2. • Exis;ng
studies
lack
a>en;on
for
dura;ons
• Exis;ng
studies
have
meteorological
shortcomings:
– Assumed
linear
rela;onships
– Thermal
condi;ons
are
only
analysed
by
air
temperatures
– Weather
parameters
are
oFen
singled
out
– Need
for
analysing
the
integrated
effects
of
weather
Background
6. Descrip;ves:
modal
split
0%
20%
40%
60%
80%
100%
Ta (max)
Bicycle
Walking
Public transport
Car
20-‐25˚C
100
0
%
7. Descrip;ves:
modal
split
0%
20%
40%
60%
80%
100%
Ta (max)
0%
20%
40%
60%
80%
100%
Precipitation (sum)
0%
20%
40%
60%
80%
100%
Ws (avg.)
Bicycle
Walking
Public transport
Car
100
0
% 20-‐25˚C
8. Descrip;ves:
cycling
frequencies
0,0
0,2
0,4
0,6
0,8
1,0
1,2
1,4
Ta (max)
20-‐25˚C
Bicycle as main mode
Bicycle as access/egress to public transport
9. Descrip;ves:
cycling
frequencies
0,0
0,2
0,4
0,6
0,8
1,0
1,2
1,4
Ta (max)
0,0
0,2
0,4
0,6
0,8
1,0
1,2
1,4
Precipitation (sum)
0,0
0,2
0,4
0,6
0,8
1,0
1,2
1,4
Ws (avg.)
20-‐25˚C
Bicycle as main mode
Bicycle as access/egress to public transport
11. Descrip;ves:
cycling
dura;ons
0
5
10
15
20
25
Ta (max)
0
5
10
15
20
25
Precipitation (sum)
0
5
10
15
20
25
Ws (avg.)
Bicycle as main mode
25
0
5
10
15
20
min.
20-‐25˚C
12. Mul;variate
analysis
Meteorological
a$ributes
(Daily
level)
-‐Maximum
Ta
-‐Maximum
Tmrt
-‐Maximum
PET
-‐Precipita;on
sum
(between
6
and
12
a.m.)
-‐Wind
speed
(between
6
and
12
a.m.)
Cycling
behaviour
-‐Mode
choice
-‐Cycling
frequencies
(per
person
per
day)
-‐Cycling
dura;on
(total
per
person
per
day)
13. Mul;variate
analysis
Spa'otemporal
a$ributes
-‐Residen;al
environment
-‐Weekday/weekend
-‐Morning
peak/
evening
peak/off-‐peak
day;me/
offpeak
nigh`me
-‐Daylight/darkness
Personal
a$ributes
-‐Age,
gender
-‐Ethnicity
-‐BMI
-‐Educa;on
-‐Weekly
work
dura;on
-‐Working
hour
flexibility
-‐Bicycle
ownership
-‐Public
transport
card
Household
a$ributes
-‐Household
type
-‐Car
ownership
-‐Household
income
-‐Garden/balcony
size
-‐House
air-‐condi;oning
AAtudes/habits
-‐Urban/countryside
person
-‐Environmental
concern
-‐A`tude
towards
seasons
Meteorological
a$ributes
(Daily
level)
-‐Maximum
Ta
-‐Maximum
Tmrt
-‐Maximum
PET
-‐Precipita;on
sum
(between
6
and
12
a.m.)
-‐Wind
speed
(between
6
and
12
a.m.)
Cycling
behaviour
-‐Mode
choice
-‐Cycling
frequencies
(per
person
per
day)
-‐Cycling
dura;on
(total
per
person
per
day)
Trip
a$ributes
-‐Trip
purpose
(work/
study,
errands,
social
visit,
leisure)
-‐Type
of
trip
(rou;ne,
planned,
impulsive
16. Air
temperature
(Ta)
Mean
Radiant
Temperature
(Tmrt)
Physiological
Equivalent
Temperature
(PET)
à
Air
temperature
à
Radiant
heat
load
à
Wind
speed
à
Humidity
Three
thermal
parameters
17. 14 August 2012
- semi-cloudy day no precipitation, mean wind speed 1.3 m/s (1.1m)
0
10
20
30
40
50
60
70
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Hour
TemperatureC
Ta Tmrt PET
Ta
Tmrt
PET
Three
thermal
parameters
Weather
change:
from
clear
and
calm
to
cloudy
and
windy
18. Results:
mode
choice
Ta
model
Cycling
v
car
Walking
v
car
Publ.
