# Generating Activity-Based Mobility Plans from Trip-Based Models and Mobility Surveys
## 5 Discussion
### Requirement Fulfillment  
Motivated by the research gap identified in Section 2.1, two output and two data requirements were imposed on the approach to be developed in Section 3.1. Following these requirements, the aim of the presented methodology is to generate microscopically consistent and feasible activity-based mobility plans (\(\text{Req}_{1a}\)) while simultaneously achieving a realistic macroscopic mobility behavior with regard to the order, frequency, location, time, and duration of each activity as well as the induced travel distances (\(\text{Req}_{1b}\)). Furthermore, the influence of sociodemographic factors on typical mobility patterns is to be respected (\(\text{Req}_{1c}\)). In order to reduce the complexity of the approach, it is required to operate on existing data only (\(\text{Req}_{2a}\)). These input data need to contain all necessary information, including the sociodemographic characteristics (\(\text{Req}_{2b}\)) considered in the fulfillment of \(\text{Req}_{1c}\). Within our validation case study for the city of Munich, we successfully fulfilled the input requirements \(\text{Req}_{2a}\) and \(\text{Req}_{2b}\) by employing the existing data sets MID and MITO. As a consequence, implementation of the approach did not involve any further data acquisition. It is worth noting that the presented methodology itself is not location-specific, in contrast to the data sets employed within our case study. The methodology can therefore be transferred to any study area for which equivalent input data sets are at hand. These data sets have to comprise a (synthetic) population, an associated set of trips within the study region, as well as a general mobility survey to deduce a realistic macroscopic mobility behavior. Fortunately, such data sets are widely available since they are used in many transportation engineering tasks. MID, for example, covers the whole of Germany and may be representative for other areas within central Europe. Despite these principle advantages regarding generalizability, the approach is always reliant on a location-specific trip data set and artificial population. Due to its input requirements, it is not a model that can be applied without location-specific engineering efforts. Looking at the output requirements, it can be said that the generated mobility plans are microscopically consistent and feasible with real-life mobility behavior (\(\text{Req}_{1a}\)) while being macroscopically realistic (\(\text{Req}_{1b}\)) at the same time. This statement is backed up by the successful validation of activity shares, activity chains, the number of activities per car and day, the realistic start times and durations, as well as the faithful spatial distribution of the different activity types, as presented in Section 4. From a methodological perspective, microscopic consistency was achieved by sequentially planning the drive and activity pairs, and hence no two activities of the same car could take place simultaneously in different locations. Each drive started where the previous one terminated. By design, no two consecutive activities were in the same place, ensuring that there was an actual drive between activities. Cars exceeding the available time (i.e., having activities longer than the time between two start times) were excluded from the simulation. Spatial feasibility was ensured by applying distance and drive time proxies, which resulted in trip distances and drive times that were between Google Maps' estimates for low- and high-traffic scenarios. Hence, the travel speed was realistic as well. Temporal feasibility of individual activities was ensured by drawing from a distribution fitted to real activities, but the total daily activity duration was not ensured by design. Unrealistically long days may have occurred as a result, but they were filtered out.