Link-based measurement model to estimate route choice parameters in urban pedestrian networks

Passive monitoring with the Global Positioning System (GPS) is increasingly used to automatically monitor trip data. However, GPS tracking data includes measurement errors that depend on the monitoring device and network description in the model. In the case of urban pedestrian networks, such as city centers, the built environment of streets is often diverse, and this has a significant impact on the measurement. The errors cause the biased observations of route choices, and thus the parameter estimation results of route choice models are also biased. To deal with this problem of biased estimation, this study proposes a link-based route measurement model that sequentially infers links using decomposed sequences of data and estimates the link-specific variance of the GPS measurement error. We also incorporate a link-based route choice model as the prior to correct the measurement model by considering behavioral mechanism without path enumeration. Additionally, to remove the biases included in the prior information, this study proposes a structural estimation method in which the fixed point problem of behavioral parameter is solved by the iteration process. The performance of the proposed methods is examined both through a numerical example and a case study on a real pedestrian network. As the results, the methods refine the performance of the route measurement model, and the estimated parameters of a route choice model obtained by the structural estimation method are less biased and exhibit a different trend than those using the biased route choice observations. Also, the estimated variances of the GPS measurement errors are realistic.