# Modeling Activity-Time to Build Realistic Plannings in Population Synthesis in a Suburban Area
## 4 Proposed Methodology to Set Time-Choices from Travel Survey
### Survey Data Description  
One of the main data sources that we could find is the National Transport and Travel Survey (_Enquete Nationale Transports et Deplacements_) \(ENTD\), it is a French national survey with several objectives: learning about the trips of households residing in metropolitan France and their use of both public and individual modes of transport. This survey describes all trips, regardless of the reason (origin and destination of the activity), distance, duration, mode of transport used, time of year, or time of day [18].  
Several types of information can be extracted from the \(ENTD\) trips, such as the following data distributions: (1) activity end-time, (2) activity-duration, and (3) the travel speed of each mode of transport. Others parameters can also be extracted from \(ENTD\): (4) the typical activity duration, and two tolerance (delay) coefficients, namely (5) duration delay coefficient and (6) mode-travel time delay coefficient.  
To apply the time-choice process, the parameters (1-3) are necessary, along with the demand file, which contains the activity-plans for the different agents; the parameters (4-6) are optional, as are (7) the activity-gap data. The required data (variables) are presented as follows:  
* \(ENTD\) distributions:
* Duration-distribution: each activity has a duration-distribution, which represents the proportion of users who participate in this activity for a given duration.
* End-time distribution: for each activity, the end-time distributions can be reconstructed from \(ENTD\) observations, regrouped, and based on a time-period.  
Figure 3: Agent-related and population-related constraints.  
Travel speed-distribution: in this approach, each transport mode has a travel-speed value (an approximate value), it was used to estimate the travel time from an activity to another one. Four transportation modes are considered: car, public transportation, bicycle, and walking; for each of these modes, a travel speed-distribution is extracted from the observations based on the travel distance and the travel time.
* Activity-chains (demand file): activity-sequence and transport-modes of agents is the main input of this approach, these plans are loaded without the temporal attributes (duration, start-time, or end-time).
* Optional parameters: -Activity gap: some activities require a gap-duration; we define the gap value as the minimum time between two activities of the same type. An example of such activity is _kindergarten_: a parent has to drop off their child to the kindergarten; the child spends at least the _kindergarten-duration_ there (at the kindergarten-place), which means that the parent cannot come back to pick him up (which means a second _kindergarten_-activity) before this time (_kindergarten-gap_) is over (Figure 4). -Activity-duration tolerance coefficient: the activity-duration tolerance (a tolerance refers to a positive or negative delay) represents the proportion of time that an agent can spend in an activity, more or less comparable to the typical (selected) duration. Two duration delay coefficients are defined: (1) static coefficient, which depends on the type of activity (independently from the selected duration of the activity); (2) dynamic coefficient depends on the selected duration, and is used if the duration distribution is available. These coefficients are used to control the duration delay value, with the aim to keep the duration within limits (minimum and maximum) values defined by the distribution. -Travel time delay coefficient: each trip (transfer from an activity to another) has a travel time. In real life, an agent can travel faster or slower than the estimate time; this difference of time is called a delay, and the delay coefficient is used to define the maximum delay of travel time using a travel-mode.