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Activity-based Model

Guide to background and modelling assumptions made.

Assumptions

Geographies

Since the SPC currently uses 2011 OA11CD and MSOA11CD codes, 2011 boundaries will be used.

Locations

  • Homes: OA11CD centroids
  • Work: exact location data
  • Education: exact location, travel time for a given mode is used to determine the feasible zone.
  • Shop (food):
  • Shop (other):
  • Visit:

Adding activity patterns to synthetic population

NTS data

  • We are currently using the entire NTS sample, but this could include trips with unrepresentative distances (e.g. commuting distance in London is not the same as liverpool). See #16

Household level matching

  • We use categorical matching at the household level (level 1) and then propensity score matching (PSM) at the individual level (level 2)
  • We need to implement PSM from the beginning to ensure that each individual in the SPC is matched to at least one sample from the NTS. See #13
  • Matching variables are decided using trial and error (see 2_match_households_and_individuals. Using PSM would allow us to use all variables
  • For each SPC household, we randomly select one of the matched NTS households
  • Rest of the assumptions are outlined in the wiki page

Individual level matching

  • Done based on age_group and sex only. PSM without replacement

Assigning activities to geographic locations

Mode and trip purpose mapping 

  • The NTS has a detailed breakdown of modes. We create dictionaries to map these modes to more generic modes (less categories)
  • The same is done for trip purposes
  • You can see the mode_mapping and purp_mapping dictionaries in 3_locations_primary

Home locations 

  • These are still MSOA centroids (as taken from SPC). These locations need to be edited so households are assigned to different houses

Activity locations (education)

Downloading and labeling POIs
  • We download osm locations and label them. This is currently done through osmox with a custom config json file. See this example
  • In the config file, we specify what to download from osm, and in "activity_mapping", we decide how each POI is labelled.
  • For education POIs, I've done the following:   

"kindergarden": ["education_kg", "work"],

"school": ["education_school", "work"],

"university": ["education_university", "work"],

"college": ["education_college", "work"],

Selecting feasible zones for each activity
  • This is done in get_possible_zones. The function looks at the travel mode and trip duration (from NTS), and uses a pre-calculated travel time matrix by mode to identify which zones can be reached for that mode and duration. It then selects activities that have a POI that matches our activity purpose dact
  • The function can return an empty list for some activities, as no zones match our constraints. We need to add logic to ensure at least one zone with a relevant POI is chosen. We can choose a POI based on distance and disregard the travel time matrix
Selecting a zone from feasible zones
  • If an individual in the NTS has an "education" activity, I map their age to an education type. See the age_group_mapping dictionary in 3_locations_primary:

age_group_mapping = {

1: "education_kg", # "0-4"

2: "education_school", # "5-10"

3: "education_school", # "11-16"

4: "education_university", # "17-20"

5: "education_university", # "21-29"

6: "education_university", # "30-39"

7: "education_university", # "40-49"

8: "education_university", # "50-59"

9: "education_university" # "60+" }

  • When selecting a location for an education activity in select_zone, we try to select a zone that has a POI that matches the persons age group. If we can't we choose any other feasible zone with an education POI
  • This logic should be moved upstream to the get_possible_zone. For each activity, we should always ensure that our list of feasible zones has a zone with our specific POI category. This should be added in the filter_by_activity logic. The filter_by_activity logic currently looks at activity purpose from the NTS (e.g. "education"). We need to add the extra level of detail from age_group_mapping, and then filter based on that instead
  • We select a zone from the feasible zones probabilistically based on total floor area of the POIs that match the relevant activity. See select_zone
Selecting a POI
  • We choose a POI from the relevant POIs in the chosen zone.This is done probabilistically based on floor_area
  • Should this step be embedded in select_zone()

TODO

  • edit osmox config to replace education_college with education_university. We should have mutually exclusive groups only and these two options serve the same age group
  • (DONE here) migrate logic for age_group_mapping from select_zone() to get_possible_zones()
  • edit get_possible_zones() to ensure it never returns an empty list of zones. See above for how to do this
    • 14/05/2024: I created another function fill_missing_zones(). see this commit