The DOI matches its Crossref record.
A real-time autonomous highway accident detection model based on big data processing and computational intelligence
Open accessAbstract
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.
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“Real-time autonomous accident detection is performed in [29], by analyzing data obtained from road sensors with an FF-NN, a Regression Tree, and the k-NN algorithms.”
From A Review of Machine Learning and IoT in Smart Transportation (2019) · cites this paper as [29]
In the citing paper
“However, there is a significant number of false positive estimations.”
From A Review of Machine Learning and IoT in Smart Transportation (2019) · cites this paper as [29]
In the citing paper
“The results show that all the models are able to identify accidents with great accuracy.”
From A Review of Machine Learning and IoT in Smart Transportation (2019) · cites this paper as [29]
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Cited by
Sorted by citations
A Review of Machine Learning and IoT in Smart Transportation
Zantalis, F., Koulouras, G., Karabetsos, S., & Kandris, D. (2019) · Future Internet · doi:10.3390/fi11040094 · Cited by 625
Smart Transportation: An Overview of Technologies and Applications
Oladimeji, D., Gupta, K., Kose, N. A., Gundogan, K., Ge, L., & Liang, F. (2023) · Sensors · doi:10.3390/s23083880 · Cited by 386
Pillai, M. S., Chaudhary, G., Khari, M., & Crespo, R. G. (2021) · Soft Computing · doi:10.1007/s00500-021-05576-w · Cited by 93
Metadata
- Authors
- Ozbayoglu, M., Kucukayan, G., & Dogdu, E.
- Venue
- 2016 IEEE International Conference on Big Data (Big Data)
- Publisher
- IEEE
- Published
- 2016-12
- Pages
- 1807-1813
- OpenAlex
- W2583901265
- Cited by
- 114
- References
- 0
- Citations by year
- 2026: 4 · 2025: 14 · 2024: 22 · 2023: 15 · 2022: 18 · 2021: 15 · 2020: 11 · 2019: 10 · 2018: 2 · 2017: 3
- FWCI
- 9.44
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE International Conference on Big Data (BigData) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Social Sciences › Transportation › Traffic Prediction and Management Techniques
- Physical Sciences › Engineering › Automotive Engineering › IoT and GPS-based Vehicle Safety Systems
- Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Traffic and Road Safety
- Keywords
- traffic congestion, big data processing, computational intelligence, nearest-neighbor model, regression trees, feedforward neural networks, traffic flow data, sensor data, false alarms
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