|Title||A sampling theorem approach to traffic sensor optimization|
|Publication Type||Journal Article|
|Year of Publication||2008|
|Authors||Ni D, Leow W.L., Pishro-Nik H|
|Journal||IEEE Transactions on Intelligent Transportation Systems|
|Pagination||369 - 374|
|Keywords||Sampling theorem, sensor optimization, spectral domain analysis, Traffic congestion, traffic sensing|
With the objective of minimizing the total cost, which includes both sensor and congestion costs, the authors adopted a novel sampling theorem approach to address the problem of sensor spacing optimization. This paper presents the analysis and modeling of the power spectral density of traffic information as a 2-D stochastic signal using highly detailed field data. The field data were captured by the next-generation simulation (NGSIM) program in 2005. To the best knowledge of the authors, field data with such a level of detail were previously unavailable. The resulting model enables the derivation of a characterization curve that relates sensor error to sensor spacing. The characterization curve, concurring in general with observations of a previous work, provides much more detail to facilitate sensor deployment. Based on the characterization curve and a formulation relating sensor error to congestion cost, the optimal sensor spacing that minimizes the total cost can be determined.