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Optimal Sensor Locations for Freeway Bottleneck Identification
Author(s) -
Liu Henry X.,
Danczyk Adam
Publication year - 2009
Publication title -
computer‐aided civil and infrastructure engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.773
H-Index - 82
eISSN - 1467-8667
pISSN - 1093-9687
DOI - 10.1111/j.1467-8667.2009.00614.x
Subject(s) - bottleneck , identification (biology) , computer science , genetic algorithm , real time computing , resource (disambiguation) , nonlinear system , intelligent transportation system , field (mathematics) , mathematical optimization , engineering , transport engineering , machine learning , computer network , embedded system , biology , botany , physics , mathematics , quantum mechanics , pure mathematics
In the field of traffic operations, accurate performance measures are crucial for many of the intelligent transportation systems applications. Achieving this accuracy and quality requires that network‐based roadway sensors are allocated in locations beneficial to traffic operations. However, with the budgetary restrictions most transportation agencies face, these roadway sensors cannot be placed as thoroughly as obligatory for ideal accuracy, requiring these agencies to select a limited number of installments that produce the most optimal results. In this article, a nonlinear integer program is proposed to optimally allocate point sensors along a one‐directional freeway corridor, given that any pair of adjacent sensors can produce a benefit for bottleneck identification. The objective of this model is to optimize the accuracy of bottleneck identification subject to resource and monetary constraints. This model is nonlinear and, due to a non‐differentiable function, genetic algorithm is applied to find a solution. We demonstrate that on a case study network with bottlenecks at unknown locations, the model successfully allocates sensors in a manner that optimizes bottleneck identification accuracy.