A spatial model of received signal strength indicator values for automated collision notification technology

This paper reports on a spatial model that can be used to support evaluation of the effectiveness of automated collision notification (ACN) technology. Such systems rely on wireless phones to transmit their crash messages. However, cellular coverage exhibits great variation in hilly and rural areas,...

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Bibliographic Details
Published in:Transportation research. Part C, Emerging technologies Emerging technologies, 2005-10, Vol.13 (5), p.432-447
Main Authors: Delmelle, Eric M., Rogerson, Peter A., Akella, Mohan R., Batta, Rajan, Blatt, Alan, Wilson, Glenn
Format: Article
Language:English
Subjects:
Applied sciences
Automated collision notification (ACN)
Exact sciences and technology
Geographical Information System (GIS)
Ground, air and sea transportation, marine construction
Kriging
Linear regression
Received signal strength indicator (RSSI)
Road transportation and traffic
Spatial regression
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Summary:This paper reports on a spatial model that can be used to support evaluation of the effectiveness of automated collision notification (ACN) technology. Such systems rely on wireless phones to transmit their crash messages. However, cellular coverage exhibits great variation in hilly and rural areas, and these are regions where ACN devices provide their greatest benefits. In this context, the accurate prediction of cell phone signal strength is of paramount importance and hence, an in-depth analysis of the variables influencing the cellular signal is necessary. In this paper, we first model the spatial variation of cell phone coverage using an interpolation method known as kriging. We focus on how particular factors such as slope, altitude, distance to the cell tower and topographic obstruction are used to improve the prediction of cell phone signal strength. The effect of these factors is evaluated in a multiple regression framework, followed by an analysis of the residuals, providing valuable information for cellular network providers. However, since the residuals show some spatial structure, we therefore analyze the merits of a more suitable method, namely spatial regression. We illustrate the approach using data on cell phone coverage in a hilly section of Erie county, New York. The model should (a) enhance our knowledge of the effect of particular factors on signal strength intensity, and (b) result in improved estimates of RSSI at unknown locations. The latter is important in establishing the effectiveness of automated collision notification (ACN) technology.
ISSN:0968-090X
1879-2359
DOI:10.1016/j.trc.2005.12.001