Datasets for traffic congestion management
The document should include the following:
1. Introduction section to introduce the importance of data mining in many fields, then focus on data mining in intelligent transport systems, some facts and historical information.
2. Data Mining for traffic congestion: in this section more details about data mining techniques in transport systems.Data Acquisition that includes the sources of data and how it is collected.
Then Data Preprocessing: including what are the steps and calculations that are needed to be performed to prepare the data for use, the Segmentation, Data Transformation.
The section should include a flow chart representing the data mining techniques and another figure to include the data mining steps.
3. Review the related work on datasets that are used in traffic congestion management solutions : this includes the state of the art machine learning based solutions that use different types of dataset.
This section should include the types of data that are used such as speed, GPS, density, number of vehicles, time of days, peak and non peak hours as so on.
The total size of these data and the format of the data is it Video, CSV, XML and so on, what is the Maine source that form this data, does it come from speed detectors, inductive loops, GPS systems. The section must include a table that summaries the content of this section , the table can include the name of the datasets and where it is used and the availability for use is it a commercial data or open source data. Some figures to compare the total type of data that are used. The figures can be the total number of publications that use speed data, or the total number of publications that use a specific data sets.
4. Conclusion: a summary of the work.
