Full-scale spatio-temporal traffic flow estimation for city-wide networks: a transfer learning based approach

The full-scale spatio-temporal traffic flow estimation/prediction has always been a hot spot in transportation engineering. The low coverage rate of detectors in transport networks brings difficulties to the city-wide traffic flow estimation/prediction. Moreover, it is difficult for traditional analytical traffic flow models to deal with the traffic flow estimation/prediction problem over urban transport networks in a complex environment. Current data-driven methods mainly focus on road segments with detectors. An instance-based transfer learning method is proposed to estimate network-wide traffic flows including road segments without detectors. Case studies based on simulation data and empirical data collected from the open-source PeMS database are conducted to verify its effectiveness. For the traffic flow estimation of segments without detectors, the mean absolute percentage error (MAPE) is approximately 11% for both datasets, which is superior to the existing methods in the literature and reduces MAPE by two percentage points.