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

ABSTRACT

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.

KEYWORDS:

• Calibration
• daily plans
• income dependency
• agent-based modeling
• MATSim
• mixed traffic
• location choice

Acknowledgments

The support given by DAAD (German Academic Exchange Service) to first author for his PhD studies at Technische Universität Berlin is greatly acknowledged. This paper is based on material from first author’s dissertation and a preliminary version of this paper is presented at 4th Conference of Transportation Research Group of India (CTRG 2017).

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1. Refer to Rieser-Schüssler (2012), Lee, Sener, and Mullins (2016); Barmpounakis et al. (2017) for more details about the modern data collections approaches, data sources and examples.

2. Parts of the data in the household survey were unavailable (e.g. missing trips for few zones, missing households income for few persons, etc.); for such cases the required data were imputed randomly based on other available data (e.g. trip distribution, income distribution, etc.) in the Patna CMP (see Ch. 5 in Agarwal 2012, for further details about the imputation of missing trips).

3. Refer to Appendix A for more details about the input data for external travel demand, steps to estimate the external trip counts, directional split and OD matrix for through traffic. This data is taken from Patna CMP (TRIPP, iTrans, and VKS 2009).

4. 1 $\mathrm{U}\mathrm{S}\mathrm{D}$ $\approx$ 66.6 ₹. Exchange rate on June 8 2016.

5. By default, the marginal utility of traveling, ASC, monetary distance rates for a mode are set to 0. This means, during a trip by mode truck, the agent will lose only opportunity cost of time ($={\beta }_{dur}\cdot t_{trav}$).

Additional information

Funding

This work was supported by the DAAD;