README.md
In issactoast/ikhyd: "I Know How you Drive - Analysis Tools for Telematics Data"
I Know How You Drive (ikhyd)
R package ikhyd is designed to make the driving behavior analysis
using telematics data easier.
Prerequisite
Package installation and loading
To reproduce the research result, the ikhyd should be installed from
github page, , using the following code:
# packages loading
devtools::install_github("issactoast/ikhyd")
After the installation, load package as follows:
library(ikhyd)
#> To cite this package use citation('ikhyd')
#>
#> Text citation:
#>
#> Issac Lee (2020). ikhyd : I Know How You Drive - Collection of R functions to make telematics analysis easier.
#> R package version 0.1.0. URL https://github.com/issactoast/ikhyd
Google API
To use the visualization functions, the google map aip key should be fed
into the function as an argument. Google API can be obtained the Google
Maps Platform by following the
registration
instruction.
The google map API service will charge $2 per 1000 requests however,
$200 deposite will be added to the account per
month. Thus, the
personal usage is technically free but you need to register the credit
card to use the service.
For example, track_vis() has the following structure:
track_vis(sample_trip$gps_data,
api = your_google_api,
zoom = 1)
How to access to data set
ikhyd package contains seven telematics files;
- Sample route telematics: sample_trip.csv
- Test route with OBD information:
- trip_with_obd.csv
- trip_with_obd.txt
- Driving telematics files:
- driver1.csv
- driver2.csv
- driver3.csv
- driver4.csv
Note that trip_with_obd.txt contains the OBD speed information which
combines with telematics data generated by smartphone,
trip_with_obd.csv.
To get the path of the each files, users can use system.file() in
R. For example, the follow code will give you the path of
sample_trip.csv;
system.file("extdata", "sample_trip.csv", package = "ikhyd")
#> [1] "C:/R/R-4.0.3/library/ikhyd/extdata/sample_trip.csv"
See help and actual code
You can check out the actual code in this instruction by typing the name
of the R console. For example, examination the code of
load_telematic_data() will be as follows:
load_telematic_data
#> function(file_path, all_in_one = FALSE, ...){
#> if (all_in_one){
#> gps_data <- get_trip(file_path, data_option = 1)
#> acc_data <- get_trip(file_path, data_option = 2)
#> lacc_data <- get_trip(file_path, data_option = 3)
#> gyro_data <- get_trip(file_path, data_option = 4)
#> angle_data <- get_trip(file_path, data_option = 5)
#> speed_data <- get_trip(file_path, data_option = 6)
#> result <- list(gps_data = gps_data,
#> acc_data = acc_data,
#> lacc_data = lacc_data,
#> gyro_data = gyro_data,
#> angle_data = angle_data,
#> speed_data = speed_data)
#> return(result)
#> } else {
#> result <- get_trip(file_path, ...)
#> return(result)
#> }
#> }
#> <bytecode: 0x0000000013906a20>
#> <environment: namespace:ikhyd>
Also, the help page of each function is available via
?function_name().
?load_telematic_data()
Load trip data
When you have a path of telematics data, you can load the data using
get_trip() with data_option arguments as follows:
# telematics file path
sample_trip_path <- system.file("extdata", "sample_trip.csv", package = "ikhyd")
# load data
sample_trip <- load_telematic_data(sample_trip_path, all_in_one = TRUE)
summary(sample_trip)
#> Length Class Mode
#> gps_data 6 data.frame list
#> acc_data 4 data.frame list
#> lacc_data 4 data.frame list
#> gyro_data 4 data.frame list
#> angle_data 4 data.frame list
#> speed_data 3 data.frame list
Visualization
GPS data
The snippet of gps_data for the sample trip looks as follows:
head(sample_trip$gps_data)
#> time x y z accuracy_horiz accuracy_vert
#> 1 0.000 -91.53799 41.66253 197.2922 5 4
#> 2 0.038 -91.53799 41.66253 197.2922 5 4
#> 3 0.079 -91.53799 41.66253 197.2821 5 4
#> 4 0.120 -91.53799 41.66253 197.2821 5 4
#> 5 0.153 -91.53799 41.66253 197.2821 5 4
#> 6 0.194 -91.53799 41.66253 197.2821 5 4
The GPS coordinates of the sample trip can be visualized with
track_vis() in ikhyd package as in the following Figure:
track_vis(sample_trip$gps_data,
api = your_google_api,
zoom = 1)
Visulization of the GPS data of the sample trip by `track_vis()`
The following figure shows the Speed information from GPS sensor stored
in speed_data, which can be visualized with plot_speed() as follows:
plot_speed(sample_trip$speed_data, tripname = "the sample trip")
The result of `plot_speed()` code. It also supports many base plot
arguments such as `xlim` in **R**
Accelerometer data
For accelerometer information, you can plot it by using plot_acc()
function with an option of smoothing parameter rate. Also note that
some base plot options such as xlim can be used.
The figure below is generated by the following code.
plot_acc(sample_trip$acc_data,
rate = 0.2,
tripname = "the sample trip")
The result of `plot_acc()` code. `rate` is the smoothing parameter of
low pass filter whose range between 0 to 1.
Calibration
Kalman filtering and smoothing operation of the sample trip can be
reproduced by the following functions: kalmanfilter_telematics() and
kalmansmooth_telematics().
The visualization of the Kalman filtered and smoothed telematics data
can be done by the plot_telematics() function in the package.
# telematics file for longer trips
obdtrip_path <- system.file("extdata", "trip_with_obd.csv", package = "ikhyd")
obdtrip_path_obd <- system.file("extdata", "trip_with_obd.txt", package = "ikhyd")
Kalman filter
kalmanfilter_result <- kalmanfilter_telematics(obdtrip_path)
head(kalmanfilter_result)
#> time a_lon a_lat speed
#> 1 0.000 -0.07467497 -0.0001588679 -9.638167e-06
#> 2 0.008 -0.07553793 -0.0002927250 -1.418528e-03
#> 3 0.032 -0.06798833 0.0015284343 -2.687559e-03
#> 4 0.070 -0.06708123 0.0025836721 -5.449661e-03
#> 5 0.110 -0.05978903 0.0069970185 -7.162957e-03
#> 6 0.146 -0.05870658 0.0073854722 -1.039758e-02
plot_telematics(kalmanfilter_result)
Visualization of the Kalman filtering based calibration
Kalman smoothing
kalmansmooth_result <- kalmansmooth_telematics(obdtrip_path)
head(kalmansmooth_result)
#> time a_lon a_lat speed
#> 1 0.000 -0.018178654 -0.0009477862 -5.521131e-05
#> 2 0.008 -0.015930304 -0.0027629604 -1.351008e-03
#> 3 0.032 -0.011211523 0.0004643929 -2.445627e-03
#> 4 0.070 -0.011028800 -0.0007278596 -3.118022e-03
#> 5 0.110 -0.005712119 0.0010311522 -3.774068e-03
#> 6 0.146 -0.005964552 -0.0040115553 -3.954390e-03
plot_telematics(kalmansmooth_result)
Visualization of the Kalman smoothing based calibration
Comparison with OBD data
# load OBD trip data for the comparison
obd_trip <- load_telematic_data(obdtrip_path,
all_in_one = TRUE)
# load obd information and calculate acceleration
speed_data_obd <- get_obd_trip(obdtrip_path_obd)
speed_data_obd <- acc_from_obd(speed_data_obd)
Root Mean Square Error (RMSE) of accelerometer (y-axis), Kalman filtered
accelerations, and Kalman smoothed accelerations for the given trip are
calculated as follows:
rmse <- function(x, y){
sqrt(sum((x - y)^2))
}
RMSEresult <- data.frame(
Methods = "RMSE",
Accelerometer = rmse(obd_trip$acc_data$y, speed_data_obd$dv_dt),
KalmanFilter = rmse(kalmanfilter_result$a_lon, speed_data_obd$dv_dt),
KalmanSmooth = rmse(kalmansmooth_result$a_lon, speed_data_obd$dv_dt)
)
knitr::kable(RMSEresult)
| Methods | Accelerometer | KalmanFilter | KalmanSmooth |
|:--------|--------------:|-------------:|-------------:|
| RMSE | 100.877 | 76.94296 | 50.32388 |
OBD based speed and accelaration
Speed comparison: OBD vs. GPS
obd_trip$speed_data$obd_speed <- speed_data_obd$speed
plot_speed(obd_trip$speed_data, col = "red",
tripname = "OBD trip")
Comparion of Speed: GPS(red) vs. OBD(black)
Acceleration comparison: Accelerometer vs. OBD
plot_data <- data.frame(time = obd_trip$acc_data$time,
acc1 = obd_trip$acc_data$y,
acc2 = speed_data_obd$dv_dt)
plot_acc_compare(plot_data,
sensor_name = c("Y-axis accelerometer(red)", "OBD(black)"),
xlim = c(0, 400))
Comparion of Acceleration: Accelerometer(red) vs. OBD(black)
Acceleration comparison: Kalman filtering vs. OBD
plot_data$acc1 <- kalmanfilter_result$a_lon
plot_acc_compare(plot_data,
sensor_name = c("Kalman filtering(red)", "OBD(black)"),
xlim = c(0, 400))
Comparion of Acceleration: Kalman filter base(red) vs. OBD(black)
Acceleration comparison: Kalman smoothing vs. OBD
plot_data$acc1 <- kalmansmooth_result$a_lon
plot_acc_compare(plot_data,
sensor_name = c("Kalman smooth(red)", "OBD(black)"),
xlim = c(0, 400))
Comparion of Acceleration: Kalman smooth base(red) vs. OBD(black)
Heatmap visualization
Here is some example of visualization of telematics data; v-a heatmap
suggested by Wuthrich (2017) and Lee and Shyamal
(2019)
the Lon-Lat plot suggested by The following code generates the v-a
heatmap of the telematics data
# telematics file for driver 1 and 2
driver1_path <- system.file("extdata", "driver1.csv", package = "ikhyd")
driver2_path <- system.file("extdata", "driver2.csv", package = "ikhyd")
telematics_driver1 <- kalmansmooth_telematics(driver1_path)
telematics_driver2 <- kalmansmooth_telematics(driver2_path)
Heatmap (V-A)
draw_vaHeatmap(telematics_driver1)
draw_vaHeatmap(telematics_driver2)
Reproduce V-A heatmap result for 2 drivers
Heatmap (Lon-Lat)
par(mar = c(4, 4, .1, .1))
drawHeatmap(telematics_driver1)
drawHeatmap(telematics_driver2)
Reproduce Lon-Lat plot result for drivers; 1 and 2
License
Provided under the terms of the MIT
License.
Copyright © 2019-2020, Issac Lee.
issactoast/ikhyd documentation built on March 5, 2021, 10:24 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
I Know How You Drive (ikhyd)
R package ikhyd is designed to make the driving behavior analysis
using telematics data easier.
Prerequisite
Package installation and loading
To reproduce the research result, the ikhyd should be installed from
github page, , using the following code:
# packages loading
devtools::install_github("issactoast/ikhyd")
After the installation, load package as follows:
library(ikhyd)
#> To cite this package use citation('ikhyd')
#>
#> Text citation:
#>
#> Issac Lee (2020). ikhyd : I Know How You Drive - Collection of R functions to make telematics analysis easier.
#> R package version 0.1.0. URL https://github.com/issactoast/ikhyd
Google API
To use the visualization functions, the google map aip key should be fed into the function as an argument. Google API can be obtained the Google Maps Platform by following the registration instruction. The google map API service will charge $2 per 1000 requests however, $200 deposite will be added to the account per month. Thus, the personal usage is technically free but you need to register the credit card to use the service.
For example, track_vis() has the following structure:
track_vis(sample_trip$gps_data,
api = your_google_api,
zoom = 1)
How to access to data set
ikhyd package contains seven telematics files;
- Sample route telematics: sample_trip.csv
- Test route with OBD information:
- trip_with_obd.csv
- trip_with_obd.txt
- Driving telematics files:
- driver1.csv
- driver2.csv
- driver3.csv
- driver4.csv
Note that trip_with_obd.txt contains the OBD speed information which
combines with telematics data generated by smartphone,
trip_with_obd.csv.
To get the path of the each files, users can use system.file() in
R. For example, the follow code will give you the path of
sample_trip.csv;
system.file("extdata", "sample_trip.csv", package = "ikhyd")
#> [1] "C:/R/R-4.0.3/library/ikhyd/extdata/sample_trip.csv"
See help and actual code
You can check out the actual code in this instruction by typing the name
of the R console. For example, examination the code of
load_telematic_data() will be as follows:
load_telematic_data
#> function(file_path, all_in_one = FALSE, ...){
#> if (all_in_one){
#> gps_data <- get_trip(file_path, data_option = 1)
#> acc_data <- get_trip(file_path, data_option = 2)
#> lacc_data <- get_trip(file_path, data_option = 3)
#> gyro_data <- get_trip(file_path, data_option = 4)
#> angle_data <- get_trip(file_path, data_option = 5)
#> speed_data <- get_trip(file_path, data_option = 6)
#> result <- list(gps_data = gps_data,
#> acc_data = acc_data,
#> lacc_data = lacc_data,
#> gyro_data = gyro_data,
#> angle_data = angle_data,
#> speed_data = speed_data)
#> return(result)
#> } else {
#> result <- get_trip(file_path, ...)
#> return(result)
#> }
#> }
#> <bytecode: 0x0000000013906a20>
#> <environment: namespace:ikhyd>
Also, the help page of each function is available via
?function_name().
?load_telematic_data()
Load trip data
When you have a path of telematics data, you can load the data using
get_trip() with data_option arguments as follows:
# telematics file path
sample_trip_path <- system.file("extdata", "sample_trip.csv", package = "ikhyd")
# load data
sample_trip <- load_telematic_data(sample_trip_path, all_in_one = TRUE)
summary(sample_trip)
#> Length Class Mode
#> gps_data 6 data.frame list
#> acc_data 4 data.frame list
#> lacc_data 4 data.frame list
#> gyro_data 4 data.frame list
#> angle_data 4 data.frame list
#> speed_data 3 data.frame list
Visualization
GPS data
The snippet of gps_data for the sample trip looks as follows:
head(sample_trip$gps_data)
#> time x y z accuracy_horiz accuracy_vert
#> 1 0.000 -91.53799 41.66253 197.2922 5 4
#> 2 0.038 -91.53799 41.66253 197.2922 5 4
#> 3 0.079 -91.53799 41.66253 197.2821 5 4
#> 4 0.120 -91.53799 41.66253 197.2821 5 4
#> 5 0.153 -91.53799 41.66253 197.2821 5 4
#> 6 0.194 -91.53799 41.66253 197.2821 5 4
The GPS coordinates of the sample trip can be visualized with
track_vis() in ikhyd package as in the following Figure:
track_vis(sample_trip$gps_data,
api = your_google_api,
zoom = 1)
Visulization of the GPS data of the sample trip by `track_vis()`
The following figure shows the Speed information from GPS sensor stored
in speed_data, which can be visualized with plot_speed() as follows:
plot_speed(sample_trip$speed_data, tripname = "the sample trip")
The result of `plot_speed()` code. It also supports many base plot arguments such as `xlim` in **R**
Accelerometer data
For accelerometer information, you can plot it by using plot_acc()
function with an option of smoothing parameter rate. Also note that
some base plot options such as xlim can be used.
The figure below is generated by the following code.
plot_acc(sample_trip$acc_data,
rate = 0.2,
tripname = "the sample trip")
The result of `plot_acc()` code. `rate` is the smoothing parameter of low pass filter whose range between 0 to 1.
Calibration
Kalman filtering and smoothing operation of the sample trip can be
reproduced by the following functions: kalmanfilter_telematics() and
kalmansmooth_telematics().
The visualization of the Kalman filtered and smoothed telematics data
can be done by the plot_telematics() function in the package.