transp.
v
car
Ta
bell-‐shaped
24˚C
+
Wind
speed
-‐
Precipita;on
sum
-‐
-‐
Tmrt
model
Cycling
v
car
Walking
v
car
Publ.
transp.
v
car
Tmrt
bell-‐shaped
52˚C
++
-‐
Wind
speed
-‐
Precipita;on
sum
-‐
-‐
PET
model
Cycling
v
car
Walking
v
car
Publ.
transp.
v
car
PET
bell-‐shaped
30˚C
+++
Precipita;on
sum
-‐
-‐
-‐
Mul'nomial
LOGIT
model
(clustered
S.E.)
Wald
Chi2
=
3176
Wald
Chi2
=
3242
Wald
Chi2
=
3173
19. Results:
cycling
frequencies
Tmrt
Model
#
Cycling
trips
/
person
/
day
Tmrt
bell-‐shaped
52˚C
+++
Wind
speed
Precipita;on
sum
-‐
-‐
-‐
PET
(model)
#
Cycling
trips
/
person
/
day
PET
bell-‐shaped
33˚C
+++
Precipita;on
sum
-‐
-‐
-‐
Nega've
Binomial
model
(clustered
S.E.)
Ta
Model
#
Cycling
trips
/
person
/
day
Ta
bell-‐shaped
24˚C
+++
Wind
speed
Precipita;on
sum
-‐
-‐
Wald
Chi2
=
407
Wald
Chi2
=
428
Wald
Chi2
=
415
20. Results:
cycling
dura;ons
Ta
(model)
Cycling
hours
/
person
/
day
Ta
bell-‐shaped
24˚C
+++
Ws
-‐
-‐
-‐
Precip.
-‐
-‐
Tmrt
Model
Cycling
hours
/
person
/
day
Tmrt
bell-‐shaped
52˚C
+++
Ws
-‐
Precip.
-‐
-‐
-‐
PET
(model)
Cycling
hours
/
person
/
day
PET
bell-‐shaped
31˚C
+++
Precip.
-‐
-‐
-‐
TOBIT
model
(clustered
S.E.)
Wald
Chi2
=
235
Wald
Chi2
=
244
Wald
Chi2
=
240
21. Summary
• Thermal
condi;ons
have
non-‐linear
bell
shaped
effects
on
cycling
• The
PET
and
Tmrt
models
perform
be>er
than
the
Ta
models
• Precipita;on
and
wind
have
nega;ve
linear
effects
on
cycling
• Exchange
mostly
between
cycling
and
the
car,
less
for
other
modes
• Effects
on
dura'ons
are
stronger
than
on
frequencies
• Effects
are
stronger
for
leisure
trips
than
for
u;litarian
trips
22. Conclusion
• Cycling
is
most
sensi;ve
to
weather
• Complexity
weather
and
mobility
• Non
linear
rela;onships,
op;mums
and
thresholds
• Combining
parameters
(Tmrt
or
PET)
be>er
than
singling
out
(Ta)
• Nevertheless
Ta
is
s;ll
very
useful:
– widely
available
– easily
interpretable
– compa;ble
to
weather
forecasts
and
climate
change
23. Thank
you!
Böcker
&
Thorsson
(2013)
Integrated
weather
effects
on
cycling
shares,
frequencies
and
dura;ons
in
Ro>erdam,
the
Netherlands
Exis;ng
knowledge
on
weather
and
transport
mode
choices:
Böcker,
Dijst
&
Prillwitz
(2013)
“Impact
of
weather
on
travel
behaviour
in
perspec;ve:
a
literature
review”,
Transport
Reviews
L.Bocker@uu.nl
Funded
by:
