Analysis and Forecast of Traffic Accident Big Data
نویسندگان
چکیده
منابع مشابه
Data Mining for Traffic Prediction and Analysis using Big Data
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 traffi...
متن کاملAnalysis of big data set of urban traffic data
Modern vehicles are increasingly capable of reporting location and status information in real time using GPSenabled on-board telemetry boxes which connect directly into a vehicle’s control and diagnostic systems. We perform an exploratory analysis of such data obtained from a vehicle operating in a large UK urban area. The primary objective is to devise informative summary statistics that allow...
متن کاملA Perspective Analysis of Traffic Accident using Data Mining Techniques
Data Mining is taking out of hidden patterns from huge database. It is commonly used in a marketing, surveillance, fraud detection and scientific discovery. In data mining, machine learning is mainly focused as research which is automatically learnt to recognize complex patterns and make intelligent decisions based on data. Nowadays traffic accidents are the major causes of death and injuries i...
متن کاملA Perspective Analysis of Traffic Accident using Data Mining Techniques
Data Mining is taking out of hidden patterns from huge database. It is commonly used in a marketing, surveillance, fraud detection and scientific discovery. In data mining, machine learning is mainly focused as research which is automatically learnt to recognize complex patterns and make intelligent decisions based on data. Nowadays traffic accidents are the major causes of death and injuries i...
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ژورنال
عنوان ژورنال: ITM Web of Conferences
سال: 2017
ISSN: 2271-2097
DOI: 10.1051/itmconf/20171204029