AMoDeus and eqasim
Dynamic demand simulation of automated mobility on demand
Sebastian Hörl
ITSC Workshop
26 October 2019
Pre-Workshop at ETHZ
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
I. MATSim
Mesoscopic Transport Simulation
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
Network simulation
Scoring of the plans
Selection and modification
Scenario
MATSim with Discrete Choice Model
Network simulation
Scoring
Replanning
Network simulation
Choice Model
Hörl, S., M. Balac and K.W. Axhausen (2019) Pairing discrete mode choice models and agent-based transport simulation with MATSim, presented at the Annual meeting of the Transport Research Board 2019, January 2019, Washington D.C.
https://pixabay.com/en/zurich-historic-center-churches-933732/
II. AMoD in Zurich
Fleet control
Load-balancing heuristic
Simple heuristic, fast runtime
Gloal Euclidean Bipartite Matching
Standard algorithm in Operations Research
?
?
Assignment
Car by Adrien Coquet from the Noun Project
Hail by Bradley Avison from the Noun Project
Fleet control
Feedforward Fluidic Optimal Rebalancing Policy
Linear program matching a priori known trip rates
Adaptive Uniform Rebalancing Policy
Linear program distributing vehicles equally
Car by Adrien Coquet from the Noun Project
Hail by Bradley Avison from the Noun Project
?
?
?
?
Redistribution
Fleet control
Hörl, S., C. Ruch, F. Becker, E. Frazzoli and K.W. Axhausen (2019) Fleet operational policies for automated mobility: a simulation assessment for Zurich, Transportation Research: Part C, 102, 20-32.
Fleet control
Hörl, S., C. Ruch, F. Becker, E. Frazzoli and K.W. Axhausen (2019) Fleet operational policies for automated mobility: a simulation assessment for Zurich, Transportation Research: Part C, 102, 20-32.
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
MATSim simulation
1
2
3
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 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 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 integration
Model integration
Results
Maximum
38k rides
Results
Maximum
38k rides
Results
Visualisation
Automated taxi
Pickup
Dropoff
III. Paris Scenario
https://pixabay.com/en/paris-eiffel-tower-night-city-view-3296269/
The case of Île-de-France
Census data
Récensement de la population
RP
Icons on this and following slides: https://fontawesome.com // Background: Simunto VIA
The case of Île-de-France
Dispositif sur les revenus localisés sociaux et fiscaux
Income tax data
RP
FiLo
SoFi
The case of Île-de-France
RP: Flux de mobilité
Commuting data
RP
RP
Mob
FiLoSoFi
The case of Île-de-France
Enquête globale de transport
Household Travel Survey
RP
EGT
FiLoSoFi
RP Mob
Enquête national transports et deplacements
Household Travel Survey
ENTD
The case of Île-de-France
Enquête globale de transport
Household Travel Survey
RP
EGT
FiLoSoFi
RP Mob
Enquête national transports et deplacements
Household Travel Survey
ENTD
The case of Île-de-France
Base permanente des équipements
Enterprise census
RP
BPE
FiLoSoFi
RP Mob
EGT
ENTD
The case of Île-de-France
OpenStreetMap
Road network
RP
OSM
FiLoSoFi
RP Mob
EGT
ENTD
BPE
IDFm GTFS
Public transport schedule
GTFS
The case of Île-de-France
RP
FiLoSoFi
RP Mob
EGT
ENTD
BPE
OSM
GTFS
Discrete choice model
Agent behaviour
DCM
The case of Île-de-France
RP
FiLoSoFi
RP Mob
EGT
ENTD
BPE
OSM
GTFS
DCM
The case of Île-de-France
RP
FiLoSoFi
RP Mob
EGT
ENTD
BPE
OSM
GTFS
DCM
The case of Paris
RP
FiLoSoFi
RP Mob
EGT
ENTD
BPE
OSM
GTFS
DCM
The case of Paris
RP
FiLoSoFi
RP Mob
EGT
ENTD
BPE
OSM
GTFS
DCM
The case of Paris
RP
FiLoSoFi
RP Mob
EGT
ENTD
BPE
OSM
GTFS
DCM
The case of Paris
RP
FiLoSoFi
RP Mob
EGT
ENTD
BPE
OSM
GTFS
DCM
The case of Île-de-France
RP
FiLoSoFi
RP Mob
EGT
ENTD
BPE
OSM
GTFS
DCM
The case of Île-de-France
RP
FiLoSoFi
RP Mob
EGT
ENTD
BPE
OSM
GTFS
DCM
The case of Île-de-France
Open
Data
RP
FiLoSoFi
RP Mob
EGT
ENTD
BPE
OSM
GTFS
Open
Software
+
=
Reproducible research
Verifiable results
Integrated testing
- Detailed documentation of whole open-source pipeline is under preparation
DCM
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.
Travel Behaviour
Zurich model, calibrated
for Paris population
Cost sturcture
Adapted from Berlin
AMoD Simulation
Load-balancing heuristic
AMoD in Paris
Maximum static demand: 2.3M trips
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.
AMoD in Paris
Maximum static demand: 2.3M trips
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.
Work in progress ...
Spatial constraints
Operational constraints
Intermodality
https://commons.wikimedia.org/wiki/File:Zeichen_365-65_-_Ladestation_f%C3%BCr_Elektrofahrzeuge,_StVO_2014.svg
https://www.flickr.com/photos/viriyincy/4528290409
https://de.wikipedia.org/wiki/Datei:Zeichen_316_-_Parken_und_Reisen,_StVO_1992.svg
Thanks!
Questions so far?
Contact: sebastian.hoerl@ivt.baug.ethz.ch
IV. 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
Features
- Commuter traffic (uncalibrated)
- Somewhat realistic flows on main roads
- Roughly calibrated mode share>
- Added AMoD fleet
Features
- Commuter traffic (uncalibrated)
- Somewhat realistic flows on main roads
- Roughly calibrated mode share>
- Added AMoD fleet
Options
- Operating Area & Fleet size
- Choice parameters
Placeholder Video
V. DIY
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
Run your first simulation after the break!
ITSC 2019: AMoDeus and eqasim
By Sebastian Hörl
ITSC 2019: AMoDeus and eqasim
ITSC Workshop, Auckland, 26 October 2019 (ETH Test Run)
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