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Traffic simulators are utilized to solve a variety of traffic-related problems. For such simulators, origin-destination (OD) traffic volumes as mobility demands are required to input, and we need to estimate them. The authors regard an OD estimation as a bi-level programming problem, and apply a microscopic traffic simulation model to it. However, the simulation trials can be computationally expensive if full dynamic rerouting is allowed, when employing multi-agent-based models in the estimation process. This paper proposes an efficient OD estimation method using a multi-agent-based simulator with restricted dynamic rerouting to reduce the computational load. Even though, in the case of large traffic demand, the restriction on dynamic rerouting can result in heavier congestion. The authors resolve this problem by introducing constraints of the bi-level programming problem depending on link congestion. Test results show that the accuracy of the link traffic volume reproduced with the proposed method is virtually identical to that of existing methods but that the proposed method is more computationally efficient in a wide-range or high-demand context.

Efficient Origin-Destination Estimation Using Microscopic Traffic Simulation with Restricted Rerouting