## 2 Methodology
### Synthesis Framework
Figure 4 shows population changes in the study area (i.e., the district of Ustí nad Labem) from 2011 to 2016 as a result of the demographic transition procedure. The population of the city of Ustí nad Labem declined, while population growth was observed in its surrounding municipalities. This indicated that there was a suburbanization process taking place during the study period, and changes in the commuting patterns for the study area were to be expected. Step C2 cleaned the census data (CD-2016), extracting only citizens residing in the study area and harmonizing the CD-2016 data with both City HTS and Czech Republic HTS data according to the process described in the previous section. Step H1 organized both City HTS and Czech Republic HTS data, in addition to also applying the harmonization process. Inconsistent data were removed, notably when some of attributes were unknown, such as average trip distances and the district codes for the origins or destinations of a trip, when trip purposes were missing, when activity chains did not start and end at home, and also when trips were repeated. Step H2 was originally intended to filter HTS trips that were totally carried outside the study area. This process is important to ensure more precise travel behavior, and it is also available in the open  
Figure 4: Population of municipalities in the Ustí nad Labem district in 2011 (**top image**) and percentage of population changes between 2011 and 2016 (**bottom image**). code in Supplementary Materials; tough it removed too many samples. Therefore, we deactivated this step of Eqasim framework and focused on the assignment of synthetic gates for inbound and outbound trips of the study area based on the average travel times between the study area and any municipality in the Czech Republic. As the Czech Republic HTS have only OD data between certain municipalities, synthetic gates were assigned using weighted random with the population of the destination's municipality as weight parameter. This step enabled us to model city visitors/residents traveling in and out of the city in the simulation. Step P1 performed the statistical matching process applied in the Sao Paulo scenario [19] for CD-2016, Czech Republic HTS, and City HTS datasets, which utilizes a simplified version of the hot-deck matching algorithm [37]. The principle of this process relies on assigning travel behavior characteristics from an HTS sample to a sample in the CD-2016 based on the sociodemographic attribute values in both the CD and HTS datasets. It requires mandatory attributes, which represent attributes that must be same value in both CD-2016 and HTS samples and preferential attributes that are not required to be the same. Let \(a\) be defined as an attribute, the sum of mandatory and preferential attributes as \(N\), and a sample from the CD dataset as \(c\), we can define the sample's vector of its attribute values as follows:  
\[a_{c}=(a_{c,1},\ \ldots,\ a_{c,N}) \tag{1}\]  
Similarly, for a sample from the HTS dataset, \(s\):  
\[a_{s}=(a_{s,1},\ \ldots,\ a_{s,N}) \tag{2}\]  
First, the algorithm creates a vector with all possible combinations \(k\) of mandatory attribute values with preferential attribute values, for instance:  
\[a_{k}=(a_{k,1},\ \ldots,\ a_{k,N}) \tag{3}\]  
Then, for every combination \(k\) of attribute values, we define \(\beta=\{s\,|\,a_{s}=a_{k}\}\) as the samples with same attribute values towards a combination and its size as the number of HTS samples matching with this combination, likewise \(\gamma=\{c\,|\,a_{c}=a_{k}\}\) while its size is the number of matching CD samples. Considering a minimum number of HTS samples to match with CD samples \(M\) that exist with any matching CD sample, i.e., \(\mid\beta\mid\geq M\) and \(\mid\gamma\mid>0\), then the algorithm randomly assigns HTS samples to CD-2016 samples.