URBAN ACCESSIBILITY / MOBILITY INDEX …...2 CONTENTS Executive summary p 3 Chapter I – Objectives, possible dimensions and indicators for an urban accessibility/mobility index p

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URBAN ACCESSIBILITY / MOBILITY INDEX

FEASIBILITY STAGE REPORT

JUNE 2010

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CONTENTS

Executive summary p 3 Chapter I – Objectives, possible dimensions and indicators for an urban accessibility/mobility index p 5 Chapter II – In-house regional testing of a multi-dimensional urban accessibility/mobility index p 8 Chapter III – Availability of urban transport data at international level p 12 Chapter IV – The World Bank initiative about urban data: The Global City Indicators Facility p 14 Chapter V – Recommendations p 15 References p 16 Annex A – Detailed material of the in-house regional testing p 17 Annex B – List of the current GCIF urban transport indicators p 24 Annex C – Transport sustainability ranking of 84 international cities p 26

based on transport system performance, by J Kenworthy, 2008

2010 - The World Bank – Transport Anchor

The findings, interpretations, and conclusions expressed here are those of the authors and do not necessarily reflect the views of the Board of Executive Directors of the World Bank, or the Government they represent.

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EXECUTIVE SUMMARY Urban transport concerns are increasing worldwide. The urban population has been increasing rapidly in developing countries and this trend is projected to continue. Urbanization is accompanied by increasing income levels and consequently an increasing trend in motor vehicle ownership. These have resulted in severe congestion, adversely impacting access to jobs, education, health care and other needs. It has also led to increased air pollution, adversely impacting the health of the people. Road accidents have increased and more people get killed in such accidents. It is for these reasons that the transport strategy of the Bank for 2008-12 calls for increased involvement in the urban transport sector. In order to be able to assess the impact of interventions in this sector as also to compare cities across the world, in terms of how their urban transport systems perform, it is essential to have an appropriate index. It is for this reason that the bank decided to develop a suitable index. However, in doing so, it faced the following challenges:

1. Urban transport has multiple dimensions and cannot be comprehensively assessed by a single indicator

2. There is acute lack of reliable data on different aspects concerning urban transport. Therefore, it needed a compromise between developing an index that is comprehensive and developing one that is practical, especially in the context or the data available as well as the practical difficulties in collecting the data. This effort reflects such a compromise. This report is a collective work. With external contribution from K. Gwilliam and in-house contributions from OP Agarwal, G. Darido, B. Popescu, S Zimmerman, G. Gauthier, J.C. Crochet, this feasibility stage report was produced by M. Davy, and peer-reviewed by A. Kumar, S. Mehndiratta and D. Hoornweg.

The requirements set for this exercise are that the index / indicators should be (i) easy to collect; (ii) easy to interpret and understand; and (iii) replicable from one city to another. As stated earlier, there is a

trade-off between comprehensiveness and practical feasibility. . The five chapters of the report highlight the following:

- The multiple dimensions of urban transport and possible indicators that would reflect each of these dimensions (chap I)

- The results of an in-house regional testing of the indices (chap II) - A note on the current availability of urban transport data, at the international level, for

such an index (chap III) - A note on the initiative taken in the World Bank, through the GCIF to tackle the lack of

data (chap IV) - The recommendations being made (chap V)

In Chapter I, the following dimensions of urban transport have been taken into account:

Accessibility/mobility (average one way travel time to reach the work place

Safety (number of road fatalities per 100,000 population)

City GHG emissions or City air pollution (GHG emissions per capita or PM10 concentration)

Affordability of the urban transport system (user cost of 60 one way trips as % of per capita monthly income)

Mode share (Share of trips undertaken by public transport and non motorized modes)

Public Transport supply (length of Public Transport Routes or Public Transport fleet per 100,000 population)

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In Chapter II, the results of a test of the above indices, which reflect the different dimensions as mentioned, covering 3 cities, have been presented. For easy representation the results have been presented in the form of a star diagram, as shown below, with each spoke representing one dimension.

For the urban transport dimensions identified above, the actual availability of data, from international institutional sources, has been reviewed and results presented in Chapter III. The status is summarized in the table below

Clearly the available data is inadequate and does not lend itself to making meanginful comparisons across cities or over time in a city. Therefore, the World Bank, along with UN-Habitat, World Economic Forum, OECD, and other partners, initiated the development of the Global City Indicators Facility (GCIF) to address this issue. GCIF develops indicators that are standardized to enable cross-city comparisons and third-party verification and are simple enough to facilitate data collection by cities. 106 cities are now partners of GCIF. The GCIF indicators cover four of the 6 dimensions envisaged for an urban mobility index. Data is not yet available fully, though some data have been provided by a few cities and is being reviewed by GCIF. Details of this initiative have been presented in Chapter IV. Chapter V presented the recommendations. Essentially two recommendations have been made:

1. All support be given to GCIF in developing the required data base that would enable meaning comparisons to be made, and

2. Explore the feasibility of developing a set of core indicators that would enable meaningful reporting results of the Bank‟s urban transport portfolio.

0.0

2.0

4.0

6.0

8.0

10.0

urban transport accessibility

urban transport safety

city environment (GHG or PM10)

urban transport affordability

public transport usage

public transport supply

City 1

City 2

City 3

Availability of city data at international

level World Bank UN Habitat UITP GCIF Jane’s

Urban Accessibility / Mobility

Travel time to work 1998


Data as of 1995

Not Available

Not Available

Urban Transport Safety

Not Available

Not Available

Data as of 1995

Fatalities Not yet

Available Not Available

City air pollution or City GHG emissions

AQ 2006 GHG

2007/97

Not Available

Not Available

AQ and GHG Not

yet Available Not Available

Public Transport Affordability

PT fares 2005 (not published)

Not Available

Data as of 1995

Not Available

Not Available

Urban transport modal Share or public transport

usage

Not Available

Modal share 1995-

2002

Data as of 1995

PT trips Not yet Available

PT operators journeys and vehicle-km

2004/08 (copyright issue)

Urban Transport Supply

Not Available

Car ownership 2000/04

Data as of 1995

PT length Not yet

Available

PT operators route length and

fleet 2004/08

(copyright issue)

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CHAPTER I – OBJECTIVES, POSSIBLE DIMENSIONS AND INDICATORS FOR AN URBAN ACCESSIBILITY / MOBILITY INDEX

Objectives Given the increasing importance of the urban transport sector, the World Bank decided to explore the feasibility of setting up a possible urban accessibility/mobility index. The purpose of such an index would be to provide an indication/measurement of how urban transport systems compared one another across cities, and for a given city how it evolves over time. The aim is to provide client cities and countries, along with the World Bank and other institutions, with some evidence-based information. Such indices already exist for the rural roads and logistic sectors and some other sectors of the World Bank.

The requirements set for this exercise are that the index should be (i) easy to collect; (ii) easy to interpret; and (iii) replicable from one city to another. There is a trade-off between comprehensiveness

leteness and feasibility. Moreover, such an initiative could only be fruitful if data is updated on a regular basis and easily available. The overall strategy of the World Bank for transport, including urban transport, is stated in the World Bank Transport Business Strategy for 2008-2012. This strategy is “Safe, Clean and Affordable Transport for Development”. The policy recommendation of the Bank for urban transport, along with the analysis of the key issues in the urban transport sector and international good practice on urban transport sector, are detailed in the World Bank Transport Paper Urban Transport for Development 2008 and World Bank Framework for Urban Transport Projects 2008. The core urban transport policy of the Bank is to help countries and cities develop policies that favor public transport and non-motorized transport modes, versus private cars and motorcycles. Any urban mobility index developed by the World Bank should reflect these objectives. The outline design of the urban accessibility/mobility index explored in this feasibility stage report, has been envisaged to indeed reflect these objectives.

Multi-dimension and complexity of urban transport Urban transport is complex and multi-dimensional, and it is difficult to reduce the sector to a few indicators. Outcomes of urban transport interventions help improve mobility and access to various needs. They also have other important outcomes related to clean air, reduced accidents and reduced GHG impacts. The indices should, ideally be able to reflect the impact on all these outcomes. It was therefore thought that the use of an index with several dimensions alone would provide a comprehensive assessment of the impacts. Besides, it was thought that the employment of a set of indicators might make comparison across cities more feasible.

Selection of a set of key dimensions of urban transport for the possible index Therefore, certain urban transport dimensions have to be selected, along with some possible indicators to measure these dimensions. Such a selection is difficult given that urban transport impact a lot of aspects of urban life (accessibility to work, education and social networks, gender and social groups, environmental issues, etc). The selection of dimensions should be linked to the objectives that we wish to achieve. This is the approach that we have taken in this exercise. The six main objectives that we seek to achieve in any urban transport intervention are the following:

(a) Improve access to jobs, education, healthcare, etc – by way of reducing the travel time

(b) reducing the incidence of road accidents (c) improving air quality and reducing the emission of GHG (d) enhancing affordability of urban travel

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(e) encouraging greater use of sustainable modes of travel, like public transport and NMT,

through the adoption of policies that promote the use of such modes

The last dimension which is proposed to be included, and which is generally the first step taken for monitoring, is:

(f) enhancing the supply of public transport These above 6 dimensions (urban transport accessibility and mobility, urban transport safety, urban transport impact on climate change and air pollution, urban transport modal share and urban transport supply) are examined in some detail below to explore possible simple indicators to reflect these.

Possible simple indicators to reflect these urban transport dimensions

(a) Urban transport accessibility and mobility

The Millennium Development Goals constitutes the core of the World Bank‟s development strategy. Within this framework, a major outcome of urban transport is both accessibility to work places, to schools, to health centers, and social networks, and the mobility to reach these places. This dimension assumes even greater significance if seen in the context of the forecasted continuous growth of population and car ownership in developing cities as congestion will continue to grow, altering accessibility and mobility. The limitations on the practicability of an accessibility indicator mean that only a simple indicator can be envisaged. Therefore it is suggested to consider at this stage the indicator “average travel time to go to work (one way)”.

(b) Urban transport safety

Road safety is an important transport priority of the World Bank. Statistical evidence shows that by 2030 road deaths and injuries will rank 7

th as a global burden of disease, ahead of tuberculosis (10

th) and

malaria (15th). Current statistics indicate 1.3 million deaths and up to 50 million injuries take place each year on the world‟s roads. 90% of these cases are in Low and Middle Income Countries. The above data poignantly reveals a road safety performance gap between rich and poor countries in an area of rapidly increasing public health priority. Safety can be measured in terms of mortality or morbidity rates, either per person or per vehicle. Fatalities, though less common are usually better reported. Moreover they are less dependent on local interpretation of the definition than injuries of a defined level of severity. So, pending data availability, the indicator envisaged for this dimension could be the number of fatalities on urban roads. It is possible to standardize this indicator by population.

(c) Urban transport impact on climate change and air pollution

Climate change is expected to affect developing countries the hardest. The transport industry now produces an estimated 15 percent of global greenhouse gas emissions. Helping country clients to mitigate climate change is a priority of the World Bank. The core urban transport policy of the World Bank contribute to this, as it is to help country clients develop public transport services and secured non-motorized transport modes, instead of increasing reliance on private car and motorcycle transport. The World Bank urban development team is working on the development of the indicator of greenhouse gas emissions per capita, which could be used in an urban mobility index. This is nevertheless a measure of all GHG emissions (not those specific to transport). Air pollution has an important impact on public health. As far as transport vehicles are concerned, suspended particulate matter is generally acknowledged as the most damaging to health in the lower income developing countries. There are WHO recommendations on maximum acceptable concentrations, and a significant number of cities monitor it (mainly PM10 concentration). So, pending data availability,

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the indicator envisaged for this air pollution dimension is the PM10 concentration. This is nevertheless a measure of the city air pollution (not the specific contribution of transport to air pollution).

(d) Urban transport affordability This dimension acknowledges that physical supply of infrastructure is not enough. Efficient and affordable transport underpins personal accessibility and mobility. This issue is of particular importance for low income countries and low income population in urban areas. The Bank has carried out an analysis of the affordability of public transport. This analysis was detailed in the World Bank Transport Papers Affordability of Public Transport in Developing Countries, Carruthers and Mitric, 2005. The emphasis has been on identifying the proportion of income of lower income workers necessary to pay for the journeys to work. The index used was: Affordability Index = Number of trips (60 trips) x Average cost per trip, as a % of per capita income. It is suggested to keep this definition.

(e) Urban transport policy: public transport and non motorized transport share or PT usage

In terms of core urban transport policy, the international good practice on urban transport, the core policies developed by cities around the world, as well as the core urban transport policy of the World Bank, is to increasingly favor public transport and non-motorized transport modes, versus private cars transport. Reasons are that PT and NMT are available to middle and low income population and that they are less damaging to the environment and they mitigate congestion. In this regard, the urban transport modal share can be considered, not only as an output indicator, but also as a strategic indicator. A possible indicator could be “percentage of motorized public transport among all motorized trips”. Alternatively, a measure of “public transport usage” is also a way to reflect the importance of public transport in urban transport policies.

(f) Public transport supply The last aspect that is proposed to be examined for this exercise, and which is generally the first step taken for monitoring, is an indicator to reflect the public transport supply. In a situation of data scarcity, this is generally the first step taken for an indicator. Urban transport supply could be measured by considering the length of public transport routes in the city per capita or the number of buses or seats per capita .

Conclusion of chapter I: Possible urban transport dimensions and indicators for an index

Urban accessibility/mobility: average travel time to go to work (one way)

Urban transport safety: number of fatalities on urban roads per 100,000 population

City GHG emissions or air pollution: GHG emissions per capita or PM10 concentration

Urban transport affordability: cost of 60 one way trips, as a % of per capita income

Urban transport policy: modal share (public transport and non motorized transport versus private motorized vehicles) or public transport usage (PT trips per capita)

Public transport supply: length of PT routes per population or PT seats per population

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CHAPTER II – IN-HOUSE REGIONAL TESTING

Given that the actual availability of reliable and sustainable data is a key issue in such an exercise for

developing countries, especially at the sub-national level, it was decided to do an in-house regional testing of a tentative grid of possible urban transport indicators. The format that could be used to reflect the multiple dimensions of such an indicator was also tested.

