Optimal Sensor Locations for Freeway Bottleneck Identification

Abstract

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.

Comparison of hook-turn scheme with U-turn scheme based on actuated traffic control algorithm

“…Loop detector is an applicable and effective means to instantly look into the spillback phenomenon of the HT vehicles, in order to dynamically adjust the signal setting plan (Liu and Danczyk 2009). In this paper, we adopt three types of loop detectors, as indicated in Figure 2.…”

Designing heterogeneous sensor networks for estimating and predicting path travel time dynamics: An information-theoretic modeling approach

“…In sensor location models for the second approach, significant attention (e.g., Liu and Danczyk, 2009, Danczyk and Liu, 2011, and Leow et al, 2008 has been devoted to placing point detectors along a freeway corridor to minimize the traffic measurement errors of critical traffic state variables, such as segment density and flow. The traffic origin-destination (OD) matrix estimation problem is also closely related to the travel time estimation problem under consideration.…”

Optimal Spacing of Traffic Counting Stations on a High-Volume Highway Based on Queuing Models

“…Henry X. Liu (2009) also studied the optimal sensor locations for freeway bottleneck identification. He proposed a nonlinear integer program 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.…”