TrafficBDE

Aikaterini Chatzopoulou July 27, 2017

Intoduction

This package was created in order to enable the creation of a neural network model, for the needs of a European project. “TrafficBDE” includes functions for properly formulating the data, training the neural network and predicted the wanted variable. This document introduces you to TrafficBDE’s basic set of tools.

The user should use only the loadData and the kStepsForward functions. The first one to load the historical data and the second for the computation of the predicted value.

Install Package

In order to install TrafficBDE, you should use the following code.

install.packages("devtools")
devtools::install_github("okgreece/TrafficBDE")

Input

The input dataset of the main function could be a link, a csv, an excel file. There are different parameters that a user could specify and interact with the results. The parameters: “path”, “Link_id”, “direction”, “datetime”, “predict” and “steps” should be defined by the user, to form the dataset. Then an automated process formulates the data in order to provide the prediction of the wanted variable for the desired time and road.

Input	Description
path	The path containing the historical data
Link_id	The Link_id of the road
dimension	The dimension of the road
datetime	The date time for the pediction. The format of the datetime should be ‘%Y-%m-%d %H:%M:%S’
predict	The argument to be predicted, appropriate values: “Mean_speed”, “Entries”, “Stdev_speed”
steps	How many steps forward the prediction will be

A sort description about the inputs.

Output

The output of this process is a matrix with the predicted and real values and the RMSE. The rows are equal to the steps.

Examples

Simple examples the kStepsForward function are provided, in order for the user to understand the use and how to deal with these function.

The sample of the dataset that is being used is available in TrafficBDE package and represents the traffic fload of the road with Link_id: “163204843”, for January 2017.

The first example provides, in one step, the prediction of the Mean speed at 14.00 on 27 Jan. 2017

library(TrafficBDE)
Data <- X163204843_1

kStepsForward(Data = Data, Link_id = "163204843", direction = "1", datetime = "2017-01-27 14:00:00", predict = "Mean_speed", steps = 1)

## Training...

## Loading required package: lattice

## Loading required package: ggplot2

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## Aggregating results
## Selecting tuning parameters
## Fitting layer1 = 4, layer2 = 4, layer3 = 4 on full training set
## Training Completed.
## 
## Time taken for training:  2.574222
## Predicting Mean_speed for the Next Quarter...

##                     Predicted Real Value     RMSE
## 2017-01-27 14:00:00  39.36151         29 10.36151

The second example provides, in one step, the prediction of the Entries at 20.00 on 15 Jan. 2017

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## Aggregating results
## Selecting tuning parameters
## Fitting layer1 = 4, layer2 = 3, layer3 = 4 on full training set
## Training Completed.
## 
## Time taken for training:  2.062091
## Predicting Entries for the Next Quarter...

##                     Predicted Real Value       RMSE
## 2017-01-15 20:00:00  1.012089          1 0.01208873

Github:

https://github.com/okgreece/TrafficBDE

Name		Name	Last commit message	Last commit date
Latest commit History 74 Commits
R		R
data		data
man		man
vignettes		vignettes
.Rbuildignore		.Rbuildignore
.gitattributes		.gitattributes
.gitignore		.gitignore
.travis.yml		.travis.yml
DESCRIPTION		DESCRIPTION
HIT_logo_Full_en.png		HIT_logo_Full_en.png
LICENSE		LICENSE
NAMESPACE		NAMESPACE
NEWS.Rmd		NEWS.Rmd
NEWS.html		NEWS.html
README.md		README.md
README.rmd		README.rmd
TrafficBDE.Rproj		TrafficBDE.Rproj
big-data-europe.png		big-data-europe.png
okfgr.png		okfgr.png

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TrafficBDE

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Examples

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TrafficBDE

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