Initial tentative grid of urban transport indicators set up for regional testing

Based on the considerations developed in chapter I, the following initial grid of possible indicator(s) was set up for each of the urban transport dimensions identified:

High level indicators available for the identified dimensions Data Year Source / comment

Urban transport accessibility / mobility:

travel time spent by households to go to work on average working day

average distance to a public transport stop from household home

Urban transport safety:

number of people killed on urban roads per year

Urban transport impact on environment : greenhouse gas emissions and air pollution

PM10 concentration microgram per cubic meter

carbon dioxyde emissions emitted per year for the city, in tons

Urban transport affordability:

percentage of household income spent on transport

Urban transport policy: strategic modal share:

percentage of motorized public transport trips

percentage of motorized private trips

percentage of non-motorized trips

Urban transport supply:

overall mileage of bus routes in km

overall number of buses including mini buses

overall number of bus seats available including minibuses

overall mileage of urban roads and streets in km

overall mileage of paved urban roads and streets in km

overall mileage of un-paved urban roads and streets in km

Approach taken for the in-house regional testing

The approach taken for the regional testing was to conduct a light in-house testing by regional teams of the World Bank on the availability of the data for the indicators in the grid, using data that the Bank was aware of at the regional level, for a couple of cities. The exercise included the description of the data, its source and date. The exercise also included the mention of possible alternative indicators whose data would be available and that could be used as alternative proxy measures of the topics selected (urban transport supply, urban transport modal share, urban transport mobility, urban transport accessibility, urban transport affordability, urban transport safety, environment). The test was carried out for three cities, namely Mumbai, Lagos, and Sao Paulo.

Detailed material of the in-house regional testing The data and sources found through the regional teams, for the 3 selected cities are listed in Annex A.

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Compilation of the rough data and sources identified through the in-house regional testing – summary table

High level indicators for the identified topics

Mumbai Lagos Sao Paulo

Data Year Source / comment

Data Year Source / comment Data Year Source / comment

Urban transport accessibility and mobility:

travel time spent by households to go to work (one way) on average working day

31min 2008 MMRDA (CTS) 53min 2009 LAMATA 39min 2007 State of Sao Paulo

average distance to a public transport stop from household home

n/a 800m n/a LAMATA n/a

Urban transport safety:

Road fatalities per year 623 2007 MMRDA reg. stats.

782 2006 Lagos State (FRSC) n/a data for StateArea

Urban transport impact on environment: greenhouse gas emissions and air pollution:

PM10 concentration microgram per cubic meter (not specific transport)

132 2008 Gov India (CPBC) n/a 40 2004 World Dev. Ind.

carbon dioxyde emissions emitted per year for the city, in tons

n/a n/a 15.7 million

2003 SaoPaulo City only

Urban transport affordability:

percentage of household income spent on transport

est. 17.5%

2008 est. MMRDA (CTS)

20% n/a LAMATA n/a

Urban transport policy: modal share

percentage of motorized public transport trips 29% 2008 Calc. MMRDA (CTS)

41% 2008 Calculated LAMATA (minibuses + buses)

37% 2007 State of Sao Paulo (ODstudy)

percentage of motorized private trips 10% 2008 Calc. MMRDA (CTS)

19% 2008 Calculated LAMATA (car + taxis + motorcycle taxis)


percentage of non-motorized trips 61% 2008 Calc. MMRDA (CTS)

40% 2008 Calculated LAMATA (walk)


Urban transport supply:

overall mileage of bus routes in km 41481km 2008 calcul. MMRDA reg. stats

9644km 2006 LAMATA n/a

overall number of buses including mini buses 25732 2008 MMRDA reg. stats Est. 100000

n/a LAMATA (est.)

54500 2007 SaoPaulo State reg.


1544000 2008 calc. MMRDA (CTS)

n/a n/a

Size of the city

Population million inhabitants 17.85 million

2001 National Census 17.55 million

2006 Lagos State census 19.5 million

2007 Sao Paulo State

Size of the city square km2 4354 km2

2007 MMRDA website 3577 km2

Lagos State 7970 km2

2007 Sao Paulo State

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Possible methodology to present the multiple dimensions of an urban mobility index Possible methodology to aggregate and present various dimensions of a composite urban mobility index could be as follows: Using the following indicators and calculating the average figures for the cities, as follow:

The next possible step is to calculate indicative points for each dimension, to have a scale of roughly the same span, say 0 to 10, for each urban transport dimension. An example of calculation could be as below:

- If number of cities is sufficient (which is not the case here), 5 points could represent the average of the indicator (e.g. 41 minutes travel time represents 5 points)

- Directions of points have to be chosen to be consistent between each other: high points would indicate a comfortable situation of the given city compared to the city average and points between 0 and 5 would indicate a possible issue in comparison with the cities average

- Possible indicative formula to define the points are as follows: Example of urban transport accessibility: Accessibility points = minimum of (5 x average travel time / city travel time ; 10) Example of public transport supply: PT supply points = minimum of (5 x city mileage ratio / average mileage ratio ; 10)

Representation (star diagram) of a multi-dimensional urban mobility index with 6 dimensions: accessibility, safety, environment, affordability, public transport usage, public transport supply

rough figures City 1 City 2 City 3 average

travel time to go to work (one way) (minutes) 31 53 39 41

road fatalities per 1 000 000 population 35 45 40

PM10 concentration microgram per cubic meter 132 40 86

percentage of household income spent on transport 20 20

percentage of public transport trips among all motorized trips 74 68 56 66

mileage (km) of bus routes per 100 000 population 232 55 144

possible points City 1 City 2 City 3

urban transport accessibility 6.6 3.9 5.3

urban transport safety 5.7 4.5

air quality 3.3 10.0

urban transport affordability 5.0

public transport usage 5.6 5.2 4.2

public transport supply 8.1 1.9

0.0

2.0

4.0

6.0

8.0

10.0

urban transport accessibility

urban transport safety

city environment (GHG or PM10)

urban transport affordability

public transport usage

public transport supply

City 1

City 2

City 3

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Issues on data consistency, sustainability, availability and reliability

Inconsistency linked to the various administrative boundaries and to the mix of data with different areas

The Rural Access Index and the Logistic Performance Index were both devised for application at the national level, where boundaries of countries are clearly defined. For urban transport, this clarity does not exist. Data have to be found at the sub-national levels. What would be ideal is a set of indicators which represent the conditions for the conurbation as a whole. Unfortunately, data often reflect administrative boundaries and these boundaries usually do not match the conurbation perimeter (municipality areas are very diverse and often limited to a part of the conurbation and regions or states include rural areas in their perimeters). The difficulty is that urban transport conditions differ substantially between city centers, suburbs and rural areas in these administrative perimeters. In addition to this issue, some data are provided for the municipal boundary and some other data are provided for the regional or state level and some data are not specifically linked to an administrative boundary (e.g. PT networks and boardings). When creating a multi-dimensional urban mobility index, because of scarcity of data, there is in practice a mix of data with different perimeters, creating an additional reliability issue of the outcome of such a composite index. In practice one can exercise little control over this situation. A mitigating action is to accept the boundary of the city itself and limit as far as possible the analysis within this boundary. This is the solution adopted by the Global Cities Indicators Facility (see chapter IV), which has the merit of ensuring that the transport indicators which are collected will be compatible spatially, with other data (e.g. population) assembled by the same GCIF. However, in several cases, difference of figures between two cities will reflect a difference linked to boundaries, instead of an actual difference of urban transport performance for the two cities.

