https://github.com/arup-group/londinium
A semi-synthetic MATSim ABM dataset
Science Score: 13.0%
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Low similarity (8.2%) to scientific vocabulary
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A semi-synthetic MATSim ABM dataset
Basic Info
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Metadata Files
README.md
Londinium - A MATSim dataset
Londinium is a semi-synthetic dataset for use with MATSim agent based models. The intention is to provide a dataset small enough to use when running MATSim on a typical developer laptop, but big enough to give MATSim a useful workout. Londinium uses real OSM and GTFS data, combined with a synthetic population of agents.
The Londinium dataset is comprised of:
- "pre-baked", ready-made MATSim input files:
- LSOA data - used to generate the agent activity plans
- an OSM data file covering a region in west London - used to generate the MATSim network
- a GTFS public transit schedule for the geographical area covered by the OSM file - used to generate the MATSim network, schedule and vehicles files used as input to MATSim
- a synthetic MATSim population of 100 agents with travel plans inside this region
OSM Data
We chose the location so as to have a selection of different public transit modes, allowing us to see agents interchange modes. We also wanted to have a mixture of major and minor roads, which this particular section of west London gives us.
OSM Files
| File | Size | Nodes | Ways | Relations |
| ---------------------------------------------- |:-------:|:-------:|:------:|:---------:|
| data/network/osm/Londinium.osm.pbf | 4.1M | 340,621 | 65,067 | 2,129 |
Geographical Bounds

Population Data
The Londinium synthetic population includes various activities, personal attributes and modes. The agents created were classified into different subpopulations by gender and income. Activities are Home-to-Home round trips, all within 24 hours. Daily activity plans for each agent were created using random choice. Agents choose different travel modes such as bus, walk, car randomly for each trip.
The generation of the Londinium population used a notebook from PAM, our open source population activity modeller
We used the above notebook to generate a population including 100 agents. Each agent has an activity plan for the same 24 hour period and randomly chooses a mode between activities.
Population Files
| File | Size |
| -------------------------------------------|:-------|
| data/population/population.xml | 123k |
Population Details
| | | | -------------------- |:--------| | No. Agents | 100 | | No. Households | 100 | | No. Subpopulations | 3 | | Subpopulation split |
low income: 30%medium income: 40%high income: 30%
- Female: 50%
- Male: 50%
Wandsworth: 26%Kensington and Chelsea: 21%Hammersmith and Fulham: 20%Lambeth: 18%Westminster: 13%Richmond upon Thames: 2%
- 62 mode="bus"
- 54 mode="car"
- 50 mode="ferry"
- 49 mode="subway"
- 48 mode="walk"
- 47 mode="rail"
- 42 mode="bike"
- 200 "home"
- 44 "pub"
- 41 "medical"
- 40 "shop"
- 36 "gym"
- 27 "work"
- 25 "education"
- 22 "park"
- 17 "leisure"
Sample Activity Plans

Population Trips Map

Public Transit Schedule Data
The Londinium PT Network

GTFS Directory
data/network/gtfs/Londinium_arup_gb_rail_and_tnds_2020-03-12
GTFS Files
| File | Size |
| ---------------------------- |:-------|
|stop_times.txt | 93M |
|trips.txt | 7.4M |
|transfers.txt | 56B |
|stops.txt | 69K |
|shapes.txt | 73B |
|routes.txt | 1.1M |
|frequencies.txt | 53B |
|feed_info.txt | 145B |
|fare_rules.txt | 54B |
|fare_attributes.txt | 81B |
|calendar_dates.txt | 6.1M |
|calendar.txt | 714K |
|agency.txt | 77K |
GTFS Details
| | | | ---------------- |:------------| | GTFS Day | 12 Mar 2020 | | Transit Trips | 98,402 | | Transit stops | 846 | | Stop times | 1,170,122 | | Transit Routes | 20,607 | | Transit agencies | 1,039 | | Calendar entries | 19,541 | | Calendar dates | 386,517 |
Contributing
Please see our code of conduct and guide to contributing.
Owner
- Name: Arup
- Login: arup-group
- Kind: organization
- Email: media@arup.com
- Website: https://www.arup.com/
- Repositories: 168
- Profile: https://github.com/arup-group
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Last synced: 11 months ago
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- gac55 (3)
- mfitz (2)
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