# Generating Activity-Based Mobility Plans from Trip-Based Models and Mobility Surveys
## 5 Discussion
### Limitations  
The most apparent limitation of this work is given by its exclusive focus on car mobility. No other mode is considered by the employed methodology. This focus was set by design (\(\text{Lim}_{1}\)) because of the initial motivation that led to the development of the presented framework and the overall scope of this work. The question of whether and to what extent the presented methodology is generalizable to multi-modal mobility behavior remains unanswered. It is to be suspected, however, that the inclusion of multiple alternative modes of transport will incur additional modeling efforts, mainly due to the manifold interdependencies between mode choice, location choice, travel times, and activity durations. Another fundamental limitation of the work at hand is its applicability, which is restricted to homogeneous spatiotemporal environments (\(\text{Lim}_{2}\)). Based on the design of our approach and all means by which we validated it, no definite statement regarding its applicability to more heterogeneous areas that, for example, contain both urban and rural areas can be made. Regarding temporal mobility behavior, it has to be said that the existing approach is only fit to model weekdays. To enhance the capabilities of the methodology, the model could be extended to also include weekends. In addition to the existing mobility distributions for the working week, this would require replicating all distributions with the weekend data. Concatenating working weeks and weekends would allow one to significantly increase the simulation horizon to span a whole or even multiple weeks. However, the assumption of cars staying within an area of homogeneous mobility behavior does not hold as strong for weekends and longer time frames, interfering with the existing limitation to a restricted spatial domain. This leads to another potential enhancement of the capabilities: boundary conditions on the flow out of and into the area of homogeneous mobility behavior could enable modeling the strong geographical interdependencies of transportation systems without facing the complexity of them. These conditions can allow entities to temporarily leave the simulated areas and allow entities from outside to temporarily enter them without expanding the area of the simulation. The boundary condition would only determine at what time and, for example, with which state of charge cars return or enter. If the boundary conditions turn out to be realistically implementable, then the simulation of large areas of heterogeneous mobility behavior could be simplified by simulating many small areas of homogeneous mobility behavior and connecting them using boundary conditions. Another limitation of this work lies in the resolution with which sociodemographic influences were captured. Currently, the population is clustered into working and non-working people. A further differentiation promises to increase the quality and resolution of the results [63]. Additionally, to reduce the complexity of the approach, habitual behavior with regard to location selection was not modeled. This simplification is not assumed to impact the model's validity, since it is of no relevance to the car whether the driver knows the destination as long as the distances are realistic. A final but noteworthy limitation arises from the basic solution principal employed: the presented methodology is a data-driven and mostly statistical approach aimed at reproducing mobility behavior. It does so by incorporating both the general mobility behavior and the location-specific mobility behavior from two separate data sets without explicitly modeling human behavior. Hence, all mobility plans constructed by our approach merely represent the traits available in the input data, effectively merging two separate data sources \(\text{Req}_{2c}\).