Micromobility: The example of Switzerland

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Research Collection

Presentation

Micromobility

The example of Switzerland

Author(s):

Axhausen, Kay W.

Publication Date:

2020-07-09 Permanent Link:

https://doi.org/10.3929/ethz-b-000426164

Rights / License:

In Copyright - Non-Commercial Use Permitted

This page was generated automatically upon download from the ETH Zurich Research Collection. For more information please consult the Terms of use.

ETH Library

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Preferred citation style

Axhausen, K.W. (2020) Micromobility: The example of Switzerland, Emerging Mobility Systems and Services Seminar Series,

University of Michigan EM4S webinar, July 2020.

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Micromobility: The example of Switzerland?

KW Axhausen IVT

ETH

Zürich

July 2020

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Acknowledgements

• Daniel J. Reck

• Sergio Guidon

• Haitao He

• The data providers!

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Starting point: Switzerland

High quality local/regional public transport systems with high shares of season ticket ownership (national and regional) The “big” cities/agglomerations are small in absolute terms, e.g. the largest Zürich 420k and 1500k

Long history of “sharing” systems, i.e. Mobility station-based

car sharing has the largest nation-wide share of customers

world-wide (about 3% of adult population)

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Starting point: What is available?

Ride-hailing:

UBER, but strictly regulated Car-sharing:

Mobility - nationwide

Catch-a-Car (Mobility Go) Basle and Geneva Bike-sharing:

Publibike, nationwide by the publicly owned Postbus SMIDE (BOLD), some cities, private

eScooter:

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Research questions

Demand estimation

- Current usage

- Structure of the demand without competition - Choice among the direct competitors

- Choice among all competing modes - Transferability between cities

- Induced demand Their role in the modal chain

- Usage

- Choice modelling

- Demand modelling at scale

Welfare changes and regulation

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Demand estimation & transferability

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Smide (Bold) in Zürich and Berne

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Smide (Bold) in Zürich and Berne

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Smide (Bold) in Zürich and Berne

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Demand free floating e-bikes: SARAR regression model

Variable Zürich Berne

Population (k) 16.6 9.6

Employed (k) 15.9 6.4

Restaurants et al. 2.7 2.5

Distance to main station (km) -7.9 -0.5

Distance to city limit (km) -8.9 -6.7

High transit service -5.1 2.5

Urban rail station within 200m 36.0 -1.9

Main station within 500m 77.5 35.0

Intercept 38.7 4.3

ρ 0.3 -0.3

λ -0.4 -0.2

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Using Zürich models to predict Smide in its new market

ZH_SR (SARAR) (a and c) and ZH_RF (random forests) (b and d)

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Choice among the direct competitors

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Several shared micromobility providers available in

Zurich

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Localised competition increasingly common

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Scraped data of 5 providers for Jan-Feb 2020

• 169M vehicle observations

• “disappearances under conditions”

 Duration: [2 min; 60 min]

 Distance: [200m; 15km]

 Speed: (0; 45 km/h]

• ~169K trips (~2800 per day)

 ~67K docked e-bike trips

 ~25K docked bike trips

 ~15K dockless e-bike trips

 ~32K dockless e-scooter trips (provider #1)

 ~30K dockless e-scooter trips (provider #2)

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Scraped vs booking data for the dockless e-bikes

Booked

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Descriptive statistics: Time of day

Provider

Docked eBike Docked Bike

Dockless eScooter #1 Dockless eScooter #2 Dockless eBike

Docked E-Bike Docked Bike Dockless E-Scooter #1 Dockless E-Scooter #2 Dockless E-Bike

Docked E-Bike Docked Bike Dockless E-Scooter #1 Dockless E-Scooter #2 Dockless E-Bike Docked E-Bike Docked Bike Dockless E-Scooter #1 Dockless E-Scooter #2 Dockless E-Bike

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Descriptive statistics: Distance

Provider

Docked eBike Docked Bike

Dockless eScooter #1 Dockless eScooter #2 Dockless eBike

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Choice sets

• (Theoretical) choice between 5 different providers/modes within 2 min walk

• Availabilities observed

Departure Destination

2 min walking

distance

Competing available vehicles from different providers

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Availabilities and observed choices

Provider Number of vehicles

Availability in choice

situations

Chosen

(when available) Dockless E-Scooter

#1 693 62 % 29 %

Dockless E-Scooter

#2 766 85 % 20 %

Dockless E-Bike 241 44 % 19 %

Docked Bike 762 39 % 40 %

Docked E-Bike 841 63 % 64 %

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Choice patterns: Time of Day

Provider

Docked eBike Docked Bike

Dockless eScooter #1 Dockless eScooter #2 Dockless eBike

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Choice patterns: Distance

Provider

Docked eBike Docked Bike

Dockless eScooter #1 Dockless eScooter #2 Dockless eBike

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Choice patterns: Elevation change

Provider

Docked eBike Docked Bike

Dockless eScooter #1 Dockless eScooter #2 Dockless eBike

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Choice patterns: Supply density

Provider

Docked eBike Docked Bike

Dockless eScooter #1 Dockless eScooter #2 Dockless eBike

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NECLM (docked, dockless) with ρ

