## 3 Results
There was a satisfactory match for the distribution of trip counts between the datasets, and we observed a lower than 5% difference between datasets for almost all chains, while only the difference of \"one trip\" count probability for the city area was slightly higher than 5% in the synthetic population. The matching process for travel characteristics and the accurate assignment of resident numbers, facility capacities, and visitor numbers mainly caused this difference. The observed differences between the HTS datasets and the synthetic population for the main activity chain (home-work-home, accounting for 34% of trips) was around 1%, while there was an exact match for the activity-chain types representing 16% of the trips such as h-er-h (home-errands-home), h-w-s-h (home-work-shopping-home), h-er-s-h (home-errands-shopping-home). These were successful matches. The h-e-h (home-education-home) trips were estimated to be 6% higher than in the HTS surveys, and the reason for this was the consideration of escorting activities (such as bringing children into a school) to be education-related trips in our algorithm. Therefore, this result was expected. The count of h-s-h and h-l-h chains was underestimated in the synthetic population, because the accurate assignment of capacities and visitors changed the chain variety for shopping and leisure activities in the population. The shopping and leisure activities were distributed as a \"leg\" in the remaining minor activity chains, which represented more than 10% of the trips; therefore, the results were reasonable. To further confirm the rank-order matches of activity chains, [PERSON]'s correlation is checked between HTS data and the synthetic data by utilizing Scipy package in Python. Considering the leading 20 activity-chain types for the entire area in the datasets, [PERSON]'s correlation coefficient of chain type rank-order is 0.897 (\(p\) value \(<\) 0.001) which is very strong evidence of the successful match. Additionally, [PERSON]'s correlation coefficient of trip count rank-order is 0.983 (\(p\) value \(<\) 0.001) and 0.949 (\(p\) value \(<\) 0.001) for the city area and the surrounding area, respectively, which confirms the successful rank-order match. Furthermore, we analyzed the synthetic output at a different sampling rate, shown in Figure 16. Population downscaling is applied in almost all simulation studies because of  
Figure 17: Travel-distance characteristics. the requirement of higher computational resources, which is beyond the existing software abilities, such as in MATSim [44]. The effect of different population downscaling rates on the simulation results of MATSim is categorized and reviewed in a study (please see [44] for the details), which 1% to 25% sampling rates are preferred in almost all MATSim studies. Reckoning this information and population size of the study area, we tested a moderate value of 20% here. Two hundred synthetic data configurations (as similarly applied in [9]), equivalent of 20% of population size, were derived from the synthetic population with the random seeds. Figure 16 demonstrates the mean percentages of activity chains and trip counts in the 200 derived populations. Additionally, the range between the minimum and maximum percentages of the variants in the derived populations is presented with a black strip attached to the bars. Even if the population downsampling is applied, the mean distribution matches of activity-chain characteristics are at a similar level with the main data, as seen in Figure 16. The mean standard deviation of the distribution of all activity-chain types in the 200 derived populations varies between 0-2.8%, which is reasonable. The mean standard deviation of the distribution of trip counts in the 200 derived populations for the city area varies between 0-1%, except \"one trip\" and \"four trips\", which are 6.96% and 3.34%, respectively, while the standard deviation is between 0-2.2% for the surrounding area except \"one trip\", which is 7.37%.