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Real-time traffic incidents prediction in vehicular networks using big data analytics
| Title: | Real-time traffic incidents prediction in vehicular networks using big data analytics. |
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| Name(s): |
Al-Najada, Hamzah, author Mahgoub, Imad, Thesis advisor Florida Atlantic University, Degree grantor College of Engineering and Computer Science Department of Computer and Electrical Engineering and Computer Science |
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| Type of Resource: | text | |
| Genre: | Electronic Thesis Or Dissertation | |
| Date Created: | 2018 | |
| Date Issued: | 2018 | |
| Publisher: | Florida Atlantic University | |
| Place of Publication: | Boca Raton, Fla. | |
| Physical Form: | application/pdf | |
| Extent: | 144 p. | |
| Language(s): | English | |
| Abstract/Description: | The United States has been going through a road accident crisis for many years. The National Safety Council estimates 40,000 people were killed and 4.57 million injured on U.S. roads in 2017. Direct and indirect loss from tra c congestion only is more than $140 billion every year. Vehicular Ad-hoc Networks (VANETs) are envisioned as the future of Intelligent Transportation Systems (ITSs). They have a great potential to enable all kinds of applications that will enhance road safety and transportation efficiency. In this dissertation, we have aggregated seven years of real-life tra c and incidents data, obtained from the Florida Department of Transportation District 4. We have studied and investigated the causes of road incidents by applying machine learning approaches to this aggregated big dataset. A scalable, reliable, and automatic system for predicting road incidents is an integral part of any e ective ITS. For this purpose, we propose a cloud-based system for VANET that aims at preventing or at least decreasing tra c congestions as well as crashes in real-time. We have created, tested, and validated a VANET traffic dataset by applying the connected vehicle behavioral changes to our aggregated dataset. To achieve the scalability, speed, and fault-tolerance in our developed system, we built our system in a lambda architecture fashion using Apache Spark and Spark Streaming with Kafka. We used our system in creating optimal and safe trajectories for autonomous vehicles based on the user preferences. We extended the use of our developed system in predicting the clearance time on the highway in real-time, as an important component of the traffic incident management system. We implemented the time series analysis and forecasting in our real-time system as a component for predicting traffic flow. Our system can be applied to use dedicated short communication (DSRC), cellular, or hybrid communication schema to receive streaming data and send back the safety messages. The performance of the proposed system has been extensively tested on the FAUs High Performance Computing Cluster (HPCC), as well as on a single node virtual machine. Results and findings confirm the applicability of the proposed system in predicting traffic incidents with low processing latency. | |
| Identifier: | FA00013114 (IID) | |
| Degree granted: | Dissertation (Ph.D.)--Florida Atlantic University, 2018. | |
| Collection: | FAU Electronic Theses and Dissertations Collection | |
| Note(s): | Includes bibliography. | |
| Subject(s): |
Vehicular ad hoc networks (Computer networks) Big data Intelligent transportation systems Prediction traffic incidents |
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| Held by: | Florida Atlantic University Libraries | |
| Sublocation: | Digital Library | |
| Persistent Link to This Record: | http://purl.flvc.org/fau/fd/FA00013114 | |
| 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. |
