The package is still under development in the Alpha stage.
Install from GitHub with:
# install.packages("devtools")
devtools::install_github("AdrienHdz/traveltimeCLT")This package includes a small data set (tripset) that aggregates
map-matched anonymized mobile phone GPS data collected in Quebec city in
2014 using the Mon Trajet smartphone application developed by Brisk
Synergies Inc. The precise duration of the
time period is kept confidential.
View the data with:
library(traveltimeCLT)
library(traveltimeHMM)
library(data.table)
data(trips)
head(trips)
trip time timeBins tt logspeed length linkId
<int> <chr> <chr> <dbl> <dbl> <dbl> <int>
15 2014-05-05 07:32:41.000 MorningRush 8.00 1.801 48.422 24088
15 2014-05-05 07:32:50.000 MorningRush 64.49 0.147 74.703 23470
15 2014-05-05 07:33:54.000 MorningRush 17.34 2.621 238.410 34576
15 2014-05-05 07:34:11.000 MorningRush 4.54 2.875 80.557 34586
15 2014-05-05 07:34:16.000 MorningRush 3.03 2.905 55.259 34583
15 2014-05-05 07:34:19.000 MorningRush 8.56 2.810 142.211 6704
Transforming the variables
trips <- as.data.table(trips)
trips$speed <- exp(trips$logspeed)Splittig data into train and test sets.
test.trips <- create_test_trips(M = 500, trips, min.n = 1)
test = trips[trip %in% test.trips]
train = trips[!trip %in% test.trips]
Creating rules and time-bins
myrules = list(
list(start='6:30', end= '9:00', days = 0:6, tag='MorningRush'),
list(start='15:00', end= '18:00', days = 0:6, tag='EveningRush'))
mytimebins = c("MorningRush", "EveningRush", "Other")Fit the model using the following code:
ttCLTmodel <- traveltimeCLT(data.train = train,
M = 1000,
L = 2,
bin = "MorningRush",
rules = myrules,
data.timebins = mytimebins)To predict on the test set and get estimations:
ttCLTresults <- predict_traveltimeCLT(obj.traveltime = ttCLTmodel,
data.test = test,
bin = "MorningRush",
rules = myrules)Elmasri, M., Labbe, A., Larocque, D., Charlin, L, 2020. “Prediction intervals for travel time on transportation networks”.