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 DRIVING BEHAVIOR ANALYSIS ON GPS DATA USING MACHINE LEARNING METHODS

 Bin Ibrahim, Sultan Ibrahim


//uquui/handle/20.500.12248/130903
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DRIVING BEHAVIOR ANALYSIS ON GPS DATA USING MACHINE LEARNING METHODS

Call Number : 24410
Publisher :جامعة أم القرى
Pub Place : مكة المكرمة
Issue Date : 2020 - 1442 H
Description : 96 ورقة.
Format : ماجستير
Language : انجليزي
Is format of : مكتبة الملك عبدالله بن عبدالعزيز الجامعية

Traffic analysis of vehicles in densely populated areas and places of public gathering can provide interesting insights into crowd behavior. Hajj is a spatio-temporally bound religious activity that is held annually and attended by more than 2 million people. More than 17,000 buses are used to transport pilgrims on fixed days to fixed locations. This poses great challenges in terms of crowd management. Using Global Positioning System (GPS) and Automatic Vehicle Location (AVL) sensors attached to buses, a large amount of spatio-temporal vehicle data can be collected for traffic analysis. In this paper, we present a study whereby driver behavior was extracted from an analysis of vehicle big data. We have explained in detail how we collected data, cleaned it, moved it to a big data repository, processed it and extracted information that helped us characterize driver behavior according to our definition of aggressiveness. We have used data from 17,000 buses that has been collected during Hajj 2018.

Title: DRIVING BEHAVIOR ANALYSIS ON GPS DATA USING MACHINE LEARNING METHODS
Authors: Felemban, Emad
Bin Ibrahim, Sultan Ibrahim
Subjects :: Computer Science
Computer systems
Issue Date :: 2020
Publisher :: جامعة أم القرى
Abstract: Traffic analysis of vehicles in densely populated areas and places of public gathering can provide interesting insights into crowd behavior. Hajj is a spatio-temporally bound religious activity that is held annually and attended by more than 2 million people. More than 17,000 buses are used to transport pilgrims on fixed days to fixed locations. This poses great challenges in terms of crowd management. Using Global Positioning System (GPS) and Automatic Vehicle Location (AVL) sensors attached to buses, a large amount of spatio-temporal vehicle data can be collected for traffic analysis. In this paper, we present a study whereby driver behavior was extracted from an analysis of vehicle big data. We have explained in detail how we collected data, cleaned it, moved it to a big data repository, processed it and extracted information that helped us characterize driver behavior according to our definition of aggressiveness. We have used data from 17,000 buses that has been collected during Hajj 2018.
Description :: 96 ورقة.
URI: http://dorar.uqu.edu.sa//uquui/handle/20.500.12248/130903
Appears in Collections :الرسائل العلمية المحدثة

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