²

=0.35

Dockless e-scooter

#1

Dockless e-scooter

#2

Dockles s e-bike

Docked bike

Docke d e- bike

ASC -5.01 -3.53 -6.28 -0.50

Distance

¹

1.77

¹

1.77 2.97 6.81 6.91

Price -0.46 -0.62 -0.63

Vehicle density 0.21 0.24 0.23 0.05 0.09

Morning (6 – 9 a.m.) -0.35 -0.33 1.71 1.73

Night (9 p.m. - 5

a.m.) 1.02 0.70 -2.10 -2.01

Elevation gain 0.04 -0.05

Battery 0.02 0 0

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NECLM (docked, dockless) marginal probability effects

Variable Alter-

native Dockless E-

Scooter

#1

Dockles s E- Scooter

#2

Dockles s E-Bike

Docked E-Bike

Docked Bike

Price DL ES #1 -0.94 0.53 0.18 0.15 0.08

DL ES #2 0.84 -1.55 0.33 0.25 0.13

DL E-Bike 0.14 0.15 -0.41 0.09 0.03

Vehicle DL ES #1 0.46 -0.26 -0.07 -0.09 -0.04

density DL ES #2 -0.33 0.56 -0.10 -0.09 -0.05

DL E-Bike -0.04 -0.05 0.11 -0.02 -0.01

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Conclusions

Docked modes preferred for commuting

-> Docking stations for currently dockless modes to bolster use for commute?

Plateau effect for supply density

-> Vehicle providers: optimize relocating practices

-> City authorities: define max # of vehicles / density in any part of the city

Intermodal chains still need to be looked at Induced demand effects need to be estimated

Spatial regression models work, but don’t transfer easily

Random forests did not outperform here

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Questions ?

www.ivt.ethz.ch

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COVID19 impacts: Trips and activity spaces

0 1 2 3 4 5

0 100 200 300 400 500

Baseline Mar.02

Mar.09 Mar.16

Mar.23 Mar.30

Apr.06 Apr.13

Apr.20 Apr.27

May.04 May.11

May.18 May.25

Jun.01 Jun.08

Jun.15 Jun.22

Number of trips/day

Activity space [km2]

Week starting with Monday

Weekday - Activity space Weekend ..

Weekday - Trips Weekend ..

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COVID19 impacts: Door to door speeds

0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0 90.0

km/h door to door

Baseline 0 to 20

After lockdown 0 to 20 Baseline 20 to 50

After lockdown 20 to 50 Baseline 50+

After lockdown 50+

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COVID19 impacts: Supressed demand

-100.0%

-50.0%

0.0%

50.0%

100.0%

150.0% Baseline-2019 Mar.02

Mar.09 Mar.16

Mar.23 Mar.30

Apr.06 Apr.13

Apr.20 Apr.27

May.04 May.11

May.18 May.25

Jun.01 Jun.08

Jun.15 Jun.22

%Change

Week starting with

Car Walk Cycle Bus Train

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Questions ?

www.ivt.ethz.ch

ivtmobis.ethz.ch/mobis/covid19/

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IVT References 2020

Guidon, S., D.J. Reck and K.W. Axhausen (2020) Expanding a (electric) bicycle-sharing system to a new city: Prediction of demand with spatial regression and random forests, Journal of

Transport Geography, 84, 102692.

Guidon, S., M. Wicki, T. Bernauer and K.W. Axhausen (2020) Transportation service bundling – for whose benefit? Consumer valuation of pure bundling in the passenger transportation market, Transportation Research A, 131, 91-106.

Li, A. and K.W. Axhausen (2020) Environmental benefits of bike-sharing based on travel mode choice, paper presented at the 20th Swiss Transport Research Conference, online, 13-14 May 2020

Reck, D.J. and K.W. Axhausen (2020) How much of which mode? Using revealed preference data to design MaaS plans, Transportation Research Record.

Reck, D.J., S. Guidon, H. He and K.W. Axhausen (2020) Shared micromobility in Zurich,

Switzerland: Analysing usage, competition and mode choice, paper presented at the 20th Swiss Transport Research Conference, Online, May 2020.

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IVT References 2018-2019

Guidon, S., H. Becker and K.W. Axhausen (2019) Avoiding stranded bicycles in free-floating bicycle-sharing systems: Using survival analysis to derive operational rules for rebalancing, paper presented at the 22nd IEEE Intelligent Transportation Systems Conference, Auckland, October 2019.

Guidon, S., H. Becker, H. Dediu and K.W. Axhausen (2019) Electric bicycle-sharing: a new competitor in the urban transportation market? An empirical analysis of transaction data, Transportation Research Record, 2673 (4) 15-26.

Hörl, S., M. Balac and K.W. Axhausen (2019) Pairing discrete mode choice models and agent-

based transport simulation with MATSim, paper to be presented at the 98th Annual Meeting of the Transportation Research Board, Washington, D.C., January 2019

Li, A., Y. Huang and K.W. Axhausen (2018) Bicycling accessibility based on dock-less bicycle-

sharing data, Arbeitsberichte Verkehrs- und Raumplanung, 1377, IVT, ETH Zurich, Zurich Reck, D.J. and K.W. Axhausen (2019) Learning from the MaaS experience in Augsburg, Germany, Arbeitsberichte Verkehrs- und Raumplanung, 1469, IVT, ETH Zurich, Zurich.

Reck, D.J. and K.W. Axhausen (2019) Ridesourcing for the first/last mile: How do transfer penalties impact travel time savings?, paper to be presented at the International Scientific Conference on Mobility and Transport, Munich, September 2019.