Difficulty to account for the major informal mini-bus sector in indicators and systemic lack of data for this major sector

The informal mini-bus sector in a large number of low-income developing cities is by far the major public transport supply. The average bus fleet in the 14 African cities studied in the WB/SSATP 2008 Africa Infrastructure Country Diagnostic is 200 buses per city. For these same cities, the average minibuses fleet is in the range of 14,400 minibuses per city... A major difficulty in establishing public transport indicators is whether and how to account for this sector, and the absence or unreliability of data for this informal sector.

The issue of data availability on a sustained basis There is an annual transport statistic publication for one of the metropolitan areas tested (statistics for the Mumbai Metropolitan area produced by MMRDA) and another annual transport statistic for the state area for a second city tested (statistics for the State of Sao Paulo produced by the State Department for Transportation) and information provided informally for the third city (i.e. Lagos). The two annual publications provide regular annual data for 2 of the 3 cities on regular organized public transport fleets, fare, ridership and on road fatalities, but at different geographical perimeters. For example, data provided by MMRDA are: regular publicly owned bus fleets; regular publicly owned bus fare; annual regular publicly owned bus ridership; annual fatalities on roads; annual suburban rail passenger traffic; suburban rail fare; annual fatalities on suburban rail.

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CHAPTER III - AVAILABILITY OF CITY TRANSPORT DATA AT INTERNATIONAL LEVEL Despite the importance of cities and urban agglomerations as home to almost half the world‟s population, data on many aspects of urban life are not readily available. Object of this chapter is to review what data are actually available from international sources, on urban transport dimensions identified in chapter I.

World Bank

The main regular World Bank data publication is the annual World Development Indicators report.

Another regular source is the International Comparison Program on price levels. Status of city transport

data in these publications:

Accessibility/mobility: Travel time to go to work, data as of 1998, from World Development

Indicators 2005 (chapter 3.11). More recent WDI reports don‟t include this.

Urban transport modal share: Percentage of work trips by public transportation, data as of 1998, from World Development Indicators 2005 (chapter 3.11). More recent WDI reports don‟t include this data.

Public transport affordability: The International Comparison Program (ICP) uses for the

calculation of global comparable price levels, some data on urban public transport fares. The data

cannot be published (conventions with National Statistical Offices), but it can be used in an index.

Last ICP was in 2005, the next ICP is expected in 2011.

City air pollution (not specific to transport): urban population weighted PM10 concentration, data

as of 2006, from the World Development Indicators 2009 (chapter 3.14 p. 186-187).

City greenhouse gas emissions (not specific transport): CO2 emissions per capita, from various

sources ranging 2007/1997, on the World Bank website

(http://web.worldbank.org/WBSITE/EXTERNAL/TOPICS/EXTURBANDEVELOPMENT/EXTUWM/

0,,contentMDK:22450716~menuPK:6721936~pagePK:210058~piPK:210062~theSitePK:341511

~isCURL:Y,00.html)

UN Habitat

The main regular data publications are States of the World Cities and Global Report on Human

Settlements. Status of city transport data in these publications:

Accessibility/mobility: Travel time per work trip, data as of 1998, from Global Report on Human Settlements 2007

Modal share: Modal share in selected cities, data derived from various sources ranging 1995/2002, from States of the World Cities 2008/2009; Transport use for work trip (modal share), data as of 1998, from Global Report on Human Settlements 2007.

Urban transport supply: Car ownership in selected cities, data derived from various sources ranging 2000/04, from State of the World Cities 2008/09

International Association of Public Transport (UITP)

UITP has compiled a database of 100 cities, known as the “Millenium Cities Database for Sustainable

Transport”, which has been undertaken in collaboration with Dr. Jeff Kenworthy and Felix Laube of

Murdoch University, Australia. Status of city transport data in this database:

The database covers all urban transport dimensions: Over 200 indicators have been collected (road vehicles, taxis, road network, parking, public transport networks, usage and cost, individual mobility and choice of transport mode, transport system efficiency and environmental impact). Unfortunately data are as of 1995. This is the urban transport database that has been used extensively worldwide.

UITP has issued more recently (issued in 2006, data as of 2001) the “Mobility in Cities Database”. But

this database only includes 4 cities outside OECD countries (Hong Kong, Sao Paulo, Singapore, Tunis).

http://web.worldbank.org/WBSITE/EXTERNAL/TOPICS/EXTURBANDEVELOPMENT/EXTUWM/0,,contentMDK:22450716~menuPK:6721936~pagePK:210058~piPK:210062~theSitePK:341511~isCURL:Y,00.html



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Global City Indicators Facility (GCIF)

See chapter IV on GCIF

IHS Jane’s

IHS Jane‟s (private company) publishes regularly the Jane‟s Urban Transport Systems publication (last

issue 2010/2011; $935), which provides information on the world‟s major public transport systems and

operators, including information on current operations and future plans. The data is protected by a

copyright. Main urban transport data are on:

Public transport usage: operators‟ passenger journeys and vehicle-km,

Public transport supply: operators‟ number of routes, route length, and fleet

Summary Table

Availability of city

data at international level

World Bank UN Habitat UITP GCIF Jane’s

Urban Accessibility / Mobility



Data as of 1995

Not Available

Not Available

Urban Transport Safety

Not Available

Not Available

Data as of 1995

Fatalities Not yet

Available Not Available

City air pollution or City GHG emissions

AQ 2006 GHG

2007/97

Not Available

Not Available

AQ and GHG Not

yet Available Not Available

Public Transport Affordability

PT fares 2005 (not published)

Not Available

Data as of 1995

Not Available

Not Available

Urban transport modal Share or public transport

usage

Not Available

Modal share 1995-

2002

Data as of 1995

PT trips Not yet Available

PT operators journeys and vehicle-km

2004/08 (copyright issue)

Urban Transport Supply

Not Available

Car ownership 2000/04

Data as of 1995

PT length Not yet

Available

PT operators route length and

fleet 2004/08

(copyright issue)

Conclusions of chapter III

It is seen that for many of the urban transport indicators there is no recent regrouped comparable and quality assured data available at international level. Detailed situation is:

Travel time to go to work, data as of 1998 (WDI 2005 and UN Habitat)

City air pollution, data as of 2006, on PM10 concentration (WDI 2009)

City greenhouse gas emissions per capita, mixed data ranging 2007/1997 (WB website)

Public transport operators journeys, vehicle-km, route lengths and fleets, data as of 2004/08, with copyright issue (Jane‟s 2010/11)

Modal share, mix of data ranging 1995/2002 (UN Habitat States of the World Cities 2008/09)

Huge database of 200 indicators, but with data as of 1995 (UITP Millennium City Database 2001 J Kenworthy)

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CHAPTER IV - THE WORLD BANK INITIATIVE ABOUT URBAN DATA: THE GLOBAL CITY INDICATORS FACILITY

Presentation of the Global City Indicators Facility (GCIF) The World Bank, along with UN-Habitat, the World Economic Forum, OECD, the Government of Canada, and ICLEI, recognized the need for a single comprehensive system for measuring and monitoring city service delivery and urban quality of life that would enable elected officials, city managers, and the public to monitor the performance of cities effectively over time, facilitate cross- city comparisons, and provide enhanced government accountability demanded by policy makers and the public. It has also been recognized that the existing city data globally is not standardized, consistent, nor comparable over time or across cities and that this lack of standardization limits the ability of cities to share best practices and learn from each other. The Urban Development Team of the World Bank initiated the Global City Indicators Program, through funding from the Government of Japan, to develop a set of indicators to be collected and utilized by cities that would be representative and rigorous enough to enable third-party verification. With this objective in mind, the program was announced as a pilot initiative at the World Urban Forum in Vancouver in 2006, with support from several donors and development partners, such as UN-Habitat, World Economic Forum, OECD, the Government of Canada, and ICLEI. The Global City Indicators Program is a decentralized, city-led initiative that enables cities to measure, report, and improve their performance and quality of life indicators, facilitate capacity building, and share best practices through an easy-to-use web portal. The World Bank proposed to build on the existing indicators and facilitated the development of consistent and comparative city indicators to help cities monitor service delivery performance and quality of life effectively. The indicators are designed in a manner to enable easy and inexpensive annual data collection. Each participating city is responsible for collecting and updating the indicators for its city. An ISO standard for city indicators is presently under development to facilitate comparability and verification across cities and over time. This program is now run since 2008 by the Global City Indicators Facility based at the University of Toronto, which manages the development of indicators and assists cities in joining the Program. As of June 2010, 106 cities were partners of GCIF. The exhaustive list of current GCIF indicators is provided in Annex B.

The current GCIF urban transport indicators The indicators concerning urban transport are:

Km of high capacity public transit system per 100,000 population

Km of light passenger transit system per 100,000 population

Number of personal automobiles per capita

Annual number of public transit trips per capita

Number of two-wheel motorized vehicles per capita

Commercial Air Connectivity (number of nonstop commercial air destinations)

Transportation fatalities per 100,000 population The indicators pertaining to environmental impact are:

PM10 concentration

Greenhouse gas emissions These GCIF indicators cover 4 of the 6 dimensions envisaged for the possible urban mobility index (PT supply, PT usage, urban transport safety, city environment GHG emissions or PM10) Data are not yet available, but a set of data have been provided by some city partners, and are currently being reviewed by the GCIF.

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CHAPTER V: RECOMMENDATIONS

First recommended next step It has been seen that for many of the urban transport indicators, there is no recent regrouped comparable and quality assured data available at international level. This is an issue, as developing an urban accessibility / mobility index could only be fruitful if data is available on a regular basis to enable comparison across cities and over time. Any urban accessibility / mobility index should be underpinned by a regular data collection process. The Global City Indicators Facility, launched by the World Bank with other partners, does not currently provide urban transport data, but is the only institution that offers, on the medium/long terms, the perspective of a regular data collection process, with standardized indicators and some quality control of the data. It is based on a partnership of currently 106 cities (as of June 2010), which have signed an agreement with GCIF to provide their data, and benefit from the data provided by other partner cities. It is therefore recommended to co-operate with GCIF to develop an urban accessibility / mobility index, that would have the form of star diagrams, to steadily reflect key urban transport dimensions identified in this report, using current and later future GCIF indicators. Detailed joint work would include:

- possible strengthening of some current GCIF urban transport indicators; - addition of a few GCIF urban transport indicators to cover dimensions not yet covered; - discussion with GCIF on the reliability of the data collected; - production and testing of a first set of a few star diagrams to reflect urban transport dimensions

covered by existing GCIF indicators, to be later expanded to include additional GCIF indicators, to ultimately cover in the long term the urban transport dimensions identified in this report.

This recommended approach is therefore very practical, and would consist in a very and to some extend too rustic urban accessibility / mobility index in a first step, but has the potential to steadily evolve towards a robust multi-dimensional urban accessibility / mobility index, with data provided on a sustained regular basis, by an increased number of cities, through the GCIF platform. Second recommended next step The absence of data at international level does not prevent a work on urban transport project indicators, consistent with a future urban mobility index. Given the increasing importance of urban transport, the World Bank has decided this year to create a specific coding for the urban transport sub-sector that will facilitate a better monitoring of the urban transport portfolio. On such sectors, the policy of the Bank is to develop, where feasible and simple enough, a set of a few core indicators, to facilitate the reporting of aggregated results for the portfolio. It is therefore recommended to explore the feasibility of possible core indicators to facilitate the reporting of the WB urban transport portfolio results. Detailed work would include:

- listing performance indicators used for WB urban transport projects and identify occurrences; - identify performance indicators used by other international development banks for UT projects; - establish possible options for a few urban transport indicators, consistent with the work presented

in this report on an urban mobility index, and consistent with other core indicators already chosen by the World Bank for other sectors, with proposed definition and estimation method;

- this work would include advice on how to estimate carbon footprint of urban transport projects.

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REFERENCES Jeffrey Kenworthy Urban Transport Sustainability 2008: Transport sustainability ranking of 84 cities across 11 regions based on a comprehensive assessment of their transport system performance (data drawn from the Millennium Cities Database 2001) World Bank / AusAID / ESMAP 2009 Working Paper Urban transport and CO2 emissions: some evidence from Chinese cities Global City Indicators Facility (GCIF), www.cityindicators.org UN Habitat States of the World Cities, 2010-2011 SSATP. 2006. Report on the Second Transport Data Collection Cycle. SSATP Transport Indicator initiative International Association of Public Transport (UITP). 2006. Mobility in cities database. Issued in 2006, data as of 2001 International Association of Public Transport (UITP) 2001, Millennium Cities Database for Sustainable Transport. Issued in 2001 – data as of 1995-1998 UN-Habitat. 2004. Urban Indicators Guidelines. Monitoring the Habitat Agenda and the Millennium Development Goals Roberts, P., Shyam, KC, Rastogi, C. 2006. Rural Access Index: A Key Development Indicator. World Bank, Transport Paper #10 JANE‟S Urban Transport Systems 2007-2008 UN Habitat Global Report on Human Settlements, 2007, Statistical annexes World Bank Transport Business Strategy for 2008-2012, Safe, Clean and Affordable Transport for Development, 2008 World Bank Transport Papers Affordability of Public Transport in Developing Countries, Robin Carruthers, Malise Dick and Anuja Saurka, TP-3, 2005 World Bank Transport Papers, A Framework for Urban Transport Projects, Operational Guidance for World Bank Staff, TP-15, 2008 World Bank Transport Papers, Urban Transport for Development, Towards and Operationally-Oriented Stategy, Slobodan metric, TP-22, 2008 World Bank, Perinaz Bhada and Dan Hoornweg, The global city indicators program: A more credible voice for cities, 2009 World Bank and SSATP / Africa Infrastructure Country Diagnostic, Stuck in Traffic: Urban Transport in Africa, Ajay Kumar and Fanny Barrett, January 2008 World Bank. 2002. Cities on the move. A World bank Urban Strategy Review. World Development Indicators Report 2009 and annual series

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ANNEX A – DETAILED MATERIAL OF THE REGIONAL TESTING Are listed below the data and sources found by the regional teams, for the 3 selected cities Mumbai, Lagos and Sao Paulo Mumbai Source of data The key source of data used in this exercise by the South Asia Team of the Bank, is the Mumbai Metropolitan Region Development Authority (MMRDA). It produces an annual urban transport data report (“Basic Transport and Communications Statistics for Mumbai Metropolitan Region”), which is therefore a sustainable source of the main urban transport data. MMRDA has also commissioned and owns some more detailed urban transport analysis, but only produced at specific times (in particular the “Comprehensive Transportation Study for Mumbai Metropolitan Region, April 2008). Other sources used include Government of India ministries, Government of Maharashtra, Municipal Corporation of Greater Mumbai. Details of the indicators, data and sources are:

Urban Transport Indicators – Mumbai Metropolitan Region – Regional testing

High level indicators available for the identified topics Data Year

Sources & comments

Urban transport supply

overall number of registered vehicles in MMR (million) (private vehicles) 3.54 2008

MMRDA Basic Transport & Comm. Statistics (online)

overall number of vehicles per 1000 inhabitants in MMR (private vehicles) 198 2008 Calculated from above

number of registered private cars (million) 0.93 2008 MMRDA Basic Transport & Comm. Statistics (online)

number of registered two/three-wheelers (million) (private vehicles) 2.15 2008

MMRDA Basic Transport & Comm. Statistics (online)

overall mileage of bus routes, operated by BEST only, in km 5,716 2008 MMRDA Basic Transport & Comm. Statistics

overall mileage of bus routes, operated by MSRTC only, in km 35,277 2008 MMRDA Basic Transport & Comm. Statistics

overall mileage of bus routes, operated by TMT only, in km 488 2008 MMRDA Basic Transport & Comm. Statistics

overall mileage of bus routes for BEST, MSRTC, TMT, in km 41,481 2008 Calculated from above

overall number of buses BEST, MSRTC, TMT, NMMT, MBMT, KDMT operations 4,661 2008

MMRDA Basic Transport & Comm. Statistics

overall number of buses in MMR (#) 25,732 2008 MMRDA Basic Transport & Comm. Statistics

overall number of bus seats available 1,544,000 2008 Calculated from CTS average capacity per bus

overall mileage of urban roads and streets in km 7,660 2008 MMRDA Basic Transport & Comm. Statistics

overall mileage of paved urban roads and streets in km 7,471 2008 MMRDA Basic Transport & Comm. Statistics

overall mileage of un-paved urban roads and streets in km 189 2008 MMRDA Basic Transport & Comm. Statistics

length of suburban railway network (km) 404 2008 CTS (sum of Central Railway + Western Railway)

overall number of suburban train cars in CR and WR fleet (rail) 1803 2008 CTS

Traffic Volumes and Modal share

total number of trips per day all transport modes (including walking, million) 34.3 2005 CTS

per capita trip rate with walk 1.65 2005 CTS

per capita trip rate without walk 0.65 2005 CTS

total number of trips per day public transport (million) 11.89 2005 CTS (sum of bus+suburban rail)

total number of passenger.kms on suburban rail per day (million) 172.9 2006 CTS

percentage of walk passenger trips 60% 2008 CTS surveys - % trips

percentage of public transport (bus+train) passenger trips, no walk 73% 2008 CTS surveys - % trips, no walk

percentage of motorized (car+2W) private passenger trips, no walk 14% 2008 CTS surveys - % trips, no walk

percentage of taxis/shared taxis passenger trips, no walk 9% 2008 CTS surveys - % trips, no walk

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percentage of non-motorized (cycle) passenger trips, no walk 3% 2008 CTS surveys - % trips, no walk

percentage of public transport (bus+train) passenger trips, with walk 29% 2008

Calculated from above CTS surveys - % trips with walk

percentage of motorized (car+2W) private passenger trips, with walk 6% 2008

Calculated from above CTS surveys - % trips with walk

percentage of taxis/shared taxis passenger trips, with walk 4% 2008 Calculated from above CTS surveys - % trips with walk

percentage of non-motorized (cycle) passenger trips, with walk 1% 2008 Calculated from above CTS surveys - % trips with walk

percentage of walk passenger.km 7% 2008 CTS surveys

percentage of public transport (bus+train) passenger.km, no walk 89% 2008

CTS surveys - % of pass.kms, no walk

percentage of motorized (car+2W) private passenger.km, no walk 8% 2008

CTS surveys - % of pass.kms, no walk

percentage of taxis/shared taxis passenger.km, no walk 3% 2008 CTS surveys - % of pass.kms, no walk

percentage of non-motorized (cycle) passenger.km, no walk 1% 2008 CTS surveys - % of pass.kms, no walk

percentage of public transport (bus+train) passenger.km, with walk 83% 2008

Calculated from above CTS surveys - % of pass.kms with walk

percentage of motorized (car+2W) private passenger.km, with walk 7% 2008


percentage of taxis/shared taxis passenger.km, with walk 3% 2008


percentage of non-motorized (cycle) passenger.km, with walk 1% 2008


Urban transport mobility

travel time spent by households to go to work (one way) on average working day, mins 31 2008 CTS

average trip length on suburban rail, in km 27 2008 CTS

average trip length in vehicle, in km 6 2008 CTS

Accessibility of public transport

average distance to a bus stop from household home, in minutes 6.5 2005 Calculated from distribution in WB WP

average distance to a railway station from household home, in minutes 17.5 2005

Calculated from distribution in WB WP

Affordability of urban transport

average monthly household income, in Rs. 8,100 2008 CTS

average monthly travel expenditure per capita, in Rs. 600 2008 CTS

percentage of household income spent on transport 7.4% 2008 Calculated from above

Safety of urban transport

Road safety:

number of road accidents urban roads per year 29,967 2007 MMRDA Basic Transport & Comm. Statistics

number of people killed on urban roads per year 623 2007 MMRDA Basic Transport & Comm. Statistics

number of deaths in road crashes per 10,000 vehicles per year in Greater Mumbai 3.82 2007 Calculated from above

number of deaths in road crashes per 100,000 inhabitants per year in Greater Mumbai 3.49 2007 Calculated from above

percentage of pedestrians among fatalities/casualties of road accidents 49% 2004 Indiastat.com

Rail safety:

number of injured people in suburban rail per year 4,030 2008 Mumbai Railway Police Commissionerate

number of people killed in suburban rail per year 3,782 2008 Mumbai Railway Police Commissionerate

road casualties per million passenger km 0.112 2008 Calculated from CTS Passenger km Estimates

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suburban rail casualties per million passenger km 0.069 2008 Calculated from CTS Passenger km Estimates

Air pollution and greenhouse gas emissions

PM10 concentration microgram per cubic meter 132 2008 CPCB

SO2 concentration microgram per cubic meter 9 2008 CPCB

NO2 concentration microgram per cubic meter 42 2008 CPCB

carbon dioxyde emissions emitted by transport sector per year per capita, in tons 0.11 2008 EMBARQ

carbon dioxyde emissions emitted by transport sector per year for the city, in million tons 1.96 2008 Calculated from above

