# Determination of Factors Influencing Road Transport Demand: Evidence from Croatia
## 5 Discussion and Conclusions
The number of registered passenger cars and domestic tourist arrivals showed the closest correlation with AADT, both in terms of annual percentage changes and overall trend patterns. This alignment suggests that road transport demand in Croatia is significantly influenced by the availability and use of passenger cars, as well as seasonal tourism patterns. The strong correlation with domestic tourist arrivals is expected, as Croatia's road network is heavily utilized during peak tourist seasons, particularly along major motorway corridors connecting inland regions to the Adriatic coast. On the other hand, factors such as rail transport of goods and total population trends demonstrated opposite percentage changes when compared to AADT. The negative correlation with rail transport of goods can be explained by the underdeveloped railway infrastructure in Croatia, which results in road transport being the dominant mode for freight movement. When road freight traffic increases, rail transport of goods tends to decline, indicating a modal shift preference for road transport. Additionally, the observed decline in total population during the study period contrasts with the increase in road transport demand, suggesting that economic and tourism factors have a greater impact on road use than demographic shifts. Interestingly, while GDP growth rates and real net wage growth showed positive correlations with AADT, their annual percentage changes did not always move in lockstep with transport demand fluctuations. This indicates that while economic prosperity influences road transport demand, short-term transport trends may be more sensitive to specific economic conditions such as fuel prices, employment rates, and household disposable income rather than macroeconomic growth alone. These findings highlight the complexity of road transport demand forecasting, as multiple interacting factors contribute to the observed trends. Recognizing which variables move in parallel with AADT and which exhibit opposite behaviors can help policymakers refine transport demand models and better predict infrastructure needs. Future research should further investigate the interplay between these factors using more advanced modeling techniques, such as machine learning approaches, to capture non-linear dependencies and improve forecasting accuracy. This research defined the relevant economic, demographic, as well as traffic and transport indicators for modeling road transport demand. Based on the results of descriptive statistics, graphical analysis, and correlation analysis, it was determined that the number of registered road motor vehicles (annual rates of change) has the greatest impact on the trend of transport demand, followed by real net wage growth (annual rates of change), real GDP growth rates, and the number of tourist arrivals (annual rates of change). It was also found that the rates of change in the intensity of road transport demand differ statistically significantly for motorways and state roads, and that, if tourist activity is not evaluated, there is no statistically significant difference in the spatial distribution within the Republic of Croatia. This is confirmed by the fact that no statistically significant difference in GDP growth rates by counties was established. The correlation with the population did not prove statistically significant and had a negative direction, which is expected given that the population in the Republic of Croatia has been declining for about 15 years. Since most of the previous transport demand forecasting models have been based on the population, it can be concluded that the relationship between the population and road transport demand has changed. Specifically, these are older models where owning a personal vehicle was more of a matter of prestige than necessity, whereas with the development of motorization in more recent times, owning at least one vehicle per household is taken for granted, and households often own two or more vehicles.