def calc_activity_choice_prob():
# activity/zone choice probability
for comm in enum_commodity():
for timeslice in xrange(min2slice(conf.DAY)+1):
for state in enum_state(comm, timeslice):
prob.activity_choice_prob[comm.person][timeslice][(state.activity,state.zone)] += \
flow.state_flows[comm][timeslice][state] / elem.person_flows[comm.person]
def calc_state_util():
math.exp = math.exp
math.log = math.log
# find the optimal uitility
for comm in enum_commodity():
# print " %s" % comm
# backtrack from the ending to the beginning
for timeslice in xrange(min2slice(conf.DAY) - 1, -1, -1):
for state in enum_state(comm, timeslice):
these_util = {}
for transition, starting_time, travel_cost, schedule_delay in \
enum_transition(comm, timeslice, state):
these_util[transition] = conf.THETA_tour * ( conf.discount * \
util.state_util[comm][starting_time][transition.state] + \
util.solo_util[timeslice][state.activity] + \
util.socio_util[comm.person][timeslice][(state.activity,state.zone)] - \
(travel_cost + schedule_delay) )
# if these_util == [], the decision space is empty and then continue to next
if these_util == {}:
continue
# scale the utility of each decision
min_util = min(these_util.values())
max_util = max(these_util.values())
sum_exp_util = sum([math.exp(utility-(max_util+min_util)/2.0) \
for utility in these_util.values()])
# calculate the expected optimal utility
util.state_util[comm][timeslice][state] = \
((max_util+min_util)/2.0 + math.log(sum_exp_util)) / conf.THETA_tour
# calculate the transition choice probability
for trans, utility in these_util.items():
prob.transition_choice_prob[comm][timeslice][state][trans] = \
math.exp(utility - (max_util+min_util)/2.0) / sum_exp_util
util.commodity_util[comm] = util.state_util[comm][0][comm.init_state]
def calc_state_util():
math.exp = math.exp
math.log = math.log
# find the optimal uitility
for comm in enum_commodity():
# print " %s" % comm
# backtrack from the ending to the beginning
for timeslice in xrange(min2slice(conf.DAY)-1,-1,-1):
for state in enum_state(comm, timeslice):
these_util = {}
for transition, starting_time, travel_cost, schedule_delay in \
enum_transition(comm, timeslice, state):
these_util[transition] = conf.THETA_tour * ( conf.discount * \
util.state_util[comm][starting_time][transition.state] + \
util.solo_util[timeslice][state.activity] + \
util.socio_util[comm.person][timeslice][(state.activity,state.zone)] - \
(travel_cost + schedule_delay) )
# if these_util == [], the decision space is empty and then continue to next
if these_util == {}:
continue
# scale the utility of each decision
min_util = min(these_util.values())
max_util = max(these_util.values())
sum_exp_util = sum([math.exp(utility-(max_util+min_util)/2.0) \
for utility in these_util.values()])
# calculate the expected optimal utility
util.state_util[comm][timeslice][state] = \
((max_util+min_util)/2.0 + math.log(sum_exp_util)) / conf.THETA_tour
# calculate the transition choice probability
for trans, utility in these_util.items():
prob.transition_choice_prob[comm][timeslice][state][trans] = \
math.exp(utility - (max_util+min_util)/2.0) / sum_exp_util
util.commodity_util[comm] = util.state_util[comm][0][comm.init_state]
def calc_activity_choice_prob():
# activity/zone choice probability
for comm in enum_commodity():
for timeslice in xrange(min2slice(conf.DAY) + 1):
for state in enum_state(comm, timeslice):
prob.activity_choice_prob[comm.person][timeslice][(state.activity,state.zone)] += \
flow.state_flows[comm][timeslice][state] / elem.person_flows[comm.person]
def export_state_flows(export):
print>>export, '\n-------- state flows ---------\n'
for comm in enum_commodity():
print>>export, " %s " % comm
for timeslice in xrange(min2slice(conf.DAY)+1):
