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update baseline model
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@rakow
rakow committed Sep 23, 2024
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77 changes: 53 additions & 24 deletions input/v6.3/params/baseline_v5.yaml
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#Number of choices: 27235
#Varying: ['km', 'hours', 'walking_km', 'switches', 'valid']
# The sum of the weights (26625.70501375963) is different from the sample size (27235). Multiply the weights by 1.0228837127852763 to reconcile the two.
# Results for model trip_choice_performing_exp_income_util_money_car_price_perception_pt_price_perception_pt_switches
#Nbr of parameters: 9
# Results for model trip_choice_performing_exp_income_util_money_car_price_perception_pt_price_perception
#Nbr of parameters: 8
#Sample size: 27235
#Excluded data: 0
#Null log likelihood: -40155.25
#Final log likelihood: -29555.53
#Likelihood ratio test (null): 21199.42
#Rho square (null): 0.264
#Rho bar square (null): 0.264
#Akaike Information Criterion: 59129.07
#Bayesian Information Criterion: 59202.98
# Value Active bound ... Rob. t-test Rob. p-value
# ASC_bike -1.545543 0.0 ... -43.286963 0.000000e+00
# ASC_car -2.233102 0.0 ... -28.207947 0.000000e+00
# ASC_pt -0.980155 0.0 ... -33.584408 0.000000e+00
# ASC_ride -2.622651 0.0 ... -63.860835 0.000000e+00
# BETA_CAR_PRICE_PERCEPTION 0.309765 0.0 ... 2.046619 4.069552e-02
# BETA_PERFORMING 5.159459 0.0 ... 30.825908 0.000000e+00
# BETA_PT_SWITCHES 0.000000 1.0 ... 0.000000 1.000000e+00
# EXP_INCOME 0.789770 0.0 ... 3.803749 1.425223e-04
# UTIL_MONEY 0.156224 0.0 ... 8.080423 6.661338e-16
#Final log likelihood: -30195.23
#Likelihood ratio test (null): 19920.03
#Rho square (null): 0.248
#Rho bar square (null): 0.248
#Akaike Information Criterion: 60406.46
#Bayesian Information Criterion: 60472.16
# Value Rob. Std err Rob. t-test Rob. p-value
# ASC_bike -1.715849 0.031549 -54.386385 0.000000e+00
# ASC_car -2.158581 0.048948 -44.099241 0.000000e+00
# ASC_pt -0.809443 0.029379 -27.551863 0.000000e+00
# ASC_ride -2.564303 0.034765 -73.759994 0.000000e+00
# BETA_CAR_PRICE_PERCEPTION 0.158473 0.023321 6.795296 1.080913e-11
# BETA_PERFORMING 5.700208 0.155554 36.644578 0.000000e+00
# EXP_INCOME 0.391611 0.038146 10.266193 0.000000e+00
# UTIL_MONEY 0.532500 0.026424 20.152200 0.000000e+00
# Correlation matrix
# Covariance Correlation t-test p-value Rob. cov. Rob. corr. Rob. t-test Rob. p-value
# ASC_car-ASC_bike 0.000256 0.259219 -10.160431 0.000000e+00 0.000597 0.386459 -9.444052 0.000000e+00
# ASC_pt-ASC_bike 0.000342 0.581612 40.475364 0.000000e+00 0.000640 0.690007 37.657118 0.000000e+00
# ASC_pt-ASC_car 0.000501 0.444600 34.126157 0.000000e+00 0.000735 0.511313 31.901856 0.000000e+00
# ASC_ride-ASC_bike 0.000265 0.364104 -26.690799 0.000000e+00 0.000547 0.498303 -25.456136 0.000000e+00
# ASC_ride-ASC_car 0.000231 0.165875 -8.185488 2.220446e-16 0.000432 0.254041 -7.750977 9.103829e-15
# ASC_ride-ASC_pt 0.000185 0.223678 -48.261224 0.000000e+00 0.000326 0.318985 -46.566639 0.000000e+00
# BETA_CAR_PRICE_PERCEPTION-ASC_bike -0.000056 -0.125556 59.076207 0.000000e+00 -0.000211 -0.286277 42.331376 0.000000e+00
# BETA_CAR_PRICE_PERCEPTION-ASC_car 0.000454 0.534917 62.657319 0.000000e+00 0.000416 0.364650 50.474740 0.000000e+00
# BETA_CAR_PRICE_PERCEPTION-ASC_pt 0.000134 0.266074 34.658122 0.000000e+00 0.000061 0.088306 26.991066 0.000000e+00
# BETA_CAR_PRICE_PERCEPTION-ASC_ride -0.000193 -0.310452 64.360946 0.000000e+00 -0.000357 -0.440557 54.819792 0.000000e+00
# BETA_PERFORMING-ASC_bike -0.000887 -0.532129 84.685568 0.000000e+00 -0.003942 -0.803238 40.776913 0.000000e+00
# BETA_PERFORMING-ASC_car -0.000821 -0.257024 83.230030 0.000000e+00 -0.003268 -0.429151 43.177454 0.000000e+00
# BETA_PERFORMING-ASC_pt -0.000681 -0.358833 75.628198 0.000000e+00 -0.002829 -0.619080 37.141330 0.000000e+00
# BETA_PERFORMING-ASC_ride -0.000530 -0.225850 95.669603 0.000000e+00 -0.002595 -0.479940 47.247686 0.000000e+00
# BETA_PERFORMING-BETA_CAR_PRICE_PERCEPTION 0.000209 0.146243 75.832229 0.000000e+00 0.001180 0.325401 37.043819 0.000000e+00
# EXP_INCOME-ASC_bike 0.000101 0.148132 60.457025 0.000000e+00 0.000365 0.303395 50.812503 0.000000e+00
# EXP_INCOME-ASC_car -0.000458 -0.350799 41.851560 0.000000e+00 -0.000563 -0.301518 36.148432 0.000000e+00
# EXP_INCOME-ASC_pt 0.000002 0.003219 30.364000 0.000000e+00 0.000115 0.102178 26.276838 0.000000e+00
# EXP_INCOME-ASC_ride 0.000096 0.099515 70.997536 0.000000e+00 0.000375 0.282524 67.557954 0.000000e+00
# EXP_INCOME-BETA_CAR_PRICE_PERCEPTION -0.000245 -0.419629 5.540331 3.019004e-08 -0.000524 -0.588607 4.224089 2.399087e-05
# EXP_INCOME-BETA_PERFORMING -0.000380 -0.172496 -63.271976 0.000000e+00 -0.001964 -0.330957 -30.866171 0.000000e+00
# UTIL_MONEY-ASC_bike -0.000096 -0.203938 66.582694 0.000000e+00 -0.000304 -0.364746 46.863781 0.000000e+00
# UTIL_MONEY-ASC_car 0.000053 0.058231 57.096476 0.000000e+00 -0.000053 -0.040687 47.576467 0.000000e+00
# UTIL_MONEY-ASC_pt -0.000215 -0.400498 34.302566 0.000000e+00 -0.000380 -0.489254 27.854800 0.000000e+00
# UTIL_MONEY-ASC_ride 0.000171 0.256846 92.796138 0.000000e+00 0.000108 0.118027 75.330081 0.000000e+00
# UTIL_MONEY-BETA_CAR_PRICE_PERCEPTION -0.000204 -0.503111 10.718115 0.000000e+00 -0.000244 -0.396093 8.991802 0.000000e+00
# UTIL_MONEY-BETA_PERFORMING 0.000379 0.249067 -72.708055 0.000000e+00 0.001548 0.376691 -35.001272 0.000000e+00
# UTIL_MONEY-EXP_INCOME -0.000114 -0.182041 3.567757 3.600502e-04 -0.000174 -0.172640 2.817023 4.847105e-03


