Estimating dynamic origin-destination demand: A hybrid framework using license plate recognition data

摘要

Abstract This article proposes a hybrid framework for estimating dynamic origin–destination (OD) demand that fully exploits the information available in license plate recognition (LPR) data. A Bayesian path reconstruction model is initially developed to replenish the lost information resulting from the recognition error and insufficient coverage rate of the LPR system. The link flows, initial OD demand, left‐turning flows, and partial path flows are derived based on the reconstructed data. Subsequently, with the information derived, a two‐step ordinary least squares (OLS) OD estimation model is formulated, which incorporates the output from the Bayesian model and coestimates the OD demand and assignment matrix. The proposed framework is qualitatively validated using the real‐world LPR data collected from Langfang City, Hebei Province, China, and is quantitatively validated using the synthesized simulation data for the simplified road network of Langfang. The results show that the proposed model can estimate OD demand distribution with a mean absolute percentage error (MAPE) of about 30%. We also tested the model with different LPR coverage rates, with results showing that an LPR coverage rate of over 50% is required to obtain reasonable results.