Simulation scenarios for automated mobility
Static vs. dynamic demand
Sebastian Hörl
SystemX
5 Feb 2019, Paris
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?
Julius Bär / Farner
I. MATSim
Mesoscopic Transport Simulation
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
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, K.W. Axhausen (2018) Pairing discrete mode choice models and agent-based transport simulation with MATSim, presented at the Annual Meeting of the Transport Research Board 2019
https://pixabay.com/en/zurich-historic-center-churches-933732/
II. AMoD in Zurich
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, Under Review.
Fleet control
Load-balancing heuristic
Simple heuristic, fast runtime
Gloal Euclidean Bipartite Matching
Standard algorithm in OR
Feedforward Fluidic Optimal Rebalancing Policy
Linear program matching a priori known trip rates
Adaptive Uniform Rebalancing Policy
Linear program distributing vehicles equally
Fleet control
Fleet sizing with dynamic demand
Fleet sizing with dynamic demand
Fleet sizing with dynamic demand
Autonomous vehicles?
Cost structures?
User preferences?
System impact?
1. AV Cost Calculator
2. Stated preference survey
3. MATSim simulation
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
Felix Becker, Institute for Transport Planning and Systems, ETH Zurich.
Model integration
Results
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
III. Paris Scenario
https://pixabay.com/en/paris-eiffel-tower-night-city-view-3296269/
Sociodemographics
Activity Chains
Locations
MATSim
Census data, FILOSOFI
Enquête globale de transport (HTS)
- OD from census data
- Specifically designed sampling algorithm
Paris Transport Scenario - Demand side
Paris Transport Scenario - Demand side
Paris Transport Scenario - Demand side
Paris Transport Scenario - Demand side
Paris Transport Scenario - Demand side
Road network
Public Transport
Locations
MATSim
OpenStreetMap
GTFS from IDFm
Paris Transport Scenario - Supply side
Paris transport scenario - Supply side
Paris transport scenario - Supply side
Paris transport scenario - Supply side
Traffic counts
Paris transport scenario - Simulation
Paris transport scenario - Simulation
Paris transport scenario - Simulation
IV. Case study
AMoD in Paris
* Hörl, S., M. Balac, and K.W. Axhausen (2019) Dynamic demand estimation for an AMoD system in Paris, Upcoming.
AMoD in Paris
Travel Behaviour
Zurich model, calibrated for Paris population
AMoD Cost
Costs for Berlin
Fleet control
Heuristic algorithm
AMoD in Paris
Maximum static demand: 2.3M trips
AMoD in Paris
Maximum static demand: 2.3M trips
AMoD in Paris
Potential studies
Final remarks
Thanks!
Questions?
Contact: sebastian.hoerl@ivt.baug.ethz.ch