Implementation of origin-destination matrix estimation model for an urban public passenger transport route

Introduction. The relevance of this research stems from the need to quickly obtain information for calculations, particularly to study the regularities of passenger component in transport load. Currently, a major factor hindering successful forecasting in the field of passenger transportation is the insufficient implementation of models for estimating correspondence within urban passenger transport route networks via modern programming languages. The implementation of numerous methods at road transport enterprises is hindered by a severe lack of empirical survey data. Therefore, this study aims to implement a correspondence matrix estimation model for a public transport route as the basis for new data processing methods and algorithms, the use of specialized Python libraries enabling high performance computing without expensive equipment.

Materials and Methods. The data source for the experiment was a control tower equipped with the POTOK software package for monitoring passenger transportation. Primary data was collected from a BARS-01 T unit and Luch M.

Results. This study presents an efficient data processing method to estimate origin-destination matrices by integrating traffic flow reconstruction techniques with passenger flow data, using the least absolute deviations method to determine the optimal solution.

Robust optimization is proposed as an evaluation tool to address transport problems given inaccurate initial data. To solve the problem, the interior-point, simplex, revised simplex method, high-performance GHS, and dual simplex method algorithms from the Python package have been used.

Discussion and Conclusion. The extended functionality of this methodology reduces labor intensity in collecting passenger traffic data.

This study presents a practical model of evaluating matrices for public transport routes. This approach ensures the required measurement accuracy while reducing data collection costs, and supporting both transportation system planning and a broader functional assessment of urban areas.

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