Calibration of choice model parameters in a transport scenario with heterogeneous traffic conditions and income dependency

By raising the issue of data requirements for the purpose of modal development, validation and application, this study proposes an approach to calibrate choice model parameters in heterogeneous traffic condition using minimal empirical data. For this, a real-world scenario of Patna, India is chosen. For the calibration, a Bayesian framework-based calibration technique (CaDyTS: Calibration of Dynamic Traffic Simulations) is used. Commonly available, mode-specific, hourly-classified traffic counts are used to generate full day plans of agents and their initially unknown activity locations. While the proposed approach implements location choice implicitly, the approach can be applied to a variety of other problems. Further, the effect of household income is included in the utility function to incorporate the effect of income in the decision-making process of individual travelers and to filter out inconsistencies in the daily plans, which originate from the survey data.