Connected Vehicles: V2V and V2I Road Weather and Traffic Communication Using Cellular Technologies
There is a continuous need to design and develop wireless technologies to meet the increasing demands for high-speed wireless data transfer to incorporate advanced intelligent transport systems. Different wireless technologies are continuously evolving including short-range and long-range (WiMAX, LTE, and 5G) cellular standards. These emerging technologies can considerably enhance the operational performance of communication between vehicles and road-side infrastructure. This paper analyzes the performance of cellular-based long-term evolution (LTE) and 5GTN (5G Test Network) in pilot field measurements (i.e., vehicle-to-vehicle and vehicle-to-infrastructure) when delivering road weather and traffic information in real-time environments. Measurements were conducted on a test track operated and owned by the Finnish Meteorological Institute (FMI), Finland. The results showed that 5GTN outperformed LTE when exchanging road weather and traffic data messages in V2V and V2I scenarios. This comparison was made by mainly considering bandwidth, throughput, packet loss, and latency. The safety critical messages were transmitted at a transmission frequency of 10 Hz. The performance of both compared technologies (i.e., LTE and 5GTN) fulfilled the minimum requirements of the ITS-Assisted Road weather and traffic platform to offer reliable communication for enhanced road traffic safety. The field measurement results also illustrate the advantage of cellular networks (LTE and 5GTN) with a clear potential to use it heterogeneously in future field tests with short-range protocols, e.g., IEEE 802.11p.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/14248220
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Supplemental Notes:
- © Muhammad Naeem Tahir et al.
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Authors:
- Tahir, Muhammad Naeem
- Leviäkangas, Pekka
- Katz, Marcos
- Publication Date: 2022
Language
- English
Media Info
- Media Type: Digital/other
- Pagination: 1142
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Serial:
- Sensors
- Volume: 22
- Issue Number: 3
- Publisher: MDPI AG
- ISSN: 1424-8220
- Serial URL: http://www.mdpi.com/journal/sensors
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Publication flags:
Open Access (libre)
Subject/Index Terms
- TRT Terms: Connected vehicles; Data streaming; Neural networks; Traffic data; Weather forecasting; Wireless communication systems
- Identifier Terms: Long Term Evolution
- Subject Areas: Data and Information Technology; Highways; Vehicles and Equipment;
Filing Info
- Accession Number: 01836979
- Record Type: Publication
- Files: TRIS
- Created Date: Feb 25 2022 8:51AM