An IoT Cloud System for Traffic Monitoring and Vehicular Accidents Prevention Based on Mobile Sensor Data Processing
Abstract
The sudden traffic slowdown especially in fast scrolling roads and highways characterized by a scarce visibility is one of the major causes of accidents among motorized vehicles. It can be caused by other accidents, work-in-progress on roads, excessive motorized vehicles especially at peak times and so on. Typically, fixed traffic sensors installed on roads that interact with drivers' mobile App through the 4G network can mitigate such a problem, but unfortunately not all roads and highways are equipped with such devices. In this paper, we discuss a possible alternative solution for addressing such an issue considering mobile traffic sensors directly installed in private and/or public transportation and volunteer vehicles. In this scenario a fast real-time processing of big traffic data is fundamental to prevent accidents. In particular, we discuss an IoT Cloud system for traffic monitoring and alert notification based on OpenGTS and MongoDB. Our IoT Cloud system, besides for private drivers, it is very useful for drivers of critical rescue vehicles such as ambulances. Experiments prove that our system provides acceptable response times that allows drivers to receive alert messages in useful time so as to avoid the risk of possible accidents.
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[72] proposes an IoT cloud platform that enables traffic visualization and early notifications about sudden slowdowns that can lead to accidents.
“The authors in the research [72] propose an IoT cloud platform that will enable traffic visualization and early notifications about sudden slowdowns that can lead to accidents.”
From A Review of Machine Learning and IoT in Smart Transportation (2019) · cites this paper as [72]
The system of [72] achieves sending an alert for a 1 km distance in about 120 ms.
“the suggested systems achieves sending an alert for a 1 km distance in about 120 ms.”
From A Review of Machine Learning and IoT in Smart Transportation (2019) · cites this paper as [72]
The suggested solution can transmit an alert over a distance of 1 km in a little under 120 ms.
“The system response time is a crucial factor in the implementation’s success, and the suggested solution can transmit an alert over a distance of 1 km in a little under 120 ms.”
From Smart Transportation: An Overview of Technologies and Applications (2023) · cites this paper as [37]
An IoT cloud platform can enable traffic visualization and early alerts about unexpected slowdowns that could cause accidents.
“The research’s authors [37] suggest an IoT cloud platform to enable traffic visualization and early alerts about unexpected slowdowns that could cause accidents.”
From Smart Transportation: An Overview of Technologies and Applications (2023) · cites this paper as [37]
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 · see citing works
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 · see citing works
Lightweight Deep Learning Based Intelligent Edge Surveillance Techniques
Zhao, Y., Yin, Y., & Gui, G. (2020) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2020.2999479 · Cited by 76 · see citing works
- Authors
- Celesti, A., Galletta, A., Carnevale, L., Fazio, M., Lay-Ekuakille, A., & Villari, M.
- Affiliations
- University of Messina (IT)
- University of Salento (IT)
- ORCID
- Venue
- IEEE Sensors Journal
- Published
- 2018-06-15
- Volume
- 18
- Issue
- 12
- Pages
- 4795-4802
- ISSN
- 1530-437X (ISSN-L)
- 1558-1748
- 2379-9153
- OpenAlex
- W2769233938
- Cited by
- 165
- References
- 0
- Citations by year
- 2026: 8 · 2025: 13 · 2024: 12 · 2023: 29 · 2022: 25 · 2021: 24 · 2020: 27 · 2019: 17 · 2018: 10
- FWCI
- 11.48
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Sensors Journal (publishedVersion)
- AIRUS (submittedVersion)
- Topics
- Physical Sciences › Engineering › Mechanical Engineering › IoT and GPS-based Vehicle Safety Systems
- Physical Sciences › Engineering › Control and Systems Engineering › Traffic control and management
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Keywords
- traffic monitoring, mobile sensor data, Internet of Things, cloud computing, traffic alerts, MongoDB, 4G
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