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)

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
       
  • Visualizing the simulation
     
    • Clone / Download
       
    • Run
       

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

 

U_i=V_i+\epsilon_i
k = \text{argmax}_i \{ U_i,...,U_N \}
P(k) = \frac{\exp(U_k)}{\sum_i \exp(U_i)}

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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