scoring:
scoringParameters:
- performing: 5.159459
utilityOfLineSwitch: 0
marginalUtilityOfMoney: 0.156224
- performing: 5.700208
marginalUtilityOfMoney: 0.532500
modeParams:
- mode: walk
constant: 0
- mode: car
constant: 0
dailyMonetaryConstant: -4.4296395
dailyMonetaryConstant: -2.2661639
- mode: pt
constant: 0
dailyMonetaryConstant: -0.929295
dailyMonetaryConstant: -0.475419
advancedScoring:
incomeExponent: 0.789770
incomeExponent: 0.391611
8 changes: 8 additions & 0 deletions src/main/python/choicemodels/estimate_biogeme_plan_choice.py
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prob = models.logit(U, AV, v["choice"])
logprob = log(MonteCarlo(prob))


logprob = {'loglike': logprob, 'weight': v["weight"]}
biogeme = bio.BIOGEME(database, logprob)

modelName = os.path.basename(args.input).replace(".csv", "")
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pandas_results = results.getEstimatedParameters()
print(pandas_results)

print()
print("Correlation matrix")

corr_matrix = results.getCorrelationResults()
print(corr_matrix)
12 changes: 11 additions & 1 deletion src/main/python/choicemodels/estimate_biogeme_trip_choice.py
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parser.add_argument("--est-pt-switches", help="Estimate the beta for PT switches", action="store_true")
parser.add_argument("--est-price-perception-car", help="Estimate price perception", action="store_true")
parser.add_argument("--est-price-perception-pt", help="Estimate price perception", action="store_true")
parser.add_argument("--est-ride-alpha", help="Estimate ride detour parameter", action="store_true")
parser.add_argument("--same-price-perception", help="Only estimate one fixed price perception factor", action="store_true")

parser.add_argument("--no-income", help="Don't consider the income", action="store_true")
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BETA_PT_PRICE_PERCEPTION = Beta('BETA_PT_PRICE_PERCEPTION', 1, 0, 1, ESTIMATE if args.est_price_perception_pt else FIXED)

BETA_PT_SWITCHES = Beta('BETA_PT_SWITCHES', 1, 0, None, ESTIMATE if args.est_pt_switches else FIXED)
BETA_RIDE_ALPHA = Beta('BETA_RIDE_ALPHA', 1, 0, 2, ESTIMATE if args.est_ride_alpha else FIXED)

for i, mode in enumerate(ds.modes, 1):
# Ride incurs double the cost as car, to account for the driver and passenger
u = ASC[mode] - BETA_PERFORMING * v[f"{mode}_hours"] * (2 if mode == "ride" else 1)
u = ASC[mode] - BETA_PERFORMING * v[f"{mode}_hours"] * ( (1 + BETA_RIDE_ALPHA) if mode == "ride" else 1)

price = km_costs[mode] * v[f"{mode}_km"]
price += daily_costs[mode] * v["dist_weight"] * (BETA_CAR_PRICE_PERCEPTION if mode == "car" else BETA_PT_PRICE_PERCEPTION)
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pandas_results = results.getEstimatedParameters()
print(pandas_results)

print()
print("Correlation matrix")

corr_matrix = results.getCorrelationResults()
print(corr_matrix)

# TOOD: matrix of correlations, costs correlations ?

# sim_results = biogeme.simulate(results.getBetaValues())

# print(sim_results)
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5 changes: 5 additions & 0 deletions src/main/python/choicemodels/prepare.py
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TripChoice = namedtuple("TripChoice", ["df", "modes", "varying", "global_income"])


# Set pandas options, when this package is used
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)

def read_global_income(input_file: str) -> float:
""" Read global income from input file """

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