print>>export, " [%03d]\t" % timeslice,
for state in enum_state(comm, timeslice):
print>>export, " %s: %8.2f\t" % (state, flow.state_flows[comm][timeslice][state]),
print>>export
def export_state_util(export):
print>>export, '\n------ state utility ------\n'
for comm in enum_commodity():
print>>export, " %s " % comm
for timeslice in xrange(min2slice(conf.DAY)):
print>>export, "[%3d] " % timeslice,
for state in enum_state(comm, timeslice):
print>>export, ("\t %s: %4.2f" % (state, util.state_util[comm][timeslice][state])),
print>>export
print>>export
def export_state_flows(export):
print >> export, '\n-------- state flows ---------\n'
for comm in enum_commodity():
print >> export, " %s " % comm
for timeslice in xrange(min2slice(conf.DAY) + 1):
print >> export, " [%03d]\t" % timeslice,
for state in enum_state(comm, timeslice):
print >> export, " %s: %8.2f\t" % (
state, flow.state_flows[comm][timeslice][state]),
print >> export
def init_state_flows(init_value):
flow.state_flows = {}
for comm in enum_commodity():
flow.state_flows[comm] = {}
for timeslice in xrange(min2slice(conf.DAY)+1):
flow.state_flows[comm][timeslice] = {}
for state in enum_state(comm, timeslice):
flow.state_flows[comm][timeslice][state] = init_value
# initialize the population at timeslice 0 for each zone
for comm in enum_commodity():
flow.state_flows[comm][0][comm.init_state] = flow.commodity_steps[comm]
def init_transition_choice_prob():
prob.transition_choice_prob = {}
for comm in enum_commodity():
prob.transition_choice_prob[comm] = {}
for timeslice in xrange(min2slice(conf.DAY)):
prob.transition_choice_prob[comm][timeslice] = {}
for state in enum_state(comm, timeslice):
prob.transition_choice_prob[comm][timeslice][state] = {}
for transition_info in enum_transition(comm, timeslice, state):
transition = transition_info[0]
prob.transition_choice_prob[comm][timeslice][state][transition] = 0.0
def init_state_flows(init_value):
flow.state_flows = {}
for comm in enum_commodity():
flow.state_flows[comm] = {}
for timeslice in xrange(min2slice(conf.DAY) + 1):
flow.state_flows[comm][timeslice] = {}
for state in enum_state(comm, timeslice):
flow.state_flows[comm][timeslice][state] = init_value
# initialize the population at timeslice 0 for each zone
for comm in enum_commodity():
flow.state_flows[comm][0][comm.init_state] = flow.commodity_steps[comm]
def init_transition_flows(init_value):
flow.transition_flows = {}
for comm in enum_commodity():
flow.transition_flows[comm] = {}
for timeslice in xrange(min2slice(conf.DAY)):
flow.transition_flows[comm][timeslice] = {}
for state in enum_state(comm, timeslice):
flow.transition_flows[comm][timeslice][state] = {}
for transition_info in enum_transition(comm, timeslice, state):
transition = transition_info[0]
flow.transition_flows[comm][timeslice][state][transition] = init_value
def export_state_util(export):
print >> export, '\n------ state utility ------\n'
for comm in enum_commodity():
print >> export, " %s " % comm
for timeslice in xrange(min2slice(conf.DAY)):
print >> export, "[%3d] " % timeslice,
for state in enum_state(comm, timeslice):
print >> export, (
"\t %s: %4.2f" %
(state, util.state_util[comm][timeslice][state])),
print >> export
print >> export
def init_transition_choice_prob():
prob.transition_choice_prob = {}
for comm in enum_commodity():
prob.transition_choice_prob[comm] = {}
for timeslice in xrange(min2slice(conf.DAY)):
prob.transition_choice_prob[comm][timeslice] = {}
for state in enum_state(comm, timeslice):
prob.transition_choice_prob[comm][timeslice][state] = {}
for transition_info in enum_transition(comm, timeslice, state):
transition = transition_info[0]
prob.transition_choice_prob[comm][timeslice][state][
transition] = 0.0
def init_transition_flows(init_value):
flow.transition_flows = {}
