.sumo_var['objective'] == 'time': time = pd.DataFrame() for counter in range(self.sumo_var['n_sumo']): prefx = str(ga)+'_'+str(counter)+'_' route_xml = self.paths["cache"] + prefx +'network.vehroutes.xml' route_csv = self.paths["cache"] + prefx +'network.vehroutes.csv' Data2csv = ('python ' + self.paths["sumo"] + 'tools/xml/xml2csv.py ' + route_xml + ' -x ' + self.paths["sumo"] + 'data/xsd/routes_file.xsd -o ' + route_csv) subprocess.run(Data2csv, shell=True) TT_output = pd.read_csv(route_csv, sep=';') TT_output = TT_output.loc[:,['vehicle_arrival', 'vehicle_depart', 'vehicle_fromTaz','vehicle_toTaz']] TT_output["Travel_time"] = (TT_output["vehicle_arrival"] - TT_output["vehicle_depart"]) all_tt = pd.DataFrame() for clock in np.arange(start, end, agg_inter): startSimTime = clock*60*60 + fract*60 endSimTime = (clock+agg_inter)*60*60 + fract*60 interval = (TT_output['vehicle_arrival']>=startSimTime)&(TT_output['vehicle_depart'] <= endSimTime) simulated_tt = TT_output[interval] simulated_tt = simulated_tt.loc[:, ['vehicle_fromTaz','vehicle_toTaz', 'Travel_time']] grouped = simulated_tt.groupby(['vehicle_fromTaz','vehicle_toTaz']).mean() grouped.columns = ['tt_'+str(clock)+'_'+str(clock+agg_inter)] all_tt = pd.concat([all_tt, grouped], axis=1) time = pd.concat([time, all_tt], axis=1) measurements_agg = time.groupby(by=time.columns, axis=1).apply(lambda f: f.mean(axis=1)) return measurements_agg def sumo_run(self, k, seed, ga)-
Run sumo simulation including:
od2tripsandsumo.Parameters
k:int- An indicator of simulation instance.
seed:int- A random value.
ga:intorstring- An indicator of gradient sample.
Returns
None.
Expand source code
def sumo_run(self, k, seed, ga): ''' Run sumo simulation including: `od2trips` and `sumo`. Parameters ---------- k : int An indicator of simulation instance. seed : int A random value. ga : int or string An indicator of gradient sample. Returns ------- None. ''' start = int(float(self.sumo_var["starttime"])) end = int(float(self.sumo_var['endtime'])) rerouteProb = 0.1 cols = list(np.arange(start, end, self.sumo_var['interval'])) # OD2TRIPS, generate upd_ODTrips.trips.xml prefx = str(ga)+'_'+str(k)+'_' seed = int(seed) oD2TRIPS = (self.paths["sumo"]+'bin/od2trips --no-step-log --output-prefix '+prefx+ ' --spread.uniform --taz-files '+ self.paths["cache"] + self.sumo_var["tazname"] +' -d ') for i in range(len(cols)): oD2TRIPS = oD2TRIPS + self.paths["cache"] + 'OD_updated_'+str(cols[i])+'.txt,' oD2TRIPS = oD2TRIPS[:-1] oD2TRIPS = oD2TRIPS + ' -o '+ self.paths["cache"]+'upd_ODTrips.trips.xml --seed '+str(seed) ''' If passing a single string, either shell must be True (see below) or else the string must simply name the program to be executed without specifying any arguments. ''' subprocess.run(oD2TRIPS, shell=True) # generate out.xml, vehroutes.xml sumoRUN = (self.paths["sumo"]+'bin/sumo --mesosim --no-step-log -W --output-prefix '+ prefx+' -n '+self.paths["cache"]+self.sumo_var["network"]+ ' -b '+self.sumo_var['beginSimTime']+ ' -e '+ self.sumo_var['endSimTime']+ ' -r '+self.paths["cache"]+prefx+'upd_ODTrips.trips.xml'+ ' --vehroutes ' + self.paths["cache"] + 'network.vehroutes.xml'+ ' --additional-files '+ self.paths["cache"] + self.sumo_var["add_file"] + ' --xml-validation never'+ ' --device.rerouting.probability '+str(rerouteProb)+ ' --seed '+str(seed)) subprocess.run(sumoRUN, shell=True) def write_od(self, od_matrix, ga=0)-
Convert the OD matrix from DataFrame format to SUMO format.
Parameters
od_matrix:DataFrame- The OD matrix to write.
ga:intorstring, optional- An indicator of gradient sample.
Returns
COUNTER:list- A list of indictors for simulation index (length is
n_sumo) for distinguishing different simulation instance in parallel computing. seedNN_vector:array- An array of random seeds for simulation (length is
n_sumo) for generating different simulation outputs in parallel computing. GENERATION:list- A list of
ga(length isn_sumo).
Expand source code
def write_od(self, od_matrix, ga=0): ''' Convert the OD matrix from DataFrame format to SUMO format. Parameters ---------- od_matrix : DataFrame The OD matrix to write. ga : int or string, optional An indicator of gradient sample. Returns ------- COUNTER : list A list of indictors for simulation index (length is `n_sumo`) for distinguishing different simulation instance in parallel computing. seedNN_vector : array An array of random seeds for simulation (length is `n_sumo`) for generating different simulation outputs in parallel computing. GENERATION : list A list of `ga` (length is `n_sumo`). ''' start = int(float(self.sumo_var["starttime"])) end = int(float(self.sumo_var['endtime'])) cols = list(np.arange(start, end, self.sumo_var['interval'])) cols = [0, 1] + cols od_matrix.columns = cols for i in range(od_matrix.shape[1]-2): tt = start+self.sumo_var['interval']*i inter = int(tt) fract = (tt - inter)*60 if fract == 0: TheBeginText= ('$OR;D2 \n* From-Time To-Time \n' + str(inter) +'.00 ' + str(inter)+'.' + str(int(fract+60*self.sumo_var['interval'])) + '\n* Factor \n1.00\n') if fract == 45: TheBeginText= ('$OR;D2 \n* From-Time To-Time \n' + str(inter) +'.' + str(45)+' ' + str(inter+1)+ '.00' + '\n* Factor \n1.00\n') else: TheBeginText= ('$OR;D2 \n* From-Time To-Time \n' + str(inter) +'.' + str(int(fract))+' ' + str(inter)+'.' + str(int(fract+60*self.sumo_var['interval'])) + '\n* Factor \n1.00\n') ma = self.paths["cache"] + 'OD_updated_'+str(od_matrix.columns[i+2])+'.txt' file = open(ma, 'w') file.write(TheBeginText) file.close() od_matrix.iloc[:, [0,1,i+2]].to_csv(ma, header=False, index=False, sep=' ', mode='a') # replicate the variables seedNN_vector = np.random.normal(0, 10000, self.sumo_var["n_sumo"]).tolist() COUNTER = list(range(0,self.sumo_var['n_sumo'])) GENERATION =[] for num in range(self.sumo_var['n_sumo']): GENERATION.append(ga) return COUNTER, seedNN_vector, GENERATION