CHARACTERISTICS OF ROAD TRAFFIC MODELING FOR ADAPTIVE TRANSPORTATION PLANNING

Abstract

This paper addresses the problem of accounting for diurnal variability of road traffic in adaptive transportation planning systems. Traditional transportation models with fixed cost coefficients often fail to reflect real operational conditions, leading to suboptimal routing decisions, increased delivery times, higher costs, and additional environmental impacts. To overcome these limitations, the study proposes a compact and computationally efficient method for approximating daily traffic profiles that can be directly integrated into modified transportation problems while preserving their linear structure.

The proposed approach represents the temporal component of traffic congestion as a parametric combination of normal (Gaussian) density functions. Model parameters are estimated automatically based on route classification and a set of contextual factors, including the degree of urbanization, route length, and travel direction (inbound or outbound). Additional stochastic corrections and local temporal shifts are introduced to increase variability and realism under data-scarce conditions. Such a formulation enables the generation of interpretable and reproducible daily traffic profiles for different classes of routes, including urban, suburban, and intercity connections.

An algorithm for automatic profile construction is presented, encompassing route classification, detection of significant traffic peaks, estimation of distribution parameters, and calculation of congestion multipliers discretized on an hourly basis. The resulting profiles are used to model time-dependent transportation costs within an adaptive planning framework. The method has been implemented in the client–server information system “ChronoLogix,” which integrates geospatial data, interactive visualization, and tools for editing transport matrices and daily profiles.

Experimental results demonstrate that the automatically generated profiles adequately reproduce characteristic differences in traffic dynamics between route types and travel directions. Urban routes exhibit higher baseline congestion and pronounced peak periods, while suburban and intercity routes show lower overall intensity with narrower, direction-dependent peaks. At the same time, the study confirms that achieving closer agreement with observed traffic statistics requires local calibration of contextual multipliers and consideration of additional factors such as seasonality, weather conditions, and planned road events.

Overall, the proposed method provides an interpretable, data-efficient, and practically oriented solution for incorporating temporal traffic variability into adaptive transportation planning systems, making it suitable for research, educational applications, and small- and medium-scale logistics operations.