Optimization of traffic count locations for estimation of stochastic origin-destination demands under uncertainty with sensor failure

Stochastic OD demands are usually estimated from the link flows observed by traffic counting sensors over time. Unavoidably, traffic counting sensors located in the road network are subject to failure such that these links with failed sensors are not capable to obtain the link flows. This paper addresses the traffic count location optimization problem considering sensor failure to estimate mean and covariance of OD demands. The information loss of stochastic OD demands due to failed sensors can be quantified by the proposed criteria. Based on these criteria, the traffic count locations are optimized to minimize the information loss of stochastic OD demand estimates considering the uncertainty of sensor failure. To solve the proposed integer programming model, the Genetic Algorithm (GA) is used. Numerical examples are presented to demonstrate the effects of sensor failure on the estimation accuracy of stochastic OD demands.