Estimate and predict travel time on road networks

Implements two methods for prediction of average travel time on a route and its uncertainty (variance): a general population-based prediction, and a trip-specific method. The population-based method provide an estimate of average travel time and asymptotic Gaussian-based prediction intervals. The trip-specific methods integrates route data to provide tight route-specific Gaussian-based predictive distribution. From which, average travel time and prediction intervals are supplied.

Package is based on Elmasri et. al. (2020).

Package website

Installation

Install from GitHub.

# install.packages("devtools")
devtools::install_github("melmasri/traveltimeCLT")

Example

This package includes a small data set (trips) 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(data.table)

data(trips)
head(trips)
 tripID linkID timeBin     speed duration_secs distance_meters            entry_time 
1   2700  10469 Weekday  5.431914     13.000000        70.61488  2014-04-28 06:07:27 
2   2700  10444 Weekday  9.219505     18.927792       174.50487  2014-04-28 06:07:41 
3   2700  10460 Weekday  9.052796      8.589937        77.76295  2014-04-28 06:07:58 
4   2700  10462 Weekday  6.850282     14.619859       100.15015  2014-04-28 06:08:07 
5   2700  10512 Weekday  6.075674      5.071986        30.81574  2014-04-28 06:08:21 
6   2700   5890 Weekday 10.771731     31.585355       340.22893  2014-04-28 06:08:26 

Splittig data into train and test sets.

test_trips = sample_trips(trips, 10)
train = trips[!trips$tripID %in% test_trips,]
test =  trips[trips$tripID %in% test_trips,]

Fitting and predicting the trip-specific model, with lag 1

fit <- traveltimeCLT(train, lag = 1)
predict(fit, test)

Fitting and predicting the populaton model, with lag 1

fit <- traveltimeCLT(train, model = 'population')
predict(fit, test)

Bugs

For bugs and features, please refer to here.

References

Elmasri, M., Labbe, A., Larocque, D., Charlin, L,2020. “Predictive inference for travel time on transportation networks”. https://arxiv.org/abs/2004.11292