mobility patterns and car use in madrid. cost action 355 changing behavior towards a more...

Mobility Patterns and car use in Madrid.

COST ACTION 355Changing behavior towards a more sustainable transport system

Daniel de la Hoz SánchezTransport Research Center (TRANSyT -UPM)www.transyt.upm.es


Aims

• Current mobility patterns and car use in Madrid• Some influential variables about car use in Madrid• Assessment of strategies for modal split in Madrid• More discussion ...


Foundation

• This presentation is based on:– Census 2001– Household Mobility survey 1996-2004– Madrid MARS Model


Madrid case study

Surface: 8,000 km2

Inhabitants: 5.8 millions (3.1 out of them in Madrid City)


Madrid case study – Metropolitan dependences


Madrid case study – Metropolitan dependences evolution


Modal share depending on area of residence.

0,0%

10,0%

20,0%

30,0%

40,0%

50,0%

60,0%

70,0%

Municipio de Madrid Corona metropolitana Corona Regional

Moto

Coche

Coche+ TP

TP

No motorizado

Otros

Madrid case study – Current Scene

Modal Share & Distance to CBD Census 2001 – Bounded mobility - JTW

Modal Share (%)

)Madrid Municipality Metropolitan Ring Regional Ring


Madrid case study – Current Scene

• Modal Share Trends

19,6%30,5%

53,9%66,7%

14,9%26,7%

45,7%

69,2%

0,0%

20,0%

40,0%

60,0%

80,0%

Almendra Perif CMetrop CReg

EDM96 EDM0480,4%

69,5%

46,1%33,4%

85,5%

73,3%

54,3%

30,8%

0,0%

20,0%

40,0%

60,0%

80,0%

100,0%

Almendra Perif CMetrop CReg

EDM96 EDM04

Private

PublicTransport

Distance to the CBD

Metropolitan Ring Regional RingMM Center MM Periphery

Metropolitan Ring Regional RingMM Center MM Periphery


Madrid case study – development number of residents

Development percentage of residents city centre and outskirts

(CAM, 2001)

Residence Location

0

500.000

1.000.000

1.500.000

2.000.000

2.500.000

3.000.000

3.500.000

1985

1987

1989

1991

1993

1995

1998

2000

2002

2004

Ha

b

0%

10%

20%

30%

40%

50%

60%

70%

c


Madrid case study – development number of employments

Employment sprawl

Worplace localitation

0%

10%

20%

30%

40%

50%

Almendra Periferia CoronaMetropolitana

CoronaRegional

88

96

04

Closer jobs & population = shorten travel distance?

Difussed commuting patterns (workforces in the familiy, workplace =daily-life destination where the longest distace is accepted,…)Trade off between accesibility and their space requirement (lack of affordable housing, high car accesibility on the metropolitan area, …)Type of jobs (specialized – non specialized)…


-2%

0%

2%

4%

6%

8%

10%

12%

14%

0 20 40 60 80 100 120

1998

2004

Madrid case study – City Structure

Accumulative differences of population and employment distribution in Madrid.

p(ri) e(ri)

ir

Ljjji rerpDA ))()((

Difference increased = dispersion forceDistance to the City Center

DA

Assuming that workers try to minimize the travel distance to working place,…


Polycentrism have not carry out self-sufficient urban villagesDecline the importance of Mass transit (not surprising : rise of polycentrism has come about through increased auto ownership).

Madrid Case Study – System Supply

Distance from de CBD - Average Travel Distance to Work

0

5

10

15

20

25

30

35

40

45

50

0 20 40 60 80 100 120Distance to the City Center

Ave

rag

e D

ista

nce

To

Wo

rk (

km

)

km

Census 2001 – Bounded mobility - JTW


POBLACIÓN OCUPADOS CLASIFICADA SEGÚN LUGAR DE TRABAJO - MUJERES

0,00%

10,00%

20,00%

30,00%

40,00%

50,00%

60,00%

70,00%

80,00%

90,00%

En mi propiodomicilio

En varios municipios En este municipio En otro municipio

Municipio de Madrid

Corona metropolitana

Corona Regional

POBLACIÓN OCUPADOS CLASIFICADA SEGÚN LUGAR DE TRABAJO- HOMBRES

0,00%

10,00%

20,00%

30,00%

40,00%

50,00%

60,00%

70,00%

80,00%

90,00%

En mi propiodomicilio

En varios municipios En este municipio En otro municipio

Municipio de Madrid


Corona Regional

POBLACIÓN OCUPADOS CLASIFICADA SEGÚN LUGAR DE TRABAJO

0,0%

10,0%

20,0%

30,0%

40,0%

50,0%

60,0%

70,0%

80,0%

90,0%

En mi propio domicilio En varios municipios En este municipio En otro municipio

Municipio de Madrid


Corona Regional

Madrid case study – geographic dependence & gender

• More distance to the City Center – More dependence to other municipalities. Residence location choice – workplace location?.

• Women have a geographically less extended daily action space.


Spatial working dependence

Spatial Working dependence - Men Spatial Working dependence - Women


Madrid case study – gender

MALE

0,0%

10,0%

20,0%

30,0%

40,0%

50,0%

60,0%

70,0%

80,0%

Moto Coche Coche+ TP TP No motorizado Otros

Municipio de Madrid


Corona Regional

FEMALE

0,0%

10,0%

20,0%

30,0%

40,0%

50,0%

60,0%

70,0%

80,0%

Moto Coche Coche+ TP TP No motorizado Otros

Municipio de Madrid


Corona Regional



Madrid case study – Car ownership

• Current Scene of Car ownership

Motoritation Rates

682,31

0,00

200,00

400,00

600,00

800,00

1.000,00

1.200,00

1.400,00

Vehículos/red total (km) Vehículos/1000 habitantes


Average Income per capita (Municipalities) VS Average HH Car Ow nership

00,2

0,40,6

0,81

1,2

1,41,6

1,82

4000 8000 12000 16000 20000 24000


Madrid Municipality



Madrid case study – Demographics

• Demographic situation influences on Car ownership and usage



COMPARACIÓN DEL TAMAÑO MEDIO FAMILIAR1996-2004

2,70 2,95 2,823,103,002,633,41 3,11

0,00

1,00

2,00

3,00

4,00

Almendracentral

Periferia urbana Coronametropolitana

Corona regional

Coronas

Per

son

as/h

og

ar

TMF 1996 TMF 2004

0,750,87

1,100,97

0,720,91

1,14 1,10

0,00

0,50

1,00

1,50

Almendra Periferia CMetrop CRegional

EDM96 EDM04

Motorización Personal

0,28 0,290,32 0,31

0,270,31

0,37 0,40

0

0,1

0,2

0,3

0,4

0,5

Almendra Periferia Corona Metropolitana Corona Regional

Car/hab 96

Car/hab 04

HH size – 96/04HH car ownership – 96/04

Individual MotorisationMotoritation

0

100000

200000

300000

400000

500000

600000

700000

800000

900000

1000000

Almendra Periferia CoronaMetropolitana

Corona Regional

0,0%

1,0%

2,0%

3,0%

4,0%

5,0%

6,0%

7,0%

8,0%1996

2004

Anual growth rate

Higher avaliability of cars for individuals & higher travel demand for personal needs


M-30 (1992)M-40 (1996)M-50 (1996 -…)




• Inner city inhabitants PT for shorter travels • While outer rings, the contrary occurs

Average Distance Traveled

0

5

10

15

20

25

30

Almendra Zona A Zona B1 Zona B2 Zona B3 Zona C1 Zona C2

Distance to the City Center

kms

PT

Car

Average Time

0

10

20

30

40

50

60

70

Almendra Zona A Zona B1 Zona B2 Zona B3 Zona C1 Zona C2

Distance to the City Center

Min

PT

Car

Door to door speed Vs Average trip distance

0

5

10

15

20

25

30

35

40

0 5 10 15 20 25 30

Kms

Do

or

to D

oo

r S

pee

d (

kms/

h)

PT

Car

EDM 04

Automobile commute time tend to be lower in polycentric than in monocentric regions.


0

5

10

15

20

25

30

35

40

45

50

0 10 20 30 40 50Distance

Do

or

to d

oo

r sp

eed

TP

Car

Madrid Case Study – System Supply and mode choice

• Attending to spatial relations: PT can reach High quotas of Modal Share.– Inner and radial relation close Travel Time relation PT

and Car = high percentage of PT.– Tangential flows on Periphery , Metropolitan and Regional

Areas = high differences

km

0

10

20

30

40

50

60

70

80

90

0,4 0,45 0,5 0,55 0,6 0,65 0,7 0,75

Tcar/Ttp

Mo

dal

Sh

are

(%ca

r)

% car

Km

/h


TRAVEL TIME TO WORK Vs HH CAR OWNERSHIP

0

1 2

3 or +

0%

5%

10%

15%

20%

25%

30%

Menos de 10minutos

Entre 10 y 20minutos



Entre 45minutos y 1

hora

Entre 1 hora yhora y media

Más de hora ymedia

TRAVEL DISTANCE TO WORK Vs CAR HH OWNERSHIP

0

1

2 , 3 o +

0

0,05

0,1

0,15

0,2

0,25

0,3

0,35

0,4

0,45

5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80

Madrid Case Study – Car Ownership and Trip length

AVERAGE door to door SPEED Vs HH CAR OWNERSHIP

0

5

10

15

20

25

30

35

40

0 1 2 3+


Time saved invested on distance and speed? = spread out or induced trips

Km/h

Km

Cars per HH


The Madrid Case Study – System Supply

• Travel time budget : relatively constant

min

Higher speeds, allow people to cover longer distance within their time and money constrains. This spatial opportunities will increase including the distance between their residence and job locations, resulting in the dispersion of residences and jobs.

TOTAL TRAVELING TIME

0

20

40

60

80

100

Almendra Zona A Zona B1 Zona B2 Zona B3 Zona C1 Zona C2Serie1

TOTAL TRAVELING TIME

0

20

40

60

80

100

Almendra Zona A Zona B1 Zona B2 Zona B3 Zona C1 Zona C2Serie1

Presumption: An individual traveler attempts to travel appropriate distance by various modes such that the travel utility is optimized subject to time and money constraints (Connected to Zahavi’s works)


MAIN CONCLUSIONS … OR QUESTIONS

• Close interaction between travel demand, system supply and urban structure. Car use and travel behavior is affected by:– Residential location affects travel behavior– Social, demographical or cultural dynamics affects car use– Spatial configuration (size, land use, transport system,

neighborhood design,…) – Activity patterns have become more complex and diverse.

• Part of the time save by speed increases are traded off for more travel distance= suggest : the generation of induce travel?

• If travel speeds increase and land use is uncontrolled, city starts to expand from within. Travelers find better spatial opportunities (residence and job locations) within their travel constraints such as travel time budget

• Travel underpriced (externalities) city spreads out ; higher motorization ; cost-benefits analysis (CBA) enhance land use devoted to roads (time saving).


Some strategies in Madrid – An assessment approach

• Public transport encouragement, basically based on Infrastructure (Interchanges Hubs, Metro, Suburban buses, Park and ride infrastructure on suburban areas, alternative fueled vehicles (CNG, Hydrogen, Electrical),…)

• M-30 renewal, Radial toll highways, M-50 orbital ring (in mind)• Parking regulation, Restricted access areas• HOV (High Occupant Vehicle) Priority & Suburban Bus Exclusive

Lanes to the City Center• Car-Free Districts and Pedestrianized Streets• …• High-speed Train – Polycentric Macro Metropolitan Area• ¿Building standards? New Urbanism• Education and Marketing? Initiative in Madrid of “muevete verde” –

labeling (in mind)• Cycling? New Director Plan (on mind)• Land use and transport planning Integration? • LEZ?• Business mobility plans?


Some strategies in Madrid – Land Use and Transport Planning

MADRID – Line 1 Extension • 26.000 Housing units. Periphery of Madrid. • Land use and transport integration.• Urban densities and design in coherence with Public Transport• Value capture for financing public transport. 25 MMEur.


Some strategies in Madrid –Bus exclusive Transit & HOV Lane

INITIATIVE: f transport policy instrument based on A6 . Extension to other radial HC arterials (100 kms)

SITUATION: high share of people commutes between the periphery and the core city, causing high levels of peak hour congestion.

the question is whether this transport policies contribute to the high level objective of sustainability.



Travel time (minutes) for 15 km access road; A6 morning peak, 2001

Source: Modelización y evaluación de medidas de gestión en corredores urbanos (Monzón et al, 2003)

Greater reliability of suburban bus services has fostered their use

Bus patronage has increased significantly - from 24% in 1991 to 36% in 2001.



Long Term Effects on Regional Sustainability MARS(Metropolitan Activity Relocation Simulator)

Land Use Market

Residence Location Workplace LocationTransportSub-model

Land Use: Residential

Location Choice Sub-model

Land Use: Business Location

Choice Sub - Model

Accessibility

(Pfaffenbichler, 2003)

Model simulation can illustrated and synthesize already existing knowledge about transportation consequences of alternative urban structures, measures,… but cannot be used to investigate whether the assumption on which the model is based are correct ie: TIME BUDGET



Accessibility



LAND USE:Land Consume NEW LANES = Higher sprawl than REPLACE LANES



CAR

35

37

39

41

43

45

47

49

51

53

55

1996

1998

2000

2002

2004

2006

2008

2010

2012

2014

2016

2018

2020

2022

2024

2026

Mill

on

es

Do minimum

NEW

REPLACE

TP

45,00

50,00

55,00

60,00

65,00

70,00

1996

1998

2000

2002

2004

2006

2008

2010

2012

2014

2016

2018

2020

2022

2024

2026

Mill

on

es

Do minimum

NEW

REPLACE

MECANIZE TRIPS-KM

75,00

80,00

85,00

90,00

95,00

100,00

105,00

110,00

115,00

120,00

125,00

1996

1998

2000

2002

2004

2006

2008

2010

2012

2014

2016

2018

2020

2022

2024

2026

Mill

on

es

Do minimum

NEW

REPLACE

CAR

20

21

22

23

24

25

26

27

28

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31

Mill

on

es

Serie1

Serie2

Serie3

CAR

10

12

14

16

18

20

22

24

26

28

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31

Mill

on

es

Serie1

Serie2

Serie3

CAR

20,00

22,00

24,00

26,00

28,00

30,00

32,00

34,00

36,00

38,00

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31

Mill

on

es

Serie1

Serie2

Serie3

CAR

10,00

15,00

20,00

25,00

30,00

35,00

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31

Mill

on

es

Serie1

Serie2

Serie3

Mechanized

Car

PT

24h

peak Off- peak

trips-kmTime savings in the peak hour are spent on the off-peak hourBetter PT modal quota, but higher transport demand than in scenario do-minimun



New modal split and travel demand affect emission

90

100

110

120

130

2005 2010 2015 2020 2025

Year

An

nu

al E

mis

sio

ns

(2

00

5=

10

0)

NOx DoMin

NOx New Lanes

NOx Replace Car Lanes

VOC DoMin

VOC New Lanes

VOC Replace Car Lanes

NOX and VOC = significantly reduced on REPLACE scenario

CO2 = reduction relatively small and in danger on the long term



-158.5-585.2Present Value of finance (PVF)

658.8716.6Total Objective Function

12.76.9Greenhouse gas emissions

-5.5-36.0Accidents, local emissionsSociety (external costs)

17.71.3Fuel tax, ParkingGovernment

346.3151.1Residences

0.00.0MaintenanceRoad

123.8141.4Revenues

0.0-5.9Operating costs

-300.0-722.0Investment

Public transport

Operator

-340.7-148.0Rent, mortgageResidences

-81.233.3Money

-255.1112.7Time savingsCar

1,140.81,181.8Time savingsPublic transport

User

“Replace Lanes”“New Lanes”

Bus Lanes

Source of costs and benefits

-158.5-585.2Present Value of finance (PVF)

658.8716.6Total Objective Function

12.76.9Greenhouse gas emissions

-5.5-36.0Accidents, local emissionsSociety (external costs)

17.71.3Fuel tax, ParkingGovernment

346.3151.1Residences

0.00.0MaintenanceRoad

123.8141.4Revenues

0.0-5.9Operating costs

-300.0-722.0Investment

Public transport

Operator

-340.7-148.0Rent, mortgageResidences

-81.233.3Money

-255.1112.7Time savingsCar

1,140.81,181.8Time savingsPublic transport

User

“Replace Lanes”“New Lanes”

Bus Lanes

Source of costs and benefits

Both result in a welfare surplus but ….

- +

Multicriteria Analysis?


Conclusions

• In the CBA, the new bus lanes result in a welfare surplus. The welfare surplus is mainly generated by time savings.

• Bus lanes contribute to the overall objective of sustainability. But nevertheless it has to be mentioned that their contribution is rather small. The environmental benefits are small and the instruments can only be a small part of wider strategy.

• Concerning bus lanes from the viewpoint of sustainability the scenario “Replace Car Lanes” clearly has to be favoured.

• The objective of sustainability requires a holistic perspective for achieve a sustainable urban development.– An increase of planning variables = higher complexity.– Strategies should be based on a combination of different measures.– Policy integration and coordination. In Madrid, transport is coordinated all

along the region by the Regional Transport Consortium, but, what about land use development?, and transport infrastructure? Environmental issues?

• A change on focus: from just net and capacity providing to an equilibrium in the supply and the demand: Forecast and Prevent = Planning.


Further discussion ?

• Modal split = complex interaction between urban structure, density and neighborhood design, HH status, location, and system supply. Developing a higher quality of PT is enough for achievement of sustainable scenes? Underpricing car usage (congestion, externalities,…) = road based model?.

• Travel patterns influenced by city structure. Should the city be allowed to become dispersed, or should cities become more compact?. Overcrowding could mean loss of urban quality? Optimum Neighborhood design (Densities, Transit oriented, mix-land uses,…)? Thinking on accessibility rather than mobility or traffic?.

• Travel patterns influenced by location. Links between population and employment decentralization and lower distance travel always replicated (Interaction with transport system supply and land use market (affordable housing))?. Location residence decisions related to more variables than employment place (leisure, buying,…)?

• Strategies for sustainable mobility could not be based on isolation actions. No single measure will achieve sustainable scenarios for urban mobility. Integrated of synergies of different approaches (vertical and horizontal) is possible?

• …• And a provocative one: How to counterbalance Automobile industry

marketing? Automobile Industry is similar to Smoking Industry?

mobility patterns and car use in madrid. cost action 355 changing behavior towards a more...

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