for comm in enum_commodity():
flow.transition_flows[comm] = {}
for timeslice in xrange(min2slice(conf.DAY)):
flow.transition_flows[comm][timeslice] = {}
for state in enum_state(comm, timeslice):
flow.transition_flows[comm][timeslice][state] = {}
for transition_info in enum_transition(comm, timeslice, state):
transition = transition_info[0]
flow.transition_flows[comm][timeslice][state][
transition] = init_value
def init_state_util():
util.state_util = {}
for comm in enum_commodity():
util.state_util[comm] = {}
for timeslice in xrange(min2slice(conf.DAY) + 1):
util.state_util[comm][timeslice] = {}
for state in enum_state(comm, timeslice):
# flag the states that has not been visited by
# setting the state utilities to negative infinity
util.state_util[comm][timeslice][state] = float('-inf')
# initialize the value of terminal state to zeros
# and the backward recursive process starts here
util.state_util[comm][min2slice(conf.DAY)][comm.term_state] = 0.0
def calc_average_activity_duration(commodity):
average_activity_duration = {}
# initialize average duration
for activity in commodity.bundle.activity_set:
average_activity_duration[activity] = 0.0
# average duration
for timeslice in xrange(min2slice(conf.DAY)):
for state in enum_state(commodity, timeslice):
average_activity_duration[state.activity] += conf.TICK * \
flow.state_flows[commodity][timeslice][state] / flow.commodity_steps[commodity]
# average travel time
average_travel_time = 1440 - sum(average_activity_duration.values())
return average_activity_duration, average_travel_time
def calc_average_activity_duration(commodity):
average_activity_duration = {}
# initialize average duration
for activity in commodity.bundle.activity_set:
average_activity_duration[activity] = 0.0
# average duration
for timeslice in xrange(min2slice(conf.DAY)):
for state in enum_state(commodity, timeslice):
average_activity_duration[state.activity] += conf.TICK * \
flow.state_flows[commodity][timeslice][state] / flow.commodity_steps[commodity]
# average travel time
average_travel_time = 1440 - sum(average_activity_duration.values())
return average_activity_duration, average_travel_time
def init_state_util():
util.state_util = {}
for comm in enum_commodity():
util.state_util[comm] = {}
for timeslice in xrange(min2slice(conf.DAY)+1):
util.state_util[comm][timeslice] = {}
for state in enum_state(comm, timeslice):
# flag the states that has not been visited by
# setting the state utilities to negative infinity
util.state_util[comm][timeslice][state] = float('-inf')
# initialize the value of terminal state to zeros
# and the backward recursive process starts here
util.state_util[comm][min2slice(conf.DAY)][comm.term_state] = 0.0
def calc_average_temporal_util(commodity):
average_temporal_utility = {}
average_travel_disutil = {}
for timeslice in xrange(min2slice(conf.DAY)+1):
# initialize temporal utility
average_temporal_utility[timeslice] = 0.0
for state in enum_state(commodity, timeslice):
# average temporal utility
average_temporal_utility[timeslice] += (util.solo_util[timeslice][state.activity] + \
util.socio_util[commodity.person][timeslice][(state.activity,state.zone)])* \
flow.state_flows[commodity][timeslice][state] / flow.commodity_steps[commodity]
# travel disutility
for timeslice in xrange(min2slice(conf.DAY)+1):
average_travel_disutil[timeslice] = (flow.commodity_steps[commodity] - \
flow.static_population[commodity][timeslice]) * \
conf.ALPHA_car / flow.commodity_steps[commodity]
return average_temporal_utility, average_travel_disutil
def calc_average_temporal_util(commodity):
average_temporal_utility = {}
average_travel_disutil = {}
for timeslice in xrange(min2slice(conf.DAY) + 1):
# initialize temporal utility
average_temporal_utility[timeslice] = 0.0
