Preferred citation style

Axhausen, K.W. (2017) Mobility in Smart Cities of the Future, Conference “Mobility and Energy Systems in Smart Cities of the Future”, Hong Kong, October 2017.

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HK meets ZRH 17

Mobility in Smart Cities of the Future

KW Axhausen

IVTETHZürich

October 2017

Acknowledgments

• A Erath, P Fourie, S Ordonez, L Sun

• A Loder, L Ambühl, M Menendez

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Basic assumptions

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Basic assumption 1

Accessibility ∼Opportunities, Speeds

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Basic assumptions2

Traffic is a system of moving, self-organising

Queues

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Basic assumption 3

The queues are the result of the crucial short-term interaction between capacity, i.e. the

current number of slots

for the desired speed and the

current demand

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Basic assumption 4

Travel demand (pkm) is a

normal good

i.e. it grows with

sinking “generalised costs”

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Basic assumption 5

The travellers chose their

average generalised costs

with their package of

locations (residence, work) andmobility tools

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Basic assumption 6

A person‘s travel demand is the

result of its activity participation

constrained by the currently

available time and money resources

and the currently

available slots and their speeds

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How to increase the usable number of slots?

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How to increase…

• Build more (and better/faster) slots

• Increase their usage through better technology

• Make overlooked ones visible through information

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How to manage the demand in the short term?

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How to manage …

• Price slots for their scarcity

• Stage their use through intentional delays

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How to manage the demand in the longer term?

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How to manage …

• Change the number of vehicles through taxation

• Change the commitment to public transport through the ticketing system

• Build alternatives

But don’t forget one can

• Lower the generalised costs

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Delay demand

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The car MFDsM

IV V

erke

hrsf

luss

[Fz/

Spur

-h]

MIV Verkehrdichte [Fz/Spur-km]

Innenstadt Wiedikon

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Lode

r et a

l., 20

17

3d MFD (Zürich, Schleifen) City centre

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Lode

r et a

l., 20

17

05

1015

2025

Wei

ghte

d av

erag

e sp

eed

[km

/h]

0 .2 .4 .6 .8 1Share of public transport users (paxpt ⁄ paxtot)

1000 pax3000 pax5000 paxobserved

Measure it and use the data

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Measure it …

• Cross-sectional flows

• Flows• Image/pattern recognition• RFID recognition

• Trips• Smart card data

• Daily schedules• GSM• GPS tracks

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CEPAS data for public transport

System:

• Tap in/tap out • Covering 98% of all stages, even if they are free of charge

Issues:

• Late tap-in/Early tap-out• Precision of on-board GPS units• Time between tap in/out and boarding/egress from MRT

trains

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MATSim applications around the world

© Marcel Rieser, senozonHK meets ZRH 17

MATSim Singapur

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Accounting for travel time and its variability

Dwell link

Stop to stop linkRoad network

Travel time distributions by time of day

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The reliability of a long bus line

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Reliability: Excess waiting time along line EW

orginal line

after split

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Sour

ce: F

ourie

(201

6).

Conclusion

• Measure to manage demand• Make use of the existing slots

• Have clear high-level policy goals

• Derive detailed policies from these

• Implement them consistently

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Questions?

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