Journal of the Eastern Asia Society for Transportation Studies, Vol.10, 2013

Urban Transportation Characteristics and Urban Mass Transit Introduction in the Cities of Developing Countries

Yukihiro KOIZUMIa, Noriaki NISHIMIYAb, Motoko KANEKOc a, Economic Infrastructure Development Department, International Cooperation Agency(JICA), Nibancho Center Building 5-25,Niban-cho, Chiyoda-ku, , 102-8012, Japan [email protected] b JICA Chugoku International Center, 3-1, Kagamiyama 3-chome, Higashi City, Hiroshima, 739-0046, Japan [email protected] c ALMEC Corporation, Kensei Shinjuku Building, 5-5-3, Shinjuku, Shinjuku-ku, Tokyo,160-0022, Japan [email protected]

Abstract: JICA has been conducting urban transportation master plan studies in more than 50 cities in developing countries, which established comprehensive traffic database including person trip, traffic volume, and users’ stated preference. This paper analyzes the relationship between urban socio-economic indicators and traffic patterns particularly in the cities of developing countries and identifies their urban transportation characteristics based on the above traffic database. Special attention is paid on the “two-wheeler cities” in Asia which have different trend from global tendencies. The criteria to introduce urban mass transit system are also identified in conjunction with level of socio-economic development of cities.

Keywords: Urban Density, Socio-economic Development Phase, Ownership, Modal Share, Urban Mass Transit, Developing Countries

1. INTRODUCTION

Rapid urbanization have posed several problems on developing countries, especially in the transportation sector, which include , inconvenient mobility, deteriorated traffic safety, serious air pollution and social injustice/inequity. In order to tackle such problems, a comprehensive urban transport strategy is essential, which comprises a set of long-term policy objectives and policy measures in both physical infrastructure and institutional improvement. The strategy should also include a description of how to implement and manage the proposals, based on which necessary actions should be taken on a timely basis. There are two important factors to formulate an urban transport strategy. Socio-economic development phase of the target city is one of the key factors to be considered since introducing mass transit system especially MRT (Mass ) takes huge cost and long period from planning to inauguration. Traffic pattern of a city such as car ownership and modal share should also be well analyzed and reflected to the strategy. There were some previous studies such as Newman et al. (1999), Suzuki et al. (2007), etc discussing relationships among socio-economic indicators, car ownership and traffic patterns. However, there are few studies that focus on the cities of the worldwide developing countries due mainly to the limit of data collection.

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Japan International Cooperation Agency (JICA) conducted a research study, the Research on Practical Approach for Urban Transport Planning (JICA, 2011). It undertakes (i) the collection and typological analysis of basic data on current urban situations and traffic patterns of urban agglomerations or cities in the world and (ii) the examination of the development needs in urban transport infrastructure and the related institutional capacity building vis-à-vis phases of socio-economic development and proposals on urban transport strategy formulation. This paper (hereinafter called “the Study”) introduces two practical implications extracted from JICA (2011) for an urban transport strategy formulation in the cities of developing countries. Firstly the Study analyzes the relationships between the urban socio-economic indicators and urban traffic patterns. These indicators are urban density, GDP (gross domestic product) per capita, the modal share of , and the passenger car ownership based on the comprehensive traffic database in more than 50 cities of the worldwide developing countries. Secondly the Study quantitatively examines the criteria to introduce urban mass transit system in conjunction with level of socio-economic development of cities, based on which an appropriate timing for introducing of urban mass transit is identified. Following the research framework in Chapter 2, Chapter 3 describes the relationships between the urban socio-economic indicators and the urban traffic patterns, where two wheeler cities are analyzed separately. Timing of introduction of mass transit system in association with phases of socio-economic development is provided in Chapter 4. Finally Chapter 5 describes conclusion and issues for future considerations.

2. RESEARCH FRAMEWORK

2.1 Research Method

In Chapter 3, two steps are taken to find correlative relationships among the four indicators in the cities of worldwide developing countries: namely urban density, GDP per capita, passenger car ownership, and modal share of public transport. First, target cities are grouped in terms of population size, GDP per capita, or regional distribution and plotted by above four indicators. Then the relationships are examined in order to identify their urban transportation characteristics. Either linear or non-linear regression analysis, whichever has higher coefficient of determination (R2), is applied on all of the plotted data. The same regression analysis is applied on the each grouped data in order to compare the strength of the association among the groups. In Chapter 4, the development phases of the cities are graphically illustrated with longitudinal data of urban population and GDP per capita. The timing to start operating urban mass transit system in each city is plotted on the above development curve in order to analyze an appropriate timing to introduce urban mass transit system in association with the development phases of the cities.

