Simulation scenarios for automated mobility

Static vs. dynamic demand

Sebastian Hörl

IVT Brown Bag, ETH Zurich

18 Mar 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

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 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 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

MATSim

RScript

Results

Maximum

38k rides

Results

Maximum

38k rides

Results

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

MATSim Scenario for Île-de-France

- OpenStreetMap, GTFS

MATSim Scenario for Île-de-France

MATSim Scenario for Île-de-France

MATSim Scenario for Île-de-France

AMoD in Paris

Hörl, S. M. Balac, and K.W. Axhausen (2019) Dynamic demand estimation for an AMoD system in Paris, Under review.

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

Hörl, S. M. Balac, and K.W. Axhausen (2019) Dynamic demand estimation for an AMoD system in Paris, Under review.

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, Under review.

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

AMoD in Paris (or elsewhere)

Potential studies

  • Financial aspects: Profits, subsidies
     
  • Dispatching algorithms under customer behaviour
     
  • Sensible scenario diameters
     
  • Interaction with land use
     
  • Emissions

Final remarks

Thanks!

Questions?

Contact: sebastian.hoerl@ivt.baug.ethz.ch

Simulation scenarios for automated mobility

By Sebastian Hörl

Simulation scenarios for automated mobility

Brown Bag, IVT, ETH Zurich, 18 March 2019

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