AMoDeus and eqasim
Dynamic demand simulation of automated mobility on demand
Sebastian Hörl, Claudio Ruch, Milos Balac
ITSC Workshop
27 October 2019
The street in 1900
http://www.loc.gov/pictures/item/2016800172/
The street today
https://commons.wikimedia.org/wiki/File:Atlanta_75.85.jpg
The street of tomorrow?
- Autonomous Mobility
- Mobility as a Service
- Mobility on Demand
- Electrification
- Aerial Mobility
Julius Bär / Farner
Agent-based models
Senozon VIA
https://pixabay.com/en/traffic-jam-stop-and-go-rush-hour-143391/
MATSim
Home
Work
Shop
Home
until 8am
9am to 6pm
6:15m to 6:30pm
from 6:45pm
walk
public
transport
walk
MATSim
<population>
<person id="person1">
<attributes>
<attribute name="age" value="32" type="java.lang.Integer" />
<attributes>
<plan>
<activity type="home" end_time="08:00:00" x="0.0" y="0.0" link="L1" facility="H1" />
<leg mode="walk" />
<activity type="work" end_time="18:00:00" x="100.0" y="100.0" link="L42" facility="W32" />
<leg mode="pt" />
<activity type="shop" end_time="18:30:00" x="50.0" y="100.0" link="L231" facility="S532" />
<leg mode="walk" />
<acitivity type="home" x="0.0" y="0.0" link="L1" facility="H1" />
</plan>
</person>
</population>
MATSim
-
population.xml.gz
All agents and their daily plans
-
households.xml.gz
Agents grouped into households
-
network.xml.gz
(Road) network with nodes and links
-
facilities.xml.gz
Locations for activities (shops, ...)
-
schedule.xml.gz
Detailed transit schedule
MATSim
Mobility simulation
Modification of plans
Analysis
Scenario
- Flexible, extensible and well-tested open-source transport simulation framework
- Used by many research groups and companies all over the world
- Extensions for parking behaviour, signal control, location choice, freight, ...
eqasim
Scenario
Analysis
Raw data
- New project with the aim to integrate open-source transport planning tools and data
- Pipeline from raw data to simulation and analysis
- Enables reproducible research with agent-based transport models
AMoDeus
Travellers
Fleet control
Mobility simulation
Vehicles
- Second-by-second control of vehicle fleet
- Control of customer-vehicle assignment, rebalancing, parking search, ...
- Streamlined interfaces for control engineers
Today's workshop
- Workshop introduction
- DIY 1: Setting up the simulation
- Introduction to demand modeling
- DIY 2: Run you own simulation
- Introduction to fleet control
- DIY 3: Design your own fleet controller
We're here
Slides and code available at:
https://github.com/eqasim-org/auckland-example
Preparation
- You need
- A JAVA JDK*
to run stuff
-
Eclipse IDE (or IntelliJ)
for coding
- QGIS to define
an operating area
(optional)
- A JAVA JDK*
https://www.eclipse.org/downloads/
https://www.qgis.org/en/site/forusers/download.html
* not JRE
https://adoptopenjdk.net
Slides and code available at:
https://github.com/eqasim-org/auckland-example
Simulation setup
- Running the simulation
- Clone / Download
- Run
- Clone / Download
- Visualizing the simulation
- Clone / Download
- Run
- Clone / Download
https://github.com/eqasim-org/auckland-example
org.eqasim.auckland_example.RunSimulation
https://github.com/idsc-frazzoli/amod
amod.demo.ScenarioViewer
with auckland_example as working directory
II. Demand Modeling
Today's workshop
- Workshop introduction
- DIY 1: Setting up the simulation
- Introduction to demand modeling
- DIY 2: Run you own simulation
- Introduction to fleet control
- DIY 3: Design your own fleet controller
We're here
Slides and code available at:
https://github.com/eqasim-org/auckland-example
Fleet sizing with dynamic demand
Fleet sizing with dynamic demand
Fleet sizing with dynamic demand
What do we know about automated vehicles?
Cost structures?
User preferences?
System impact?
Cost Calculator for automated mobility
Stated preference survey
Agent-based simulation
1
2
3
AMoD Choice Model
- Score (utility) for each available
choice with deterministic and
random component
- Choice model
- Choice sampling
AMoD Choice Model
AMoD Survey
Felix Becker, Institute for Transport Planning and Systems, ETH Zurich.
AMoD Cost Calculator
Bösch, P.M., F. Becker, H. Becker and K.W. Axhausen (2018) Cost-based analysis of autonomous mobility services, Transport Policy, 64, 76-91
AMoD Survey
13 CHF/h
AMoD
Taxi
19 CHF/h
Conventional
Car
12 CHF/h
Public
Transport
VTTS
Car by Adrien Coquet from the Noun Project
Bus by Simon Farkas from the Noun Project
Wait by ibrandify from the Noun Project
AMoD
AMoD Survey
Car by Adrien Coquet from the Noun Project
Bus by Simon Farkas from the Noun Project
Wait by ibrandify from the Noun Project
13 CHF/h
AMoD
Taxi
19 CHF/h
Conventional
Car
12 CHF/h
Public
Transport
VTTS
21 CHF/h
32 CHF/h
AMoD
Model structure
Cost calculator
Plan modification
Discrete Mode Choice Extension
Mobility simulation
Prediction
Price
Trips
- Utilization
- Empty distance, ...
- Travel times
- Wait times, ...
Results
Maximum
38k rides
Results
Maximum
38k rides
Results
Visualisation
Automated taxi
Pickup
Dropoff
AMoD in Paris
Hörl, S., M. Balac and K.W. Axhausen (2019) Dynamic demand estimation for an AMoD system in Paris, paper presented at the 30th IEEE Intelligent Vehicles Symposium, June 2019, Paris, France.
Auckland
The Auckland "toy" example
- OpenStreetMap data
- OpenStreetMap data
including land use
The Auckland "toy" example
- OpenStreetMap data
including land use
- Distribution of homes
The Auckland "toy" example
- OpenStreetMap data
including land use
- Distribution of homes
- Distribution of work
The Auckland "toy" example
- Commuter traffic (uncalibrated)
- Roughly calibrated mode share
- Added AMoD fleet
The Auckland "toy" example
Today's workshop
- Workshop introduction
- DIY 1: Setting up the simulation
- Introduction to demand modeling
- DIY 2: Run you own simulation
- Introduction to fleet control
- DIY 3: Design your own fleet controller
We're here
Slides and code available at:
https://github.com/eqasim-org/auckland-example
Your own simulation
- Change the fleet size
- Change the pricing structure
- Change the operating area
(GIS tool needed)
- Change the control policy
Further instructions available at:
https://github.com/eqasim-org/auckland-example
- What do you observe in the viewer?
- How does modestats.png change?
- How does av_price.csv change
- Perform some basic analysis with events.xml.gz
III. Fleet control
Today's workshop
- Workshop introduction
- DIY 1: Setting up the simulation
- Introduction to demand modeling
- DIY 2: Run you own simulation
- Introduction to fleet control
- DIY 3: Design your own fleet controller
We're here
Slides and code available at:
https://github.com/eqasim-org/auckland-example
ITSC 2019: AMoDeus and eqasim
By Sebastian Hörl
ITSC 2019: AMoDeus and eqasim
ITSC Workshop, Auckland, 26 October 2019
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