You are here

REAL-TIME HIGHWAY TRAFFIC FLOW AND ACCIDENT SEVERITY PREDICTION IN VEHICULAR NETWORKS USING DISTRIBUTED MACHINE LEARNING AND BIG DATA ANALYSIS

Download pdf | Full Screen View

Date Issued:
2022
Abstract/Description:
In recent years, Florida State recorded thousands of abnormal traffic flows on highways that were caused by traffic incidents. Highway traffic congestion costed the US economy 101 billion dollars in 2020. Therefore, it is imperative to develop effective real-time traffic flow prediction schemes to mitigate the impact of traffic congestion. In this dissertation, we utilized real-life highway segment-based traffic and incident data obtained from Florida Department of Transportation (FDOT) for real-time incident prediction. We used eight years of FDOT real-life traffic and incident data for Florida I-95 highway to build prediction models for traffic accident severity. Accurate severity prediction is beneficial for responders since it allows the emergency center to dispatch the right number of vehicles without wasting additional resources.
Title: REAL-TIME HIGHWAY TRAFFIC FLOW AND ACCIDENT SEVERITY PREDICTION IN VEHICULAR NETWORKS USING DISTRIBUTED MACHINE LEARNING AND BIG DATA ANALYSIS.
54 views
32 downloads
Name(s): Alnami, Hani Mohammed , author
Mahgoub, Imadeldin, Thesis advisor
Florida Atlantic University, Degree grantor
Department of Computer and Electrical Engineering and Computer Science
College of Engineering and Computer Science
Type of Resource: text
Genre: Electronic Thesis Or Dissertation
Date Created: 2022
Date Issued: 2022
Publisher: Florida Atlantic University
Place of Publication: Boca Raton, Fla.
Physical Form: application/pdf
Extent: 128 p.
Language(s): English
Abstract/Description: In recent years, Florida State recorded thousands of abnormal traffic flows on highways that were caused by traffic incidents. Highway traffic congestion costed the US economy 101 billion dollars in 2020. Therefore, it is imperative to develop effective real-time traffic flow prediction schemes to mitigate the impact of traffic congestion. In this dissertation, we utilized real-life highway segment-based traffic and incident data obtained from Florida Department of Transportation (FDOT) for real-time incident prediction. We used eight years of FDOT real-life traffic and incident data for Florida I-95 highway to build prediction models for traffic accident severity. Accurate severity prediction is beneficial for responders since it allows the emergency center to dispatch the right number of vehicles without wasting additional resources.
Identifier: FA00014089 (IID)
Degree granted: Dissertation (PhD)--Florida Atlantic University, 2022.
Collection: FAU Electronic Theses and Dissertations Collection
Note(s): Includes bibliography.
Subject(s): Traffic flow
Traffic accidents
Machine learning
Big data
Traffic estimation
Persistent Link to This Record: http://purl.flvc.org/fau/fd/FA00014089
Use and Reproduction: Copyright © is held by the author with permission granted to Florida Atlantic University to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.
Use and Reproduction: http://rightsstatements.org/vocab/InC/1.0/
Host Institution: FAU
Is Part of Series: Florida Atlantic University Digital Library Collections.