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 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_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 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]
)