for state in enum_state(commodity, timeslice):
# average temporal utility
average_temporal_utility[timeslice] += (util.solo_util[timeslice][state.activity] + \
util.socio_util[commodity.person][timeslice][(state.activity,state.zone)])* \
flow.state_flows[commodity][timeslice][state] / flow.commodity_steps[commodity]
# travel disutility
for timeslice in xrange(min2slice(conf.DAY) + 1):
average_travel_disutil[timeslice] = (flow.commodity_steps[commodity] - \
flow.static_population[commodity][timeslice]) * \
conf.ALPHA_car / flow.commodity_steps[commodity]
return average_temporal_utility, average_travel_disutil
def calc_population_flows():
# initialize the population flows
init_zone_population(0.0)
init_activity_population(0.0)
init_static_population(0.0)
# calculate population flow variables based on state flows
for comm in enum_commodity():
for timeslice in xrange(min2slice(conf.DAY)+1):
for state in enum_state(comm, timeslice):
# calculate static population
flow.static_population[comm][timeslice] += \
flow.state_flows[comm][timeslice][state]
# calculate zone population
flow.zone_population[timeslice][state.zone] += \
flow.state_flows[comm][timeslice][state]
# calculate activity population
flow.activity_population[timeslice][state.activity] += \
flow.state_flows[comm][timeslice][state]
def calc_population_flows():
# initialize the population flows
init_zone_population(0.0)
init_activity_population(0.0)
init_static_population(0.0)
# calculate population flow variables based on state flows
for comm in enum_commodity():
for timeslice in xrange(min2slice(conf.DAY) + 1):
for state in enum_state(comm, timeslice):
# calculate static population
flow.static_population[comm][timeslice] += \
flow.state_flows[comm][timeslice][state]
# calculate zone population
flow.zone_population[timeslice][state.zone] += \
flow.state_flows[comm][timeslice][state]
# calculate activity population
flow.activity_population[timeslice][state.activity] += \
flow.state_flows[comm][timeslice][state]
def calc_state_flows():
# calculate flow variables based on state optimal utility
for comm in enum_commodity():
# print " %s" % comm
# from the beginning to the ending
for timeslice in xrange(min2slice(conf.DAY)):
for state in enum_state(comm, timeslice):
for transition, starting_time, cost, delay in enum_transition(comm, timeslice, state):
# calculate transition flows
flow.transition_flows[comm][timeslice][state][transition] = \
flow.state_flows[comm][timeslice][state] * \
prob.transition_choice_prob[comm][timeslice][state][transition]
# add transition flows to edge steps
add_movement_steps(transition.path, timeslice, \
flow.transition_flows[comm][timeslice][state][transition])
# update state flows
flow.state_flows[comm][starting_time][transition.state] = \
flow.state_flows[comm][starting_time][transition.state] + \
flow.transition_flows[comm][timeslice][state][transition]
def calc_state_flows():
# calculate flow variables based on state optimal utility
for comm in enum_commodity():
# print " %s" % comm
# from the beginning to the ending
for timeslice in xrange(min2slice(conf.DAY)):
for state in enum_state(comm, timeslice):
for transition, starting_time, cost, delay in enum_transition(comm, timeslice, state):
# calculate transition flows
flow.transition_flows[comm][timeslice][state][transition] = (
flow.state_flows[comm][timeslice][state]
* prob.transition_choice_prob[comm][timeslice][state][transition]
)
# add transition flows to edge steps
add_movement_steps(
transition.path, timeslice, flow.transition_flows[comm][timeslice][state][transition]
)
# update state flows
flow.state_flows[comm][starting_time][transition.state] = (
flow.state_flows[comm][starting_time][transition.state]
+ flow.transition_flows[comm][timeslice][state][transition]
)
