Statistical approach for activity-based model calibration based on plate scanning and traffic counts data

Traditionally, activity-based models (ABM) are estimated from travel diary survey data. The estimated results can be biased due to low-sampling size and inaccurate travel diary data. For an accurate calibration of ABM parameters, a maximum-likelihood method that uses multiple sources of roadside observations (link counts and/or plate scanning data) is proposed. Plate scanning information (sensor path information) consists of sequences of times and partial paths that the scanned vehicles are observed over the preinstalled plate scanning locations. Statistical performances of the proposed method are evaluated on a test network using Monte Carlo technique for simulating the link flows and sensor path information. Multiday observations are simulated and derived from the true ABM parameters adopted in the choice models of activity pattern, time of the day, destination and mode. By assuming different number of plate scanning locations and identification rates, impacts of data quantity and data quality on ABM calibration are studied. The results illustrate the efficiency of the proposed model in using plate scanning information for ABM calibration and its potential for large and complex network applications.