Application of Big Data Analysis with Decision Treefor Road Accident
Open Access
- 1 February 2017
- journal article
- Published by Indian Society for Education and Environment in Indian Journal of Science and Technology
- Vol. 10 (29), 1-10
- https://doi.org/10.17485/ijst/2017/v10i29/117325
Abstract
Objectives: In transportation field, a huge amount of data collected by IoT systems, remote sensing and other data collection tools brings new challenges, the size of this data becomes extremely big and more complex for traditional techniques of data mining. To deal with this challenge, Apache Spark stand as a powerful large scale distributed computing platform that can be used successfully for machine learning against very large databases. This work employed large-scale machine learning techniques especially Decision Tree with Apache Spark framework for big data analysis to build a model that can predict the factors lead to road accidents based on several input variables related to traffic accidents. Based on this, the predicting model first preprocesses the big accident data and analyze it to create data for a learning system. Empirical results show that the proposed model could provide new information that can assist the decision makers to analyze and improve road safetyKeywords
This publication has 17 references indexed in Scilit:
- Community structure mining in big data social media networks with MapReduceCluster Computing, 2015
- Mining association rules in big data with NGEPCluster Computing, 2015
- Kernel density estimation and K-means clustering to profile road accident hotspotsAccident Analysis & Prevention, 2009
- Comparison of Methodology Approach to Identify Causal Factors of Accident SeverityTransportation Research Record: Journal of the Transportation Research Board, 2008
- Diagnostic analysis of the logistic model for pedestrian injury severity in traffic crashesAccident Analysis & Prevention, 2007
- Analysis of traffic injury severity: An application of non-parametric classification tree techniquesAccident Analysis & Prevention, 2006
- Roadway safety in rural and small urbanized areasAccident Analysis & Prevention, 2001
- Pattern recognition for road traffic accident severity in KoreaErgonomics, 2001
- Combining non-parametric models with logistic regression: an application to motor vehicle injury dataComputational Statistics & Data Analysis, 2000
- Mining association rules between sets of items in large databasesPublished by Association for Computing Machinery (ACM) ,1993