Optimization of network sensor location for full link flow observability considering sensor measurement error

Abstract Full link flow observability problem is to identify the minimum set of links to be installed with traffic sensors in a vehicular traffic network, so that unobserved link flows can be fully inferred from the observed link flows based on the correlation between links. Inevitably, the observed link flows are subject to measurement errors, which will accumulate and propagate in the inference of unobserved link flows. This study aims to identify the minimum observed link set for the installation of counting sensors to reach full link flow observability, while minimizing the effect of sensor measurement errors on the inference of unobserved link flows. We propose a network sensor location model considering the propagation of measurement errors in the link flow inference process. Mathematically, we formulate the problem as min–max and min-sum integer linear programs. The objective function attempts directly to minimize the accumulated inference errors caused by the propagation of sensor measurement errors. When the sensors have the uniform measurement error, it is equivalent to minimizing the maximum or cumulative number of observed links required for the link flow inference of each or all unobserved links. The genetic algorithm (GA) is adopted to solve the proposed optimization model, and the results commend the optimal sensor location scheme with the minimum inference error for full link flow observability. The approach is useful for deploying long-term planning and link-based applications in traffic networks.