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A Sampling Theorem Approach to Traffic Sensor Optimization

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3 Author(s)
Leow, W.L. ; Dept. of Electr. & Comput. Eng., Univ. of Massachusetts, Amherst, MA ; Daiheng Ni ; Pishro-Nik, H.

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.

Published in:

Intelligent Transportation Systems, IEEE Transactions on  (Volume:9 ,  Issue: 2 )