RESEARCH ON THE DEMAND FOR ROAD PUBLIC TRANSPORTATION IN SUBURBAN TRAFFIC

Abstract

The analysis of existing approaches to estimating transportation demand based on OD matrix modeling has been conducted to identify the most effective methods for suburban and interurban traffic planning. The interval concept is applied by analyzing patterns of trip length distribution within the city and adjacent territories, allowing the definition of intervals within which transportation demand varies in relation to urban centers. This provides a flexible and probabilistic framework for capturing the variability in passenger flows, taking into account daily and seasonal fluctuations as well as differences in travel behavior between urban and suburban areas. To determine the spatial distribution of trips, actual travel distances around the regional center were analyzed, ensuring that the model reflects real-world travel patterns and regional transport infrastructure.

The proposed methodology was applied to evaluate suburban transportation services, demonstrating its capability to generate OD matrices that are consistent with observed demand and operational data. The results confirm that the interval-based approach can enhance planning accuracy, optimize route allocation, and support decision-making in suburban public transport management. Furthermore, this method contributes to the development of more robust models for forecasting transportation demand, offering practical guidance for transport planners and policymakers aiming to improve service efficiency and passenger satisfaction. The study highlights the potential of integrating probabilistic modeling techniques into transportation planning to account for uncertainties and dynamic changes in travel behavior, providing a scientific basis for evidence-based decision-making in the organization of suburban transport systems.

Keywords: demand, suburbanarea, city, commuters, distance, OD matrix.