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- W3007788144 abstract "Traffic data collection and information extraction have been a wide area of study for various objectives. One such objective is to predict the nature of traffic in a particular road region followed by its visualization. The primary objective of this paper is to analyze the traffic big data using two comparative parallel algorithms of M5P rules and random forest regression for determining the average journey time based on other parameters related to nature of traffic such as flow, time of the day. These algorithms have been implemented in a distributed computing environment in Spark clusters using Apache Mesos resource management. The secondary objective of the paper is to visualize the correlation of average journey time with the flow of traffic and plotting comparative graphs for the real and predicted values of the average journey time. Based on root-mean-square error, mean absolute error, and other performance parameters like correlation coefficient, this paper concludes that parallel algorithms fared better in terms of prediction accuracy and error rates than traditional regression methods." @default.
- W3007788144 created "2020-03-06" @default.
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- W3007788144 date "2020-01-01" @default.
- W3007788144 modified "2023-09-26" @default.
- W3007788144 title "Analysis of Parallel M5P and Random Forest Regression for Visualization of Traffic Behavior" @default.
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- W3007788144 doi "https://doi.org/10.1007/978-981-15-2449-3_19" @default.
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