Activities

Activities describe why people travel.

They also provide the opportunity proxies that can attract trips to each transport zone. For example, work activity uses jobs, study activity uses schools or universities, and shop activity uses shops.

In the model loop, these raw proxies are converted into opportunity_capacity. The capacity is expressed as activity time that can be assigned to a destination. Jobs, schools, and shops are raw proxies. This matters when you interpret opportunity occupation and saturation.

Main Activities

The usual activities are:

  • HomeActivity

  • WorkActivity

  • StudyActivity

  • ShopActivity

  • LeisureActivity

  • OtherActivity

A minimal first model can use only home, work, and other:

activities = [
    mobility.HomeActivity(),
    mobility.WorkActivity(),
    mobility.OtherActivity(population=population),
]

PopulationGroupDayTrips requires at least one HomeActivity and one OtherActivity.

A More Detailed Activity Set

A project model can add study, shop, and leisure:

activities = [
    mobility.HomeActivity(),
    mobility.WorkActivity(),
    mobility.StudyActivity(),
    mobility.ShopActivity(),
    mobility.LeisureActivity(),
    mobility.OtherActivity(population=population),
]

Each activity brings its own opportunity data or opportunity-building logic.

Use the minimal activity set when you are checking installation, testing a territory, or debugging modes.

Use the detailed activity set when the study question needs those motives. For example:

  • add StudyActivity when school or university trips matter,

  • add ShopActivity when retail access or shopping travel is part of the analysis,

  • add LeisureActivity when leisure facilities or non-work travel are important.

Adding activities can make the model more expressive, but it also adds opportunity data and assumptions that need to be checked. In a study report, activity definitions should be linked to the question being asked: for example school access, retail access, leisure travel, or non-work travel.

For custom opportunities, provide a table with a destination zone column to and a positive opportunity proxy column n_opp. Mobility normalizes n_opp within each activity and scales it to the modelled activity-time demand.

The capacity formula is documented in the model steps page.

Activity Parameters

Activities can use parameters such as:

  • value of time,

  • destination choice sensitivity,

  • opportunity saturation level,

  • opportunity saturation strength.

Example:

work = mobility.WorkActivity(
    value_of_time=2.5,
    radiation_lambda=0.99986,
    saturation_fun_ref_level=1.2,
    saturation_fun_beta=4.0,
)

These values are modelling assumptions. radiation_lambda controls destination-choice dispersion. The saturation parameters control how attractive or costly a destination becomes as its modelled occupation approaches or exceeds capacity. Keep these assumptions visible, and test the sensitivity of important results to them when they are not calibrated from local evidence.

Other Activity

OtherActivity needs either:

  • a population, so residents are used as a proxy for other opportunities,

  • or an explicit opportunities dataframe.

The usual first setup is:

other = mobility.OtherActivity(population=population)

This is a first approximation. For a project where “other” trips are important, document why residents are an acceptable proxy for destination opportunities, or replace the proxy with project-specific opportunity data.