Mobility in Smart Cities of the Future...Preferredcitationstyle Axhausen, K.W. (2017) Mobility in...
Transcript of Mobility in Smart Cities of the Future...Preferredcitationstyle Axhausen, K.W. (2017) Mobility in...
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.
.
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
HK meets ZRH 17
Basic assumptions
HK meets ZRH 17
Basic assumption 1
Accessibility ∼Opportunities, Speeds
HK meets ZRH 17
Basic assumptions2
Traffic is a system of moving, self-organising
Queues
HK meets ZRH 17
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
HK meets ZRH 17
Basic assumption 4
Travel demand (pkm) is a
normal good
i.e. it grows with
sinking “generalised costs”
HK meets ZRH 17
Basic assumption 5
The travellers chose their
average generalised costs
with their package of
locations (residence, work) andmobility tools
HK meets ZRH 17
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
HK meets ZRH 17
How to increase the usable number of slots?
HK meets ZRH 17
How to increase…
• Build more (and better/faster) slots
• Increase their usage through better technology
• Make overlooked ones visible through information
HK meets ZRH 17
How to manage the demand in the short term?
HK meets ZRH 17
How to manage …
• Price slots for their scarcity
• Stage their use through intentional delays
HK meets ZRH 17
How to manage the demand in the longer term?
HK meets ZRH 17
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
HK meets ZRH 17
Delay demand
HK meets ZRH 17
The car MFDsM
IV V
erke
hrsf
luss
[Fz/
Spur
-h]
MIV Verkehrdichte [Fz/Spur-km]
Innenstadt Wiedikon
HK meets ZRH 17
Lode
r et a
l., 20
17
3d MFD (Zürich, Schleifen) City centre
HK meets ZRH 17
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
HK meets ZRH 17
Measure it …
• Cross-sectional flows
• Flows• Image/pattern recognition• RFID recognition
• Trips• Smart card data
• Daily schedules• GSM• GPS tracks
HK meets ZRH 17
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
HK meets ZRH 17
MATSim applications around the world
© Marcel Rieser, senozonHK meets ZRH 17
MATSim Singapur
HK meets ZRH 17
Accounting for travel time and its variability
Dwell link
Stop to stop linkRoad network
Travel time distributions by time of day
HK meets ZRH 17
The reliability of a long bus line
HK meets ZRH 17
Reliability: Excess waiting time along line EW
orginal line
after split
HK meets ZRH 17
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
HK meets ZRH 17
Questions?
HK meets ZRH 17