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Weather	
  
Lars	
  Böcker 	
  Sofia	
  Thorsson	
  
&	
  
Cycling	
  
•  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	
  
Methods	
  
N	
   A$ri'on	
  
Wave	
  1:	
  Summer	
   950	
  
Wave	
  2:	
  Autumn	
   826	
   -­‐13,1%	
  
Wave	
  3:	
  Winter	
   718	
   -­‐13,0%	
  
0
5
10
15
20
25
-10
0
10
20
30
40
Precipitationinmm/windspeedinm/s
Maximumairtemperaturein˚C
Wind speed (m/s)
Precipitation
Ta_max (observed)
Ta_max (1980-2010)
August September October November December January February
Wind Speed
Precipitation
Ta(max), observed
Ta(max), 1980-2010
Snow Cover
Methods	
  
0
5
10
15
20
25
-10
0
10
20
30
40
Precipitationinmm/windspeedinm/s
Maximumairtemperaturein˚C
Wind speed (m/s)
Precipitation
Ta_max (observed)
Ta_max (1980-2010)
August September October November December January February
Wind Speed
Precipitation
Ta(max), observed
Ta(max), 1980-2010
Snow Cover
Methods	
  
Descrip;ves:	
  modal	
  split	
  
0%
20%
40%
60%
80%
100%
Ta (max)
Bicycle
Walking
Public transport
Car
20-­‐25˚C	
  
100
0
%
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	
  
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
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
Descrip;ves:	
  cycling	
  dura;ons	
  
0
5
10
15
20
25
Ta (max)
Bicycle as main mode
20-­‐25˚C	
  
25
0
5
10
15
20
min.
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	
  
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)	
  
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	
  
Three	
  thermal	
  parameters	
  
Air	
  temperature	
  (Ta)	
  
Air	
  temperature	
  (Ta)	
   Mean	
  Radiant	
  	
  
Temperature	
  (Tmrt)	
  
Three	
  thermal	
  parameters	
  
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	
  
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	
  
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	
  	
  
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	
  
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	
  	
  
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	
  
	
  
	
  
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	
  	
  
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:	
  

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Nectar 2013

  • 1. Weather   Lars  Böcker  Sofia  Thorsson   &   Cycling  
  • 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  
  • 3. Methods   N   A$ri'on   Wave  1:  Summer   950   Wave  2:  Autumn   826   -­‐13,1%   Wave  3:  Winter   718   -­‐13,0%  
  • 4. 0 5 10 15 20 25 -10 0 10 20 30 40 Precipitationinmm/windspeedinm/s Maximumairtemperaturein˚C Wind speed (m/s) Precipitation Ta_max (observed) Ta_max (1980-2010) August September October November December January February Wind Speed Precipitation Ta(max), observed Ta(max), 1980-2010 Snow Cover Methods  
  • 5. 0 5 10 15 20 25 -10 0 10 20 30 40 Precipitationinmm/windspeedinm/s Maximumairtemperaturein˚C Wind speed (m/s) Precipitation Ta_max (observed) Ta_max (1980-2010) August September October November December January February Wind Speed Precipitation Ta(max), observed Ta(max), 1980-2010 Snow Cover Methods  
  • 6. Descrip;ves:  modal  split   0% 20% 40% 60% 80% 100% Ta (max) Bicycle Walking Public transport Car 20-­‐25˚C   100 0 %
  • 7. Descrip;ves:  modal  split   0% 20% 40% 60% 80% 100% Ta (max) 0% 20% 40% 60% 80% 100% Precipitation (sum) 0% 20% 40% 60% 80% 100% Ws (avg.) Bicycle Walking Public transport Car 100 0 % 20-­‐25˚C  
  • 8. Descrip;ves:  cycling  frequencies   0,0 0,2 0,4 0,6 0,8 1,0 1,2 1,4 Ta (max) 20-­‐25˚C   Bicycle as main mode Bicycle as access/egress to public transport
  • 9. Descrip;ves:  cycling  frequencies   0,0 0,2 0,4 0,6 0,8 1,0 1,2 1,4 Ta (max) 0,0 0,2 0,4 0,6 0,8 1,0 1,2 1,4 Precipitation (sum) 0,0 0,2 0,4 0,6 0,8 1,0 1,2 1,4 Ws (avg.) 20-­‐25˚C   Bicycle as main mode Bicycle as access/egress to public transport
  • 10. Descrip;ves:  cycling  dura;ons   0 5 10 15 20 25 Ta (max) Bicycle as main mode 20-­‐25˚C   25 0 5 10 15 20 min.
  • 11. Descrip;ves:  cycling  dura;ons   0 5 10 15 20 25 Ta (max) 0 5 10 15 20 25 Precipitation (sum) 0 5 10 15 20 25 Ws (avg.) Bicycle as main mode 25 0 5 10 15 20 min. 20-­‐25˚C  
  • 12. Mul;variate  analysis   Meteorological  a$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  
  • 14. Three  thermal  parameters   Air  temperature  (Ta)  
  • 15. Air  temperature  (Ta)   Mean  Radiant     Temperature  (Tmrt)   Three  thermal  parameters  
  • 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: