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https://github.com/sharedstreets/trip-simulator
probabilistic multi-agent trip simulation with physically realistic GPS modeling
https://github.com/sharedstreets/trip-simulator
Last synced: 18 days ago
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probabilistic multi-agent trip simulation with physically realistic GPS modeling
- Host: GitHub
- URL: https://github.com/sharedstreets/trip-simulator
- Owner: sharedstreets
- License: mit
- Created: 2019-02-05T17:52:55.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2020-12-01T14:05:56.000Z (almost 4 years ago)
- Last Synced: 2024-08-01T00:51:07.872Z (3 months ago)
- Language: JavaScript
- Homepage:
- Size: 39.1 KB
- Stars: 169
- Watchers: 11
- Forks: 28
- Open Issues: 17
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
Awesome Lists containing this project
README
# trip-simulator
*A module for generating simulated location telemetry*
## Overview
`trip-simulator` is a tool created by [SharedStreets](sharedstreets.io) for generating simulated raw GPS telemetry. Raw GPS data is highly sensitive, since it can be easily deanonymized and used to track the historical movements of an individual. For this reason, it is difficult to safely develop algorithms for use cases like transit analysis, map matching, or speed profiling without having this data locally available. The SharedStreets trip-simulator realistically creates location telemetry using agents and physically modeled GPS output. Since the data is fake, there is no privacy risk, and since the noise is plausibly simulated, it can still be used for algorithms that need to operate under real world signal conditions.
*1 million trips simulated over NYC*
## Features
- Multi-agent model supporting cars, bikes, or scooters
- Generates GPS data with no inputs besides OpenStreetMap
- Reasonable pickup/dropoff generated using agent model based on network density
- Physically realistic GPS noise modeled for usage in real world telemetry algorithms
- Outputs 1hz location probes, MDS compliant trips, and MDS compliant status changes
## Install
```sh
npm install -g trip-simulator
```
## CLI
```
trip-simulator
-h,--help show help
--config config car,bike,scooter
--pbf osm.pbf file
--graph osrm graph file
--agents number of agents
--start start time in epoch milliseconds
--seconds number of seconds to simulate
--probes GeoJSON probes output file
--traces GeoJSON traces output file
--trips MDS trips output file
--changes MDS status changes output file
```
## Use
trip-simulator features a command line interface capable of generating telemetry data for simulated vehicles around a city. The simulation requires an OpenStreetMap extract, which can be downloaded from many sources, such as planet.osm, geofabrick, or nextzen. Download and extract, and trim the network to the target area using a tool such as osmium. Once you have a trimmed extract, process the road graph using OSRM. As an example, we will walk through simulating data for Nashville, TN using a nextzen extract.
### 1. download data
```sh
curl https://s3.amazonaws.com/metro-extracts.nextzen.org/nashville_tennessee.osm.pbf -o nashville.osm.pbf
```
### 2. extract a smaller target bounding box
```sh
osmium extract -b "-86.84881,36.12262,-86.73688,36.20494" nashville.osm.pbf -o ./nash.osm.pbf -s "complete_ways" --overwrite
```
### 3. install OSRM to your project directory & process the road graph using the default pedestrian profile
```sh
npm install osrm;
./node_modules/osrm/lib/binding/osrm-extract ./nash.osm.pbf -p ./node_modules/osrm/profiles/foot.lua;
./node_modules/osrm/lib/binding/osrm-contract ./nash.osrm
```
### 4. run a 24 hour scooter simulation with 100 vehicle agents
```
trip-simulator \
--config scooter \
--pbf nash.osm.pbf \
--graph nash.osrm \
--agents 100 \
--start 1563122921000 \
--seconds 86400 \
--traces ./traces.json \
--probes ./probes.json \
--changes ./changes.json \
--trips ./trips.json
```
## Test
```sh
npm test
```
## Lint
```sh
npm run lint
```