Data Mining for Traffic Prediction and Analysis using Big Data

Rahul Khokale; Ashwini Ghate

doi:https://doi.org/10.14445/22315381/IJETT-V48P227

Research Article | Open Access | Download PDF

Volume 48 | Number 2 | Year 2017 | Article Id. IJETT-V48P227 | DOI : https://doi.org/10.14445/22315381/IJETT-V48P227

Data Mining for Traffic Prediction and Analysis using Big Data

Rahul Khokale, Ashwini Ghate

Citation :

Rahul Khokale, Ashwini Ghate, "Data Mining for Traffic Prediction and Analysis using Big Data," International Journal of Engineering Trends and Technology (IJETT), vol. 48, no. 2, pp. 152-156, 2017. Crossref, https://doi.org/10.14445/22315381/IJETT-V48P227

Abstract

Today we are living in a data-driven world. Developments in data generation, gathering and storing technology have empowered organizations to gather data sets of massive size. Data mining is a term that blends traditional data analysis methods with cultured algorithms to handle the tasks stood by these new forms of data sets. This paper is a comparative analysis of various Data Mining of traffic data using big data, visualization and data mining techniques to predict and analyse traffic. Wireless sensor networks are a technology which has played a massive role enabling a Smarter City cities is using this technology to gather data related to traffic. The objective is to have a complete infrastructure that enable the monitoring of traffic behaviours so decisions on city development can be made in a smarter way. The work exploring the application of data mining tools to support in the progress of traffic signal judgement devices. The cluster analysis approach is able to apply a high-resolution system state description that takes advantage of the wide-ranging set of sensors arranged in a traffic signal system.

Keywords

Data Mining, Time of Day (TOD), Hierarchical Clustering.

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