Urban transport efficiency

congestion as mileage of traffic jams at peak hours NOT

AVAILABLE

congestion as speed for car journey to work during peak hours NOT

AVAILABLE

congestion as average vehicular speed during peak hours, in kmph (##) 16 2008 Wilbur Smith

overcrowding in suburban trains, people density per square meter at peak hour 16 2008 CTS

average suburban train frequency at peak hour, trains per hour 16 2008 CTS

average suburban rail travel speed during peak hours, in kmph 35 2008 CTS

Size of the city

Population million inhabitants 17.85 2001 National Population Census, in CTS

Size of the city square kms 4,354 2007 MMRDA website

Details and sources: # : buses are defined by the Maharashtra Motor Vehicle Department as stage carriages, contract carriages, school buses or private

service vehicles

## : includes both public transport and private vehicles as busways are not segregated CPCB - Central Pollution Control Board, Ministry of Environment and Forests, Government of India. Annual National Ambient Air Quality Status Report. 2008. ANNUAL Source CTS - Mumbai Metropolitan Region Development Authority. Comprehensive Transportation Study for Mumbai Metropolitan Region. April 2008. LEA Associates

MMRDA Basic Transport & Communications Statistics for Mumbai Metropolitan Region, August 2008. ANNUAL Source

Mumbai City Development Plan 2005-2025, Municipal Corporation of Greater Mumbai, 2005

WB WP - Cropper et al., Urban Poverty and Transport: The Case of Mumbai, World Bank Working Paper 2005 TRW - Transport Research Wing, Ministry of Road Transport & Highways, Government of India, Road Transport Yearbook 2006-07. ANNUAL Source

Mumbai Railway Police Commissionerate, Government of Maharashtra

EMBARQ - Transport in Cities, India Indicators study, 2007, World Research Institute Wilbur Smith - Study on Traffic and Transportation Policies and Strategies in Urban Areas in India, 2008, Wilbur Smith Associates and Ministry of Urban Development, Government of India

MMRDA - Mumbai Metropolitan Region Development Authority website

MMRDA - PRK Murthy presentation at Workshop on Unified Metropolitan Transit Authority, Bangalore, 17th June 2009 Lagos Source of information The key source of data used in this exercise by the Africa Team of the Bank, is the Lagos Metropolitan Transport Authority (LAMATA) (both internal and website information). The other source used is the Lagos State. Details of the indicators, data and sources are:

Urban Transport Indicators - City of Lagos - Regional testing

High level indicators available for the identified topics Data Year

Sources & comments


mileage of bus routes in km 9644 km 2006 Lagos TDM(LAMATA)

number of buses and minibuses 100,000 LAMATA

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bus capacity: number of mini bus and bus seats per 1000 population 100 seats LAMATA

average minibus and bus age 10 - 15years WB Africa UT report (LAMATA?)

mileage of urban roads in km 8136 km 2009 LAMATA; does not include Ikorodu town, Badagry, Epe

mileage of paved urban roads network in km 7000 km 2009 WB Africa UT report City Authority

mileage of un-paved urban roads and streets in km 1136 km 2009

paved road density: mileage of paved urban roads for 1000 population 400 m

LAMATA WB Africa UT report (City authorities UN Milenium Cities)

paved road density: mileage of paved road per km2 area 1.7 km

WB Africa UT report City Authority

number of private cars per 1000 population 90 LAMATA -STMP Technical report

Modal share and demand

number of passenger trips per day (including walking) 17.5 Million 2008 LAMATA

number of motorized passenger trips per day 10.5 Million 2008 LAMATA

Percentage of walking among all trips 40%

LAMATA

Percentage of motorized trips among all trips 60%

LAMATA

percentage of mini buses (danfo, coaster, moule) trips among motorised trips 66% 2008 LAMATA

Percentage of buses trips (large buses BRT) among motorized trips 3% 2008 LAMATA

percentage of private cars trips among motorized trips 19% 2008 LAMATA

percentage of taxis (by cars) trips among motorized trips 3% 2008 LAMATA

percentage of motorcycle taxis (Okada) trips among motorized trips 9% 2008 LAMATA

Percentage of private motorcycle insignificant

LAMATA

percentage of rail trips among motorized trips 0% LAMATA

[Caculated overall modal shares:]

percentage of motorized public transport trips (minibuses, buses, rail)

2008 LAMATA ; Excluding Rail

percentage of motorized private trips (cars and motorcycles)

2008 LAMATA ; Private Motorcyle ownership is insignificant

percentage of non-motorized trips

2008 LAMATA ; Excluding bicycle trips


travel time spent by households to go to work on average working day 53min 2009 LAMATA


average distance to a public transport stop from household home 800Metres LAMATA


expenditure on transportation in percentage of the household budget 20% LAMATA

average bus fare per trip 201 Naira 2009 LAMATA

percentage of first quintile household budget needed to pay for 60 one-way trips per month 40% 2008 LAMATA


number of people killed on urban roads per year 782 2006 Lagos State (FRSC)


PM10 concentration microgram per cubic meter 433 2008 LAMATA

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carbon dioxyde emissions emitted per year for the city, in tons 1648 2008 LAMATA


congestion as mileage of traffic jams at peak hours

congestion as speed for car journey to work during peak hours 23.1km/hr 2008 LAMATA

Size of the city

City population - in million 17.55 2006 Lagos State census

City area (Metropolitan Area) 3577 km Lagos State

Sources:

LAMATA: Lagos Metropolitan Transport Authority - http://www.lamata-ng.com + internal information provided by LAMATA

PAD LUTP2 : WB Project Appraisal Document Lagos Urban Transport Project 2 - August 24, 2009 - P112956

WB Africa UT report: World Bank Urban Transport in Africa : Stuck in Traffic – 2008 Lagos State Government (http://www.lagosstate.gov.ng) Lagos State – Federal Command of the Road Safety Commission (FRSC)

Sao Paulo Source of information The key source of data used in this exercise by the Latin America Team of the Bank is the State of Sao Paulo (department in charge of transport: Sao Paulo Secret. dos Transportes). Other sources used include: Municipality of Sao Paulo, National association of public transport (ANTP), and Sao Paulo City bus company (SPTrans). The data are provided for the São Paulo Metropolitan Region (SPMR) (i.e. 39 municipalities including Municipality of São Paulo (MSP)), unless otherwise noted below. Details of the indicators, data and sources are:

Urban Transport Indicators – Sao Paulo Metropolitan Area – Regional testing

High level indicators available for the identified topics

Data Year Source / comment


number of private automobiles 5,840,000

2007 State of Sao Paulo (Department for Transport): SP State Secret. dos Transportes Metropolitanos report Issued regularly

overall mileage of bus routes in km 4,300 2007 State of Sao Paulo (Department for Transport): Article : http://www.urban-age.net/0_downloads/archive/_SA/13_NewsPaper_Essay_Biderman.pdf

overall number of buses 17,000 2007 State of Sao Paulo (Department for Transport): SP State Secret. dos Transportes Metropolitanos report Issued regularly

overall number of buses including mini buses 54,500 2007 State of Sao Paulo (Department for Transport): SP State Secret. dos Transportes Metropolitanos report Issued regularly


N/A (est.

