Integration of autonomous on-demand ridesharing vehicles

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Integration of autonomous on-demand ridesharing

vehicles into the public transport system in a

peri-urban area in Stuttgart region

Nadine Müller

Regional Transport Planner

© V

RS

Wern

er

Baechle

© V

RS

/Sto

ppel

© V

RS

/ R

udel-R

egenscheit

Funded by the „Federal program on transnational collaboration“ of the

Federal Ministry of the Interior, Housing and Home

© e

.GO

MO

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mbH

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© V

erb

and R

egio

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tuttgart

; ©

Univ

ers

ität

Stu

ttgart

VuV

2021

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I. Dynaxibility4CE

II. Traffic Model Study

III. Scenarios

IV. Study Outcomes

CONTENTS




▪ Focus on low-carbon mobility and air quality priority

▪ Duration: March 2020 – February 2022

▪ 11 partners from 7 countries

▪ Outputs: Strategies + Action Plans related to UVAR, MaaS, CAD

DYNAXIBILITY4CE




▪ Regional Traffic Model in PTV VISUM software

▪ Integration of autonomous on-demand ridesharing vehicles into the

public transport system in a peri-urban area in Stuttgart region

▪ Simulation of diverse scenarios to assess the potential impacts and

discover optimisation approaches

TRAFFIC MODEL STUDY


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© Verband Region Stuttgart 2016; © INOVAPLAN GmbH und KIT

– Institut für Verkehrswesen, 2013

nach Hause

Freizeit

Ausbildungsverkehr

Einkauf / Erledigung

Berufsverkehr

Fahrtzwecke

© Verband Region Stuttgart 2016; © INOVAPLAN GmbH und KIT – Institut für Verkehrswesen, 2013

REGIONAL TRAFFIC MODEL




▪ Peri-urban area „Schurwald“

▪ Inhabitants: 50.000

▪ 25 bus lines

➢ Microscopic adaption of the regional

traffic model

RESEARCH AREA

StuttgartSchurwald

© Verband Region Stuttgart 2016; © INOVAPLAN GmbH und KIT – Institut für Verkehrswesen, 2013

© Verband Region Stuttgart; © Universität Stuttgart - VuV, 2021




RESEARCH AREA





SCENARIOS

A Scenario 0 Base scenario of the year 2030

B Scenario 1 Ridesharing 15 min from the bus stations

C Scenario 2 Ridesharing 15 min from the place of choice

D Scenario 3 Bus + Ridesharing 30 min from the bus stations

Assumptions:

• Tariff: Same ticket price for the ridesharing offer as for the public transport

• Ridesharing vehicle: Capacity of 6 passengers; drives autonomously; driving speed is

equivalent to regular vehicles

• Transfer from the ridesharing vehicle to the rail transport:

• Transfer walking time: 3 min

• Transfer waiting time/buffer time: 5 min




SCENARIO 1 & 2





SCENARIO 3





RESULTS: MODE CHOICE

Passenger kilometres/dayTrips done by person/day

Walk

Bike

PT

Car

Passengers

Car

Walk

Bike

PT

Car

PassengersCar

*Originating, terminating & domestic traffic of the Schurwald area © Verband Region Stuttgart; © Universität Stuttgart - VuV, 2021




RESULTS: VEHICLES

Operational kilometers/day Number of necessary vehicles

Bus_emptyKm

RS_emptyKm

RS_loadKm

Bus_loadKm Buses

RS-

Vehicles





CONCLUSION

1) Implementation of a broad and individualized transport offer

2) Positive effects on the modal split of the public transport

3) Increase of total driven distance

4) Low occupancy rate of ca. 2 passengers/ridesharing vehicle

5) Costs and energy consumption are higher

6) CO2 emissions are approx. on the same level

Outlook:

➢ Scenario 3 – biggest potentials: Limited individualization and bundling of public transport

➢ Optimisation potential: Spatial and time restriction in further research




© e.GO MOOVE GmbH

© (

Reg

io)

Stu

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art

Ma

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Ms. Nadine Müller

Regional Transport Planner

Regional Association of Stuttgart

Email: [email protected]

Mobile: +49 711 22759 932

www.region-stuttgart.org

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Integration of autonomous on-demand ridesharing vehicles