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Oslo studiet –

Trafikmodellers anvendelse for nye teknologier

26 AUGUST 2019

OSLO STUDIET - TRAFIKDAGE

1

Søren Frost, COWI

(2)

Ruter

Existed since 2008:

› Public transport company

› Area: Oslo and Akershus fylkeskommune

› 1.3 million inhabitants

› Bus, tram, metro and boat

› Train is operated by NSB (YV)

› 371 million trips in 2017

(3)

Agenda at Ruter

26 AUGUST 2019

OSLO STUDIET - TRAFIKDAGE

3

Future role for Ruter:

› What is a public transport company role in the future?

› Is there a need for a public transport company?

› What will new technologies lead to?

› Self-driving vehicles

› Car sharing (shared ownership of the cars)

› Ride sharing (carpooling)

› Mobility as a Service (MaaS)

› …

(4)

Technological trends 1 (2017)

Project

• 3 consultant firms

• Literature Study

• Scenario development

Megatrends set the framework for future mobility in all scenarios:

• Technological development

• Urbanization

• Climate changes

(5)

26 AUGUST 2019

OSLO STUDIET - TRAFIKDAGE

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Technological trends 2 (2018)

› Model calculations of the possibilities and consequences of ”extreme” future scenarios

› Inspired by ITF/OECD ”Lisbon studies”

› Assumption on behaviour/demand:

› One joined MaaS concept

› Starting point is car or/and bus- and tram trips

› Car respectively bus- and tram users act together as a group

› Demand is unchanged

› Calculated on the traffic for the whole Oslo region

› Full implementation is considered

(6)

SCENARIOS FOR FUTURE MOBILITY

(7)

26 AUGUST 2019

OSLO STUDIET - TRAFIKDAGE

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Vehicle types and service level Capacity

4 persons Capacity

6 persons Capacity

20 persons

(8)

Model scenarios

- 4 base scenarios 1A, 1B, 2A and 2B

(9)

26 AUGUST 2019

OSLO STUDIET - TRAFIKDAGE

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Model scenarios

- 2 additionally scenarios 3A and 3B

(10)

THE MODEL WE USED

(11)

26 AUGUST 2019

OSLO STUDIET - TRAFIKDAGE

11

› RTM23+ traffic model (EMME)

› Existing traffic model for Oslo

RTM 23+

Modelling system

VISUM MaaS Modeller

› VISUM traffic model

› Transfer of the RTM23+ traffic model

› Public transport (from headway to timetable)

› Demand model not imported, only matrices

› PTV MaaS Modeller

› New modelling tool to calculate on future technologies like Mobility as a Service

› Uses the VISUM traffic model as base for the calculations

(12)

Ride pooling as a mobility concept of the future!

The shared mobility algorithm addresses three core conditions:

› Minimise unserved trip requests

› Minimise the fleet size required

› Minimise the objective function (cost)

Simulating

Shared Mobility

(MaaS Modeller)

City focus

Passen focus ger

Operati onal focus

Holistic overview

The Objective Function

Passenger focus

 Travel demand served

 Waiting times

 Detours

 Travel distances

 Travel times

 Fare

Operational focus

 Required vehicle fleet

 Occupancy

 Duration of the

 trip Operating hours

 Operating performance

 Revenue

City Focus

 Congestion relief / impacts

 Environmental factors (emissions)

 Safety

 Urban realm possibilities / challenges

Sustainable

decisions

(13)

26 AUGUST 2019

OSLO STUDIET - TRAFIKDAGE

13

MaaS Model

Key Statistics

 Scenario 2a

 536,436 trip requests

 56,000 vehicles

 37,279,151 journey legs

(14)

Simulating Mobility as a Service

(15)

RESULTS

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OSLO STUDIET - TRAFIKDAGE

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(16)

Best and worst case

t t

Fleet size reduction

-93%

Vehicle Vehicle

(17)

26 AUGUST 2019

OSLO STUDIET - TRAFIKDAGE

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Empty vehicles

28% of the

kilometres is

empty vehicles

(18)

Fleet utilization

Vehicle operation distance increase from 12 kilometres to

about 150 kilometres

(19)

26 AUGUST 2019

OSLO STUDIET - TRAFIKDAGE

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Level of service

Public passengers in bus/tram saves about 11 minutes Car users get extended

travel time by driving with ride sharing of

approx. 8 minutes Car users get extended

travel time by driving without ride sharing of

approx. 6 minutes

(20)

Network impact

Base scenario

(21)

26 AUGUST 2019

OSLO STUDIET - TRAFIKDAGE

21

Network impact

Scenario 1B

Network impacts flow volume vs base Volume / capacity ratio

(Period morning rush)

(22)

Network impact

Scenario 2A

(23)

26 AUGUST 2019

OSLO STUDIET - TRAFIKDAGE

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Sensitivity analysis

Level of service

• Detour factor and waiting time

Larger reduction in vehicle kilometers and fleet size can be

achieved, but…

….it costs at the

service level. From 10 to 20 minutes

of accepted waiting time makes no

difference

(24)

Sensitivity analysis

Level of service

• Served passengers

(25)

26 AUGUST 2019

OSLO STUDIET - TRAFIKDAGE

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Pick-up and drop-off locations

Scenario 1B

• Activity

(26)

Comparison with other

cities

(27)

Comparison of results with Lisbon and other studies

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OSLO STUDIET - TRAFIKDAGE

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(28)

Reduction in number of vehicles

(29)

Reduction in vehicle kilometres

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OSLO STUDIET - TRAFIKDAGE

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(30)

THANK YOU Want to know more?

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