The following Jupyter Notebook is in charge of the analysis for the smart card systematic mapping review. This systematic mapping review aims to comprehensively examine the current state-of-the-art in using smart cards for analytical studies applied to public transportation research. The review focuses on identifying and analyzing the main analytical purposes, algorithmic approaches, methods, datasets, and trends employed in these studies.
The methodology followed the PRISMA statement for systematic reviews. This code is used for obtaining the results after extracting the data from the selected studies.
Python 3.11 was used for the analysis and the libraries and dependencies are defined in requirements.txt.
The main libraries used are:
Two sources of data available in the datasets folder:
- Extracted data from the selected studies.
datasets/Cleaned_data.xlsx - Bibliometric data from online databases.
datasets/savedrecs.txt
Some results are included in the Jupyter Notebook, like latex tables and extracted summaries, others like plots and diagrams are in the images folder
Click the on the "Binder" badge above to open an interactive Jupyter environment with all required software installed.
Install Python 3.11, download the repository, open a command line in the root of the directory and install the required software by executing:
pip install -r requirements.txtInstall Docker for building an image based on a Dockerfile with a Jupyter environment and running a container based on the image.
Download the repository, open a command line in the root of the directory and:
- Build the image:
docker build . --tag smat-card-systematic-mapping-review- Run the image:
docker run -it -p 8888:8888 smat-card-systematic-mapping-review- Click on the login link (or copy and paste in the browser) shown in the console to access to a Jupyter environment.
Open the Jupyter Notebook (Analysis.ipynb) file. The notebook contains the used code and its outputs. You can execute the code to reproduce the obtained results.
Note
When executing an analysis with a component of randomness (i.e., ML models training), the obtained results could be slightly different than the reported ones. Notwithstanding, the conclusions should be similar as the reported ones.
Warning
osmnx library is used for searching the locations of the locations of the institutions of the authors' affiliations. It generates a local cache of the locations, it seems that deleting the cache after some time might require editing the specific cell, because Open Street Map might introduce changes and make it difficult to locate some institutions.
PLEASE REVISE THIS CELL AND MANUALLY MODIFY IF NECESSARY