# Generating Activity-Based Mobility Plans from Trip-Based Models and Mobility Surveys
## 3 Materials and Methods
### Input Data  
Mobility behavior consists of two fundamental elements: general mobility behavior, which covers all characteristics such as the trip length independent of the context, and context-specific mobility behavior, which contains characteristics related to the observed situations (e.g., specific location preferences) [8]. Based on the requirements to employ existing data and to apply the methodology to the city of Munich, Mobilitat in Deutschland (MID), a general mobility survey, and MITO, a trip-based model for the greater Munich area, were used as input data.  
The mobility survey MID represents everyday mobility in Germany. The data consist of 960,619 trips conducted by 316,361 surveyed persons belonging to 156,420 households. Each household was tasked with reporting all trips of all household members on a randomly assigned date. The reported trip data comprise, among others, purpose, distance, origin and destination, time of departure and arrival, and mode of transport [16]. A weighting factor is provided for each trip to balance out the selection probability of sampling and achieve that relevant criteria such as age and sex are distributed equally in the sample and in the residential population of Germany.  
MITO employs a microsimulation to individually model the mobility of households and persons that in turn constitute a realistic travel demand when aggregated. The model has been applied to the greater Munich area and provides location-specific preferences and a synthetic population for our application. Location preferences quantitatively compare the attractiveness of TAZs for a given activity type. The synthetic population contains households and persons for which travel demand is simulated. Aside from household composition and home location, it defines the destinations of mandatory trips for work and education purposes.  
Based on the requirement to model car mobility on a weekday in the city of Munich, the input data had to be filtered and transformed from a person to a car perspective. To this end, the MID survey was filtered for trips in metropolitan environments on a regular weekday by a uniquely identifiable car. Out of 960,619 trips in MID, 2.9%, or 27,658 trips by 9053 cars, remained for further analysis. The strongest reductions were, applied in this order, the limitation to a metropolitan area of residency (\(-\)82.4%) and trips where the reporting person was driving a car (\(-\)72.0%). Each car was assigned a unique car identifier for further processing. Aside from the adjustments to the MID results, the MITO data were also preprocessed to meet this work's requirements. First, the location preferences of MITO were filtered to only contain TAZs within the city limits of Munich. Second, the synthetic population was filtered to those living, working, and studying in Munich (1,223,532 people). Because of the car perspective employed in this approach, the synthetic population was also reduced to persons in possession of a driver's license, ultimately resulting in 900,032 persons living in 624,571 households. Based on this, the synthetic car-population was created. Considering the number of cars per household and the limitation that a household could not simultaneously use more cars than members with driver's licenses, the synthetic car population comprised 482,991 cars for which artificial mobility plans could be generated.