817,500)

2007 Not available Estimate using State of Sao Paulo (department for transport) and based on non available average seated capacity chosen as of 15 per vehicle

overall number of bus seats+standing available including minibuses

N/A (est.

2,180,000)

2007 Not available Estimate using State of Sao Paulo (department for transport) and based on non available average total capacity chosen as of 40 per vehicle

overall urban roads and streets in km n/a Not available

http://www.lamata-ng.com/

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overall paved urban roads and streets in km 24,000 2007 for MSP only, CET Management Report 2005-2007

overall un-paved urban roads and streets in km n/a Not available But only a few unpaved roads

Modal share

percentage of motorized public transport trips 37% 2007 State of Sao Paulo (Department for Transport): 2007 OD Study for SPMR Study produced about every ten years

percentage of motorized private trips 29% 2007 State of Sao Paulo (Department for Transport): 2008 OD Study for SPMR Study produced about every ten years

percentage of non-motorized trips 34% 2007 State of Sao Paulo (Department for Transport): 2009 OD Study for SPMR Study produced about every ten years


travel time spent by households to go to work on average working day

39 minutes 2007 State of Sao Paulo (Department for Transport): one way travel time, 2007 OD Study Study produced about every ten years


average distance (km) to a public transport stop from household home

0.3 2001 Anuário ANTP 2001 National source: Association of public transport A one time estimate (not regularly produced)


percentage of household income spent on transport 4% 2006 Article http://www.urbanage.net/10_cities/08_saoPaulo/_essays/SA_Gomide.html; If employer-based subsidy (vale-transporte) is removed, value increases to about 10% A one time estimate (not regularly produced) Detail of the figure: cost is in fact 10%, with but with companies paying 6% and the actual charge of the users on 4% on average


number of people killed on urban roads per year n/a (estimate

2730)

2006 Official source is State of Sao Paul (department for transport) Estimate for SPMR based fatalities/inhabitant from DENATRANS and reported in http://www.urban-age.net/10_cities/_data/_MC_SP/MC_SPO.html


PM10 concentration microgram per cubic meter 40 2004 World Bank - World Development Indicators, 2008 (pg. 182)

carbon dioxide emissions (equivalent) emitted per year for Municipality of Sao Paulo (MSP), in tons

15,700,000 2003 São Paulo City GHG Emissions Inventory, 2005 Area covered is Sao Paulo City

carbon dioxide emissions (equivalent) emitted per year for Sao Paulo Metropolitan Region (SPMR) in tons

n/a (est.

28,000,000)

2003 Not available for the Sao Paulo Metropolitan Region Estimate extrapolating GHG per inhabitant for the rest of region, using the data of the Sao Paulo City A law is expected to make it compulsory to collect this data in the future


congestion as mileage of traffic jams at peak hours 260 2009 Only for the Sao Paulo City Source Municipality of Sao Paulo (traffic agency) record peak km of congestion out of a total monitored network of about 825 km

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congestion as speed for car journey to work during peak hours

19 kph 2006 Only for Sao Paulo City Source Municipality of Sao Paulo (Traffic agency) CET study for major roads in MSP

congestion as speed for bus journey to work during peak hours

15 kph 2006 Only for Sao Paulo City Source Municipality of Sao Paulo (Traffic agency) CET study for major roads in MSP

Index of passenger boarding per bus-km 1.84 2007 Only for the Sao Paulo City Source City bus company: SPTrans for MSP; http://portal1.antp.net/site/simob/Lists/MrsCdds/Mcidades.aspx

Size of the metropolitan area

Population million inhabitants 19,535,000

2007 Source State of Sao Paulo: 2007 OD Study

Size of the Sao Paulo Metropolitan area in square kms

7,970 2007 Source State of Sao Paulo 2007 OD Study

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ANNEX B – LIST OF THE CURRENT GCIF INDICATORS

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ANNEX C – TRANSPORT SUSTAINABILITY RANKING OF 84 INTERNATIONAL CITIES

BASED ON TRANSPORT SYSTEM PERFORMANCE, BY J KENWORTHY, 2008

Extract of the document presented by Jeffrey Kenworthy at the 9th World Congress of Metropolis, Sydney

2008

(http://www.metropolis.org/metropolis/sites/default/files/reuniones/sydney_2008/publicaciones/CityRegion

s_4.pdf)

Transport sustainability ranking of 84 cities across 11 regions based on a comprehensive assessment of their transport system performance With a spectrum of factors in mind, the study assesses the transport systems of 84 cities from all over the world and ranks them on this basis. Data to conduct this study are drawn from the Millennium Cities Database for Sustainable Transport compiled by Kenworthy and Laube for the UITP in Brussels, which provides data for 100 cities. The corresponding data for 84 of these 100 cities have been used to rank cities in the US, Australia, New Zealand, Canada, Western Europe, Asia (both high income and low income areas), Eastern Europe, the Middle East, Latin America, Africa, and China. Criteria used for the ranking are shown in figure 2 below. The ranks (index) of the 84 cities are presented in figure 4 below. The conclusions of this study by J Kenworthy:

US cities dominate the poorest transport sustainability scores, along with Canadian and Australian cities. Riyadh also ranks poorly, which is logical as it has, in modern times, developed on the us city model. Between the least sustainable and most sustainable systems there are still a number of cities that have relatively poor sustainability, including Montreal, Toronto and New York in North America, as well as Wellington in New Zealand and Sydney, Australia. These cities are clearly the best cities within their respective countries in terms of the sustainability of urban transport, but globally they are relatively low ranking. Many of the cities from Western Europe also fall in this group and it includes three French cities (Nantes, Lyon and Marseille), Bologna, which is the worst Western European city in this analysis, as well as Athens, The Ruhr and Geneva, the most auto– oriented Swiss city. The remainder of the cities are in the Asian region consisting of Kuala Lumpur, Bangkok and Ho Chi Minh City. hcm City is a motorcycle–dominated urban environment with almost no public transport system. High–income Asian cities and a collection of Western and European cities have the most sustainable transport systems. The final or „top‟ cluster for transport sustainability commences with the exceptional public transport cities of Zurich and Berne, and also includes Vienna, Munich, Berlin and London. Beijing and Shanghai also feature in this group, along with Prague and Cracow. Much less wealthy cities also appear in this group because of their very low dependence on cars (Cairo, Mumbai, Chennai and Dakar). Finally, Osaka, Tokyo and Hong Kong are considered to have some of the most sustainable transport systems in this sample of world cities.

http://www.metropolis.org/metropolis/sites/default/files/reuniones/sydney_2008/publicaciones/CityRegions_4.pdf

http://www.metropolis.org/metropolis/sites/default/files/reuniones/sydney_2008/publicaciones/CityRegions_4.pdf

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Documents

URBAN ACCESSIBILITY / MOBILITY INDEX …...2 CONTENTS Executive summary p 3 Chapter I – Objectives, possible dimensions and indicators for an urban accessibility/mobility index p