2.2 Data Source

The Study utilizes the existing available database to prepare basic urban profiles including urban population, urban density, and GDP per capita. It includes Demographia (Wendell Cox, 2010), World Urbanization Prospect of United Nations (UN, 2010), and World Development Indicators of World Bank (WB, 2008). Detailed information on urban transport such as car

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ownership and modal share are extracted from the Millennium Cities Database for of UITP (UITP, 2000) and the final reports of the JICA urban transport master plan studies (JICA MP), which cover 53 cities listed in the Table 1. In principle car ownership is the number of passenger per thousand people, while that of some cities may include different kinds of vehicles, such as trucks, due to the data availability. Modal share of public transport is the percentage share of total person trips (excluding walking trips) that use public transport mode, which include bus, BRT, , metro, suburban and rail. Semi-public transport such as taxi and para-transit are also included in the public transport, if it is segregated. Website information is referred to particularly for the history of mass transit system in each city.

2.3 Selection of the Cities for the Study

The Study adopts a step-wise approach to the selection of cities. First step analysis is done for UITP-100-cities. It include 15 cities in East/Southeast Asia, 3 in South Asia, 10 in Latin America, 6 in Middle East, 20 in North America and Oceania, 5 in Africa and 41 in Europe, of which year is 1995. All data required for the first step analysis are extracted from the UITP database. Since the UITP 100 cities cover fewer cities in developing countries, the second step analysis is focused on the cities in developing countries. 53 cities are selected from cities which were studied by JICA for urban transport master plan formulation in the past. Other 12 cities are added to the selection as follows. Countries such as and Korea are no longer developing countries, but the two cities of Singapore and are included in the selection because their presence is informative of the relationship between the takeoff process and urban transportation. is the only JICA-studied city in , but four more cities (, , and ) are added to the selection. In all, 65 cities (hereinafter called JICA-studied Cities) are selected as listed in Table 1, of which 25 cities are overlapped with the UITP-cities. As for 53 cities with JICA MP, required data are basically extracted from the JICA reports. In case the data is not available in the report, the available database is utilized. Population, urban density, and traffic-related indicators are extracted from UN (2010), Demographia (Wendell Cox, 2010), and UITP (2000), which are also applied for additional 12 cities without JICA MP. GDP per capita is taken from World Development Indicators (WB, 2008) for all 65 cities, since GDP per capita on individual cities are not available or not suitable for comparative analysis. There are two notes to be cared. First one is definition of a city. In the Study, cities are analyzed not as administrative units but as urban agglomerations, which are spatially continuously built up land mass of urban development above a certain level of urban density as defined in the UN (2010) and Demographia (Wendell Cox, 2010). The second one is disparity of current situations from the available compiled database. The Study deals with city-level statistics and transport indices of developing countries. Since it is hardly possible to use the same database uniformly for the entire selected cities, the Study uses nationally aggregated statistics or estimates the necessary data from the available growth rate. There are cases that the obtained data, particularly for UITP (2000) for the year of 1995 is outdated. For instance, in , car ownership in the cities have rapidly increased in the past decade. In , household motorcycle ownership has increased significantly, from 34% in 2002 to 72% in 2010 and modal share of commuting trips of passenger car and motorcycle have increased accordingly. However, this research intends to identify the general relationship between urban socio-economic indicators and traffic patterns and not to examine

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the historical change or the latest transport situations of the specific cities. Therefore the present trend of some cities does not reflect our findings. Table 1. Cities Selected for In-depth Analysis (1/2) City Country Population(000), 2010* JICA M/P year Asia 1 China 5,305 1994 2 China 5,460 1994 3 China 3,255 1996 4 China 4,785 2001 5 Ulaanbaatar Mongolia 885 2009 6 Jakarta 22,000 1987, 1990, 2004 7 Surabaya Indonesia 2,885 1983, 1997 8 Medan Indonesia 2,340 1980 9 Makassar (Ujung Pandang ) Indonesia 1,405 1989 10 8,250 1979, 1988, 1990 11 20,795 1973, 1985, 1999 12 Davao Philippines 1,335 1981 13 Ha Noi 2,355 1997, 2007 14 Vietnam 7,785 2004 15 5,835 1999 16 Johore Bharu Malaysia 860 1984 17 Vientiane Laos 575 2008 18 Phnom Penh Cambodia 1,560 2001 19 Kolkata (Calcutta) India 15,535 1992 20 10,135 2010 21 Seoul Korea 19,910 1970 22 Singapore Singapore 4,635 1988 23 Colombo Sri Lanka 2,080 1984, 2006 24 Kathmandu Nepal 1,280 1993 25 7,110 1991, drawing up 26 Karachi Pakistan 13,085 drawing up 27 1,650 2002 28 Kabul Afghanistan 3,370 2009 Middle East 29 17,290 1966,2002､2008 30 Baghdad Iraq 5,850 1988 31 8,170 1977 32 Damascus Syria 2,370 1999, 2008 Latin America 33 2,675 1965 34 Guayaquil 2,690 1983 35 Guatemala City Guatemala 1,810 1992 36 Barranquilla 1,795 1985 37 Cartagena Colombia 935 1992 38 Bogotá Colombia 7,845 1996 39 5,805 1967 40 Managua Nicaragua 895 1999 41 City Panama 945 1982 42 Asunción Paraguay 2,605 1986, 1992 43 Belém 1,610 1991, 2002 44 7,995 2005 45 4,210 1969 Africa 46 Kampala Uganda 1,625 2010 47 Nairobi Kenya 3,365 2006 48 Lusaka Zambia 1,395 2009 49 Dar es Salaam Tanzania 2,905 1995, 2008

