Methods to enhance the quality of bi-level origin-destination matrix adjustment process

The quality of Origin–Destination matrix (OD) estimation depends on number of factors including the selection of appropriate upper-level function of bi-level formulation, constraints to the OD flows, and suitable solution algorithm. Addressing these aspects, the study first explored different upper-level formulations using two types of traffic information: traffic counts and sub-path flows. Second, it investigated the effects of OD constraints on the quality of solution. Third, it proposed modified genetic algorithm (MGA) to address the computational limitations of traditional genetic algorithm (GA). The study findings were as follows: a) Using symmetric mean absolute percentage error (SMAPE) to match traffic counts showed greater improvements in the OD quality; b) The estimates improved as more number of OD pairs were known to have a-priori knowledge about their flows with higher confidence levels; c) The MGA approach outperformed GA in terms of computational efficiency, and gradient descent (GD) in terms of solution quality.