# telematics file for longer trips
obdtrip_path <- system.file("extdata", "trip_with_obd.csv", package = "ikhyd")
obdtrip_path_obd <- system.file("extdata", "trip_with_obd.txt", package = "ikhyd")
Kalman filter
kalmanfilter_result <- kalmanfilter_telematics(obdtrip_path)
head(kalmanfilter_result)
#> time a_lon a_lat speed
#> 1 0.000 -0.07467497 -0.0001588679 -9.638167e-06
#> 2 0.008 -0.07553793 -0.0002927250 -1.418528e-03
#> 3 0.032 -0.06798833 0.0015284343 -2.687559e-03
#> 4 0.070 -0.06708123 0.0025836721 -5.449661e-03
#> 5 0.110 -0.05978903 0.0069970185 -7.162957e-03
#> 6 0.146 -0.05870658 0.0073854722 -1.039758e-02
plot_telematics(kalmanfilter_result)
Visualization of the Kalman filtering based calibration
Kalman smoothing
kalmansmooth_result <- kalmansmooth_telematics(obdtrip_path)
head(kalmansmooth_result)
#> time a_lon a_lat speed
#> 1 0.000 -0.018178654 -0.0009477862 -5.521131e-05
#> 2 0.008 -0.015930304 -0.0027629604 -1.351008e-03
#> 3 0.032 -0.011211523 0.0004643929 -2.445627e-03
#> 4 0.070 -0.011028800 -0.0007278596 -3.118022e-03
#> 5 0.110 -0.005712119 0.0010311522 -3.774068e-03
#> 6 0.146 -0.005964552 -0.0040115553 -3.954390e-03
plot_telematics(kalmansmooth_result)
Visualization of the Kalman smoothing based calibration
Comparison with OBD data
# load OBD trip data for the comparison
obd_trip <- load_telematic_data(obdtrip_path,
all_in_one = TRUE)
# load obd information and calculate acceleration
speed_data_obd <- get_obd_trip(obdtrip_path_obd)
speed_data_obd <- acc_from_obd(speed_data_obd)
Root Mean Square Error (RMSE) of accelerometer (y-axis), Kalman filtered accelerations, and Kalman smoothed accelerations for the given trip are calculated as follows:
rmse <- function(x, y){
sqrt(sum((x - y)^2))
}
RMSEresult <- data.frame(
Methods = "RMSE",
Accelerometer = rmse(obd_trip$acc_data$y, speed_data_obd$dv_dt),
KalmanFilter = rmse(kalmanfilter_result$a_lon, speed_data_obd$dv_dt),
KalmanSmooth = rmse(kalmansmooth_result$a_lon, speed_data_obd$dv_dt)
)
knitr::kable(RMSEresult)
| Methods | Accelerometer | KalmanFilter | KalmanSmooth | |:--------|--------------:|-------------:|-------------:| | RMSE | 100.877 | 76.94296 | 50.32388 |
OBD based speed and accelaration
Speed comparison: OBD vs. GPS
obd_trip$speed_data$obd_speed <- speed_data_obd$speed
plot_speed(obd_trip$speed_data, col = "red",
tripname = "OBD trip")
Comparion of Speed: GPS(red) vs. OBD(black)
Acceleration comparison: Accelerometer vs. OBD
plot_data <- data.frame(time = obd_trip$acc_data$time,
acc1 = obd_trip$acc_data$y,
acc2 = speed_data_obd$dv_dt)
plot_acc_compare(plot_data,
sensor_name = c("Y-axis accelerometer(red)", "OBD(black)"),
xlim = c(0, 400))
Comparion of Acceleration: Accelerometer(red) vs. OBD(black)
Acceleration comparison: Kalman filtering vs. OBD
plot_data$acc1 <- kalmanfilter_result$a_lon
plot_acc_compare(plot_data,
sensor_name = c("Kalman filtering(red)", "OBD(black)"),
xlim = c(0, 400))
Comparion of Acceleration: Kalman filter base(red) vs. OBD(black)
Acceleration comparison: Kalman smoothing vs. OBD
plot_data$acc1 <- kalmansmooth_result$a_lon
plot_acc_compare(plot_data,
sensor_name = c("Kalman smooth(red)", "OBD(black)"),
xlim = c(0, 400))
Comparion of Acceleration: Kalman smooth base(red) vs. OBD(black)
Heatmap visualization
Here is some example of visualization of telematics data; v-a heatmap suggested by Wuthrich (2017) and Lee and Shyamal (2019) the Lon-Lat plot suggested by The following code generates the v-a heatmap of the telematics data
# telematics file for driver 1 and 2
driver1_path <- system.file("extdata", "driver1.csv", package = "ikhyd")
driver2_path <- system.file("extdata", "driver2.csv", package = "ikhyd")
telematics_driver1 <- kalmansmooth_telematics(driver1_path)
telematics_driver2 <- kalmansmooth_telematics(driver2_path)
Heatmap (V-A)
draw_vaHeatmap(telematics_driver1)
draw_vaHeatmap(telematics_driver2)
Reproduce V-A heatmap result for 2 drivers
Heatmap (Lon-Lat)
par(mar = c(4, 4, .1, .1))
drawHeatmap(telematics_driver1)
drawHeatmap(telematics_driver2)
Reproduce Lon-Lat plot result for drivers; 1 and 2
License
Provided under the terms of the MIT License.
Copyright © 2019-2020, Issac Lee.
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