# Generating Activity-Based Mobility Plans from Trip-Based Models and Mobility Surveys
## 4 Results and Validation
### Spatiotemporal Mobility Behavior  
This section presents the results regarding the spatiotemporal realism of the generated mobility plans. Figure 13 compares the start time distribution for work, shopping, and recreational activities in MID with their counterparts from our simulation. As expected, the generated start times accurately followed the fitted start time distributions and hence the original MID data. A slight exception was given by the work activities. The work activity distribution of our results exhibited a larger proportion of later work times. This deviation from MID is rooted in the fact that work activities are often main activities, serving as anchor times for their mobility plans based on the work time from the synthetic MITO population. It remains an open question whether MITO models the local Munich working behavior more accurately than the more aggregated MID survey. Figure 11: Comparison of the simulation output with MID survey data in terms of daily distances and number of activities. (**a**) Number of activities per car and day. (**b**) Distance driven per car and day. Figure 12: Distance share among activity types. Comparison of simulation and MID. The total activity duration per car and day is presented in Figure 14. A qualitative comparison of the activity durations reveals that the generated durations were mostly similar to those of the reference. It is to be noted, however, that the share of the short activity duration sums was larger than expected, resulting in overly short dwelling times at activity locations. Aside from temporal considerations, the spatial modeling capabilities of the presented approach play an important role in its usability for transportation engineering tasks. In contrast to all previously mentioned statistics, MITO stepped up as the reference data set in this regard, since it focuses on the spatiotemporal modeling of mobility demand in Munich. Figure 15 can be used to assess whether the generated activities were distributed realistically among the different TAZs. The figure consists of four parts which are subsequently  
Figure 14: Total activity duration per car and day. Comparison of simulation and MID. Figure 13: Start times of the first activity per activity type. Comparison of simulation and MID. explained. The first row shows the distribution of visits per activity type according to the MITO reference. Work, shopping, and recreational activities are used to differentiate between the most important non-home activity types. Home locations are not shown since these were taken directly from MITO and thus matched the reference by design. In contrast, the distributions of non-home activities were a direct result of the presented location choice methodology and hence needed validation. For each TAZ, the absolute number of visits \(n_{v}\) was divided by the total number of activities of the respective type \(N_{v}\) to obtain the share associated with the TAZ. It can be seen that no single TAZ exceeded a share of 0.7% of any activity type. Considering the study area's 1904 unique TAZs, a uniform distribution would result in 0.053% per zone. A logarithmic colorbar was used to better reveal the existing heterogeneity regarding activity distribution. Qualitatively, the city center and the outskirts can be recognized as the main working areas. Shopping is mostly focused around the city center, and recreational activities are distributed almost uniformly. The second row of the figure displays the same statistic for the simulated data set, qualitatively yielding the same results. To better access the differences between the reference and our results, a measure of error is needed. Hence, the symmetric mean absolute percentage error (SMAPE) was used as a measure for the deviations between MITO and our simulation.