• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

Big Data Analysis and Prediction of Traffic in Los Angeles

Vol. 14, No. 2, February 29, 2020
10.3837/tiis.2020.02.021, Download Paper (Free):

Abstract

The paper explains the method to process, analyze and predict traffic patterns in Los Angeles county using Big Data and Machine Learning. The dataset is used from a popular navigating platform in the USA, which tracks information on the road using connected users’ devices and also collects reports shared by the users through the app. The dataset mainly consists of information about traffic jams and traffic incidents reported by users, such as road closure, hazards, accidents. The major contribution of this paper is to give a clear view of how the large-scale road traffic data can be stored and processed using the Big Data system - Hadoop and its ecosystem (Hive). In addition, analysis is explained with the help of visuals using Business Intelligence and prediction with classification machine learning model on the sampled traffic data is presented using Azure ML. The process of modeling, as well as results, are interpreted using metrics: accuracy, precision and recall.


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Cite this article

[IEEE Style]
D. Dauletbak and J. Woo, "Big Data Analysis and Prediction of Traffic in Los Angeles," KSII Transactions on Internet and Information Systems, vol. 14, no. 2, pp. 841-854, 2020. DOI: 10.3837/tiis.2020.02.021.

[ACM Style]
Dalyapraz Dauletbak and Jongwook Woo. 2020. Big Data Analysis and Prediction of Traffic in Los Angeles. KSII Transactions on Internet and Information Systems, 14, 2, (2020), 841-854. DOI: 10.3837/tiis.2020.02.021.