# Determination of Factors Influencing Road Transport Demand: Evidence from Croatia
###### Abstract  
Effective forecasting of road transport demand is vital for optimizing transport planning and system efficiency. Despite technological and methodological advancements, the accuracy of these forecasts has not improved significantly over the past several decades, with typical errors ranging from 20% to 35%. These inaccuracies often result in either under- or over-capacitated transport systems, leading to unforeseen economic and financial consequences. This research focuses on factors that have an impact on road transport demand, based on research conducted in Croatia over the period from 2004 to 2016. It identifies three key impact groups--traffic and transport, economy, and demography--that impact road transport demand. The findings indicate that the annual rates of change in the number of registered motor vehicles have the most significant influence on road transport demand trends. Other key factors include real net wage growth, GDP growth rates, and the rate of domestic tourist arrivals. Additionally, the analysis reveals a statistically significant difference in transport demand intensity between motorways and state roads, highlighting the importance of considering unique traffic patterns in infrastructure planning. These insights provide a robust foundation for developing more accurate forecasting models and improving transport policy and investment strategies. r 2025 15 2238 2025  
Forecasting transport demand before the 1950s was mainly based on expansion coefficients of existing demand [5]. The study that laid the foundation for modern transport planning was the Chicago Area Transportation Study, conducted in 1959 under the leadership of [PERSON]. In this study, the transport demand forecast was based on what remains the most commonly used method today--the traditional four-step model, which includes trip generation, trip distribution, mode choice, and trip assignation to the network [7]. In the last 2 decades, in addition to the traditional four-step model, Activity-based and Tour-based models have been developed. These models base transport demand forecasting on the daily activities of trip representatives, enabling the overcoming of many shortcomings found in classic four-step models. The main drawback of these models is the requirement for more detailed historical data on trips [8; 9; 10; 11]. Alongside the most commonly used models mentioned earlier, there are also models based on time series methods and causal models such as regression models [12; 13; 14]. The goal of such research has generally been to create simple mathematical models that allow decision-makers, as well as the general public, to easily use and verify the accuracy of the forecasts made while maintaining a sufficient level of precision. One of the first such models was created in 1987 for the purpose of forecasting transport demand on state roads in rural areas of the United States [12]. For the forecast, a multiple linear regression model was developed where the dependent variable was the average annual daily traffic (AADT), and the independent variables were state population, county population, and the number of households. In the last decade, unconventional methods of forecasting transport demand, which are based on natural and social sciences such as psychology, have been increasingly researched and promoted. One of the primary reasons for this is the lack of significant improvement in the accuracy of forecasts over the past 30 years, despite substantial technological advancements (especially in computer simulations) [15]. Consequently, the American Planning Association (APA) approved the use of a new forecasting method called reference class forecasting in April 2005 [16; 17] and recommends its use alongside traditional methods of transport demand forecasting. This method of forecasting is based on studying and comparing the projects being forecasted with similar projects that have already been implemented, a concept defined by the term \"outside view\".