# Determination of Factors Influencing Road Transport Demand: Evidence from Croatia
## 3 The Impact on Road Transport Demand in Croatia
### Traffic and Transport Factors  
For the purposes of the research, a total of eight transport factors were analyzed. To facilitate description and interpretation, the factors were classified into the following five groups based on their characteristics:  
* Traffic volume,
* Road category,
* Geographical location,
* Motor vehicles,
* Transport of goods.  
Special attention was given to the analysis of the rates of change in transport demand relative to various factors, with the aim of determining statistically significant differences in means. Accordingly, the existence of statistically significant differences in the rates of change was analyzed for:  
* Years,
* Time of year (Average Annual Daily Traffic-AADT, Average Summer Daily Traffic-ASDT, and Average Off-Season Daily Traffic-AODT [AODT-for the purposes of the research, the average off-season daily traffic was calculated by subtracting ASDT from AADT, to provide a more objective insight and a more accurate description of road transport demand intensity outside the tourist season),
* Road category,
* Geographical location.  
The rates of change in intensity were calculated as annual rates of change compared to the previous year, based on the road traffic counting database maintained by Croatian Roads Ltd. (Zagreb, Croatia) [29]. Hence, each rate of change was calculated separately for each road traffic counter. The unit of measurement for the rates of change in road transport demand intensity is the percentage.  
For the purposes of the analysis, the following data from the Croatian Roads Ltd. database were not used:  
* Data related to periodical automatic counting--due to differing methodologies compared to continuous automatic counting,
* Data related to continuous automatic counting on motorways--toll counting is more reliable and pertains to only four continuous counting locations,Data representing outlier values--outlier values were specifically determined at the level of each year and type of road, with the methodology for identification being 1.5\(\times\) interquartile range (The 1.5\(\times\) interquartile range rule is a commonly used rule which states that all values less than the first quartile (Q1) minus 1.5\(\times\) interquartile range are considered low outliers, while all values greater than the third quartile (Q3) plus 1.5\(\times\) interquartile range are considered high outliers) [28].  
Following the described methodology, a total of 475 road traffic counters across the Republic of Croatia were used for the analysis of transport factors, amounting to 9350 measurements during the analyzed period. A detailed analysis of the traffic counting data revealed that outlier values on certain road traffic counters mostly occur due to the closure of specific road segments, which subsequently causes significant increases or decreases in road transport demand intensity on certain routes, depending on the new traffic regulations (e.g., detour routes, etc.).  
It is important to note that, before conducting a detailed analysis of transport factors, the primary focus was on analyzing the existence of statistically significant differences in the trends of average annual rates of change in road transport demand over the years (the average annual rate of change was used in this research as it provides a clearer long-term trend across years, minimizing the impact of short-term fluctuations and offering a more reliable measure for forecasting transport demand.). For this examination, the Tukey-Kramer test was performed (The Tukey-Kramer test is a multiple comparison test used to identify means that are significantly different [28]), with a significance level of 0.05. The test revealed a statistically significant difference in the trends of average annual rates of change in road transport demand over the analyzed period (Figure 1). The greatest differences were found in 2007 (6.47%) compared to 2012 (\(-\)3.74%).