Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/6135
Title: A Real-Time Autonomous Highway Accident Detection Model Based on Big Data Processing and Computational Intelligence
Authors: Özhayoğlu, Mural
Küçükayan, Gökhan
Doğdu, Erdoğan
Keywords: Traffic flow
big data
accident detection
intelligent transportation systems
neural networks
nearest neighbor
regression tree
computational intelligence
machine learning
IoT
sensors
Issue Date: 2016
Publisher: IEEE
Source: 4th IEEE International Conference on Big Data (Big Data) -- DEC 05-08, 2016 -- Washington, DC
Abstract: Due to increasing urban population and growing number of motor vehicles, traffic congestion is becoming a major problem of the 21st century. One of the main reasons behind traffic congestion is accidents which can not only result in casualties and losses for the participants, but also in wasted and lost time for the others that are stuck behind the wheels. Early detection of an accident can save lives, provides quicker road openings, hence decreases wasted time and resources, and increases efficiency. In this study, we propose a preliminary real-time autonomous accident-detection system based on computational intelligence techniques. Istanbul City traffic-flow data for the year 2015 from various sensor locations are populated using big data processing methodologies. The extracted features are then fed into a nearest neighbor model, a regression tree, and a feed-forward neural network model. For the output, the possibility of an occurrence of an accident is predicted. The results indicate that even though the number of false alarms dominates the real accident cases, the system can still provide useful information that can be used for status verification and early reaction to possible accidents.
URI: https://hdl.handle.net/20.500.11851/6135
ISBN: 978-1-4673-9005-7
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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