Vendor-independent reliability testing model for vehicle-to-infrastructure communications Article

Elzahraa Madkour, F, Mohammad, U, Sorour, S et al. (2020). Vendor-independent reliability testing model for vehicle-to-infrastructure communications . 2674(9), 898-912. 10.1177/0361198120932910

cited authors

  • Elzahraa Madkour, F; Mohammad, U; Sorour, S; Hefeida, M; Abdel-Rahim, A

fiu authors

abstract

  • This paper describes a vendor-independent reliability testing approach for vehicle-to-infrastructure (V2I) communications in connected vehicle traffic signal system applications. It provides an alternative to using the communication data reported by proprietary vendor-supplied interfaces. This approach was based on building a rigorously tested translation model that uses measured received signal strength indicator (RSSI) from any V2I communication equipment to predict the corresponding packet delivery ratio (PDR). This was achieved by correlating the signal strength, measured using a generic power meter, to PDR values reported in the communication interface of the equipment of different vendors. Both stationary and in-motion (10–40 mph) field data collection tests were conducted at three intersections. These tests were performed over distances of up to 500m between the road-side units (RSUs) and the on-board units (OBUs). In each test, the RSSI values for line-of-sight packet exchange between various RSUs and OBUs was collected in the field, using both a generic power meter and vendorspecific tools. Next, the results were statistically analyzed and logistic and linear regression models that predict PDR values were developed. A case study to test and validate this new PDR prediction model was conducted at two intersections in Boise, Idaho. This prediction model will enable transportation system operators to test and validate the efficiency of connected vehicle RSU/OBU communications at signalized intersection approaches under different traffic conditions, independent of vendor-provided tools.

publication date

  • July 3, 2020

Digital Object Identifier (DOI)

start page

  • 898

end page

  • 912

volume

  • 2674

issue

  • 9