# Modeling Activity-Time to Build Realistic Plannings in Population Synthesis in a Suburban Area
## 3 Problem Statement and Contributions
### Contributions and Limitations of the Work  
The main contributions of the paper are as follows:  
* The proposed approach is based on public empirical data of distributions related to the temporal aspect of activities; it captures the temporal constraints based on the next activities to select time-choices (duration and end-time) of the current activity. A more classical approach would use socio-economic data to determine parameters of utility-based method: these socio-economic data are usually related to individuals and requires a complex and arbitrary population segmentation process. We believe our method is more simple and straightforward. * Other approaches that use the process of serial insertion of activity often face scheduling conflicts between two activities with overlapping time distribution: they usually proceed by removing one activity or reducing its duration. Our method has the ability to avoid this kind of coarse approximation by taking into account the following activity hours when scheduling the preceding activity. * On other approaches based on \"time budget\", the duration and travel time values are independent of location: duration and travel time are computed first, then an activity-location is chosen to fit the remaining time of the \"budget\". Our approach is more realistic with a process that starts with the choice of activities to conduct during the day, then the location-choice for these activities based on a gravitation model, then a transportation mode-choice based on travel distance, and only then is the activity-time estimated. In this approach, one adapts the hours instead of adapting the location of its activity. * Over the region of interest, the greater Paris, the reference method Eqasim [11] is based on much finer data that are not publicly available: the precise schedule of  
Figure 1: An overview of the model inputs. (**Top-left**): examples of activity-chains (home-based tours); (**bottom-left**): the activity-constraints represented by the time-window; (**top-right**): activity-end time distribution; (**bottom-right**): activity-duration distribution. Figure 2: An overview of the model output: activity-settings (start-time, duration and end time) and trip-travel time. activities of a subsample of the population. To generate a larger synthetic population from this subsample, the algorithm applies, for each activity, a random sampling of the departure time around the real departure time within a 30 min range. In our experience, this algorithm can lead to negative activity-duration for some agents, with a start-time higher than the end-time of the activity. In our approach, the trips departure time (activity end-time) are generated based on end-time distribution of each activity and satisfied following activities constraints. * The proposed approach's benefit for models that use MATSim is that it improves the quality of the initial plans (in terms of score), which allows MATSim to perform the plans with a better quality; moreover, it ensures, for each agent, the feasibility of its plan (i.e., that the agent participates in all of their activities) and keeps a realistic distributions of activities for the different attributes, namely end-time and duration. * Our model is detailed and reproducible. It also uses only public data and can therefore be directly applied to any case study on any French territory. To the best of our knowledge, this is the first detailed proposal of this kind. The main constraints of the model are: time distributions (duration, end-time) for activities; travel speed distribution for transport modes; and demand file (the activity-chains of agents). Some limitations and hypotheses have been made and can be listed as follows. One of the limitations of the algorithm is that it cannot extend its modeling to patterns of mobility that are not already and specifically sensed.