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Table 1. Cities Selected for In-depth Analysis (2/2) City Country Population(000), 2010* JICA M/P year 50 Lilongwe Malawi 575 2008 51 Monrovia Liberia 500 2009 Europe 52 13,135 2009 53 1,995 2000 Cities which don’t have JICA master plans 54 China 13,955 None 55 China 18,400 None 56 , China 13,245 None 57 Mumbai (Bombay) India 21,255 None 58 Delhi India 20,995 None 59 Hyderabad India 6,720 None 60 Pune India 4,935 None 61 São Paulo Brazil 20,180 None 62 Curitiba Brazil 3,030 None 63 Brazil 11,670 None 64 12,975 None 65 Mexico 18,690 None * Demographia (Wendell Cox,2010) and refer to UN (2010) for cities not included in the Demographia

3. URBAN TRANSPORTATION AND CITY TYPES

3.1 Urban Density and Modal Share

The relationships between the urban density and the modal share of public transport are plotted in Figure 1. The figure depicts the relationship showing the terms of population size, GDP per capita and region. An appreciable tendency of correlation can be found between the higher urban density and the larger share of public transport, but the correlation coefficient itself (R2 =0.2473) of the UITP 100 cities is not sufficiently strong. The similar situation is indicated to three different classes of population size. In terms of GDP per capita, there is a strong correlation in high-income countries between the higher urban density and the larger share of public transport. The correlation diminishes progressively from high-income countries to medium-income countries and then to low-income countries. The correlation by region is generally weak, except that the cities in Europe and North America are found concentrated in a certain range of urban density and modal share. There is no distinct correlation in Southeast Asia, Middle East and Latin America, suggesting the diversity of cities in these regions. Figure 2 shows urban density and the modal share of public transport in the JICA-studied cities. There is no apparent indication of the correlation in the plotting. The JICA-studied cities are composed of mainly those of developing countries with no reflection of the correlation found in developed countries. There is a comparatively significant regional difference. The cities of Africa in the early phase of development are plotted in the area of low density and high public transport share in the chart. On the other hand, the cities in Latin America show a relatively high share of public transport regardless of the level of urban density. The cities in Asia show an appreciable tendency of correlation. The population size varies a great deal, but in the cities of over 5 million population, the higher urban density is correlated with the higher share of public transport in the similar manner found among 100 global cities.

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90 90 80 80 70 y = 0.1374x + 18.293 70 R² = 0.2473 y = 0.1374x + 18.293 60 60 R² = 0.2473 2 Population (R ) 2 50 GDP per capita (R ) > 10mil 50 (0.3608) > US$25,000 (0.7024) 40 3-10 mil (0.2296) 40 US$ 10,000-25,000 (0.6291) < 3mil (0.2613) US$ 3,000-10,000 (0.0563) 30 30 < US$3,000 (0.0327) all 20 20

線形 (all) Modal Share of Public Public of ShareTransportModal (%)

10 Modal ofShare Public Transport (%) 10

0 0 0 50 100 150 200 250 300 350 400 0 50 100 150 200 250 300 350 400

Urban Density (pax/ha) Urban Density (pax/ha)

90

80

70 y = 0.1374x + 18.293 R² = 0.2473 60 Region (R2) 50 EA&SEA (0.0235) South&Cen.Asia 40 Middle East Latin America 30 Africa 20 Europe Europe (0.1183) North America (0.432) 10 all Modal Share of PUblic Transport (%) 線形 (all) 0 North0 America50 100 150 200 250 300 350 400 Urban Density (pax/ha)

Note: R2 is shown only on the groups of 10 or more samples. Regression line is shown on the all plotted data. Source: UITP (2000) Figure 1. Urban Density and Modal Share of Public Transport (100 Cities in 1995)

