Authors: Mohammad Mahdi Bejani, Mehdi Ghatee
Affiliations: Department of Computer Science, Amirkabir University of Technology, Tehran, Iran
Contact email: ghatee@aut.ac.ir
Abstract
The data presented in this dataset are related to the research article entitled
Bejani, M. M., & Ghatee, M. (2018). A context aware system for driving style evaluation by an ensemble learning on smartphone sensors data. Transportation Research Part C: Emerging Technologies, 89, 303-320.
Please refer to this paper for each usage.
| Subject area | Intelligent Transportation Systems |
| More specific subject area | Driver’s Style Evaluation |
| Type of data | Three types of files including MATLAB and WEKA codes and Microsoft EXCEL data |
| How data was acquired | The entire of dataset was collected by AndroSensor application for smartphones (Android OS) |
| Data format | Raw and analyzed |
| Experimental factors | Speed data that were achieved based on GPS rarely have null value, and then we use Haversine and average to treat them. |
| Experimental features | acceleration, magnetic, orientation, sound level and location |
| Data source location | Tehran, Iran |
| Data accessibility | This dataset is collected for the article |
This package includes five different datasets, which here is a guide for how to run them on Matlab and Weka.
Smartphone-Calibration
This folder consists of four code-files (*.m) and a data-file (*.xlsx). For execution, main.m file should be opened in Matlab R2016a and run it. Index variable could be changed (as default is 330) for testing in new point.
Car-Classification
This folder consists of seven code-files (*.m), two matlab-data-file (*.mat), and there are raw data files (*.xlsx) in raw-data folder that is used for learning process. The class label of data given in each file is mentioned in its name, i.e., two class labels are soft (LSC) and hard (HSC). For execution, “main.m” file should be opened in Matlab R2016a and run it.
Initial-Style-Classification
This folder consists of six code-files (*.m), a data-file (*.mat), and there are raw data files (*.xlsx) in raw-data folder that is used for learning process. The class of each file is mentioned in its name. These labels are bad (dangerous driving) and good (normal driving). For execution, “ensembleLearningCrossValTest.m” file should be executed in Matlab R2016a. For extracting features from raw file, open main.m in Matlab R2016a and edit line 4 (as input file) and 42 (as output file).
Traffic-Classification
This folder consists of four code-files (*.m), five data-files (*.mat), and there are raw data files (*.xlsx) in raw-data folder that is used for learning process. The class label of each file is mentioned in its name including idle (Normal Traffic Condition) and traffic (Congested Traffic Condition). For execution, “crossValTest.m” file should be opened in Matlab R2016a and run it. For changing dataset, edit line 3 (edit data-set name such as data-5.mat, data-10.mat, data-15.mat, data-20.mat, data-25.mat, data-30.mat, or data-40.mat). For extracting features from raw file, open main.m in Matlab R2016a and edit lines 3 (as input file) and 26 (as output file). For changing window length, edit the value of “windowLen” variable (default value is 50).
Maneuver-Classification
This folder has three code-files (*.m), a data-file (*.arff), and there are raw data files (*.xlsx) in raw-data folder that is used for learning process. The learning process is done by Weka.
Experimental Design, Materials and Methods
The datasets were collected by a smartphone. Two experts labeled the recorded data. The datasets were collected by AndroSensor application. To download this dataset, click here.
This dataset are used for driving evaluation in the following references:
M.M. Bejani, M. Ghatee, A context aware system for driving style evaluation by an ensemble learning on smartphone sensors data, Transportation Research Part C: Emerging Technologies 89, (2018) 303-320.
M.M. Bejani, M. Ghatee, Convolutional neural network with adaptive regularization to classify driving styles on smartphones, IEEE transactions on intelligent transportation systems 21 (2), (2019) 543-552.
M.M. Bejani, M. Ghatee, Theory of adaptive SVD regularization for deep neural networks, Neural Networks 128, (2020) 33-46.