Optimization of Traffic Counting Location Considering Demand Uncertainty: Evaluation by Multi-agent-based Traffic Simulation

The multi-agent-based traffic simulation is useful to evaluate traffic policies with detailed resolution. To evaluate them feasibly, not only the validity of the simulation model but also the accuracy of the input data is important. The traffic demand is one of the important input data, which is described as the set of Origin-Destination (OD) traffic volume and is estimated by OD estimation. In the OD estimation, the location of the traffic counting points plays an important role, which affects the estimation results largely, thus the traffic counting location optimization has been developed. Existing methods target capturing more information for the OD estimation, that is the location where the most OD pairs can be captured is selected. However, since they do not consider the difficulty of the estimation, the reproduction of the traffic volume in the assumed location are not always accurate. Although it is hard to evaluate the difficulty so far, thanks to the development of the estimation methods which consider stochastic properties, now the uncertainty can be used indirectly as that difficulty. In this research, we conduct the uncertainty quantification (UQ) in the OD estimation and propose a new location optimization method of the traffic count points considering UQ result.