Optimal sensor locations for freeway bottleneck identification

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.