Travel demand

A/B Street simulates people following a schedule of trips over a day. A single trip has a start and endpoint, a departure time, and a mode. Most trips go between buildings, but the start or endpoint may also be a border intersection to represent something outside the map boundaries. The mode specifies whether the person will walk, bike, drive, or use transit. Without a good set of people and trips, evaluating some changes to a map is hard -- what if the traffic patterns near the change aren't realistic to begin with? This chapter describes where the travel demand data comes from.

Scenarios

A scenario encodes the people and trips taken over a day. See the code.

TODO:

  • talk about vehicle assignment / parked car seeding

Data sources

Seattle: Soundcast

Seattle luckily has the Puget Sound Regional Council, which has produced the Soundcast model. They use census stats, land parcel records, observed vehicle counts, travel diaries, and lots of other things I don't understand to produce a detailed model of the region. We're currently using their 2014 model; the 2018 one should be available sometime in 2020. See the code for importing their data.

TODO:

  • talk about how trips beginning/ending off-map are handled

Berlin

This work is ongoing. See the code. So far, we've found a population count per planning area and are randomly distributing the number of residents to all residential buildings in each area.

Proletariat robot

What if we just want to generate a reasonable model without any city-specific data? One of the simplest approaches is just to spawn people beginning at residential buildings, make them go to some workplace in the morning, then return in the evening. OpenStreetMap building tags can be used to roughly classify building types and distinguish small houses from large apartments. See the proletariat_robot code for an implementation of this.

This is ongoing work spearheaded by Mateusz. Some of the ideas for next steps are to generate different types of people (students, workers), give them a set of activities with durations (go to school for 7 hours, 1 hour lunch break), and then further pick specfic buildings to travel to using more OSM tags.

Census Based

Trips are distributed based on where we believe people live. For the US, this information comes from the US Census. To take advantage of this model for areas outside the US, you'll need to add your data to the global population_areas file. This is one huge file that is shared across regions. This is more work up front, but makes adding individual cities relatively straight forward.

Preparing the population_areas file

See popdat/scripts/build_population_areas.sh for updating or adding to the existing population areas. Once rebuilt, you'll need to upload the file so that popdat can find it.

Custom import

If you have your own data, you can import it. The input format is JSON -- an example:

{
  "scenario_name": "monday",
  "people": [
    {
      "origin": {
        "Position": {
          "longitude": -122.303723,
          "latitude": 47.6372834
        }
      },
      "trips": [
        {
          "departure": 10800.0,
          "destination": {
            "Position": {
              "longitude": -122.3075948,
              "latitude": 47.6394773
	    }
          },
          "mode": "Drive"
        }
      ]
    }
  ]
}

Run the tool:

cargo run --bin import_traffic -- --map=data/system/seattle/maps/montlake.bin --input=/path/to/input.json

The tool matches input positions to the nearest building, within 100 meters. If the point lies outside the map boundary, it's snapped to the nearest map border. The departure time is seconds since midnight. The tool will fail if any point doesn't match to a building, or if any of the specified trips can't be created (due to graph connectivity problems, for example). If your requirements are different or you have any trouble using this format/tool, please file a Github issue -- just consider this tool and format a prototype.

Modifying demand

The travel demand model is extremely fixed; the main effect of a different random number seed is currently to initially place parked cars in specific spots. When the player makes changes to the map, exactly the same people and trips are simulated, and we just measure how trip time changes. This is a very short-term prediction. If it becomes much more convenient to bike or bus somewhere, then more people will do it over time. How can we transform the original demand model to respond to these changes?

Right now, there's very preliminary work in sandbox mode for Seattle weekday scenarios. You can cancel all trips for some people (simulating lockdown) or modify the mode for some people (change 50% of all driving trips between 7 and 9am to use transit).

Research