100.0

90.0 Chongqing (86) Dar es Salaam (93) Lima (04) Mumbai (95)* 80.0 Manila (96) Belem (90) Barranquilla (83) (95) Damascus (98) Santiago (95)* 70.0 Nairobi (04) Davao (79) Manila (71) Lilongwe (09) Jakarta (02) 60.0 Kathmandu(91) EA&SEA (>5 mil) Rio de Janeiro(95)* (80) Delhi (95)* EA&SEA (<5 mil) São Paulo(95)* 50.0 South&Cen.Asia Bangkok (95) Bangkok (89) Mexico city(95)* Jakarta(95)* Surabaya(96) Middle East Seoul(95)* 40.0 Latin America Ujung Pandang(88) Surabaya(82) Cairo (95)* Africa Curitiba(95)

Modal Share Share Modal of Public Transport (%) Beijing (95)* Singapore(95)* Europe 30.0 Dailan (94) Tehran (95)* Shanghai(95)* Chendgu (00) 20.0 Phnom Penh (00)

Kuala Lumpur(95) (05) 10.0 HCMC (02) Vientiane (05) Hanoi (95) 0.0 0 2,000 4,000 6,000 8,000 10,000 12,000 14,000 16,000 18,000 20,000

Urban Density (pax/km2)

Note: The number in parentheses after each city name indicates the year of the JICA master plan study. Urban density and the modal share of public transport pertain to the year when each JICA study was conducted. Sources: Various JICA Study Reports and UITP (2000) for cities marked with * Figure 2. Urban Density and Modal Share of Public Transport (JICA-Studied Cities)

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3.2 Urban Density and Passenger Car Ownerships

Figure 3 plots the relationships between urban density and passenger car ownership in terms of population size, per capita GDP and regional distribution. An appreciable correlation (R2 =0.5413) exists between higher urban density and lower car ownership in relation to city population. The comparable results are shown in the past studies, such as inverse relationship between urban density and transport-originating energy consumption identified in Newman et al. (1999). The correlation is stronger in high-income countries but weaker in medium- to low-income countries. In terms of regional distribution, the cities of Europe and North America are found concentrated in a certain range of urban density and car ownership, although the correlation coefficient is not sufficiently high in these regions. There is no distinct correlation found in other regions.

800 800 GDP per capita (R2) 700 700 > US$25,000 (0.6598) Population (0.7298) 600 US$ 10,000-25,000 > 10mil (0.7912) 600 US$ 3,000-10,000 (0.1205) 3-10 mil (0.6258) < US$3,000 (0.2346) 500 < 3mil (0.1626) 500 all 指数 (all) 400 400

300 -0.01x 300 people) y = 535.46e R² = 0.5413 200 200 y = 535.46e-0.01x

100 100 R² = 0.5413 Passenger car Ownership(no/1000car Passenger

0 (no/1000OwnershipCar people) Passenger 0 0 100 200 300 400 0 100 200 300 400 Urban Density (pax/ha) Urban Density (pax/ha)

800 Region (R2) 700 EA&SEA (0.3511) 600 South&Cen.Asia Middle East (0.4872) 500 Latin America Africa 400 Europe (0.069) North America (0.4345) 300 all 指数 (all) 200 y = 535.46e-0.01x 100 R² = 0.5413

Passenger cars per 1000people percars Passenger 0 0 50 100 150 200 250 300 350 400 Urban Density (pax/ha)

Note: R2 is shown only on the groups of 10 or more samples. Regression line is shown on the all plotted data. Source: UITP (2000) Figure 3. Urban Density and Passenger Car Ownership (100 Cities in 1995)

Among the JICA-studied cities, there is a correlation between lower density and higher car ownership as shown in Figure 4. However, many low-density cities in Africa and Latin America have a low ratio of private car ownership.

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350

São Paulo(95)* 300

Buenos Aires(95)* 250 Bangkok (95)

Curitiba(95) Kuala Lumpur(95) 200 Mexico city(95)* EA&SEA (>5 mil) Bucharest(98) EA&SEA (<5 mil)

Rio de Janeiro(95)* South&Cen.Asia Surabaya(96) Middle East 150 Latin America

Singapore(95)* Africa Europe 100 Jakarta (95)* Passenger Car Ownership(no/1000 PassengerCar people) Santiago (95)* Tehran (95)* Panama City (80) Bogota (95) Jakarta (02) Baku (01) Belem (90) Surabaya(96) Manila (96) Ulaanbaatar (07) Chendgu (00) 50 Hanoi (05) Cairo (95)* Dar es Salaam (07) Phnom Penh (00) Delhi (95)* Manila (71) Damascus (98) Vientiane (05) Nairobi (04) Shanghai(95)* Mumbai (95)* Surabaya(82) HCMC (02) Hanoi (95) 0 0 2,000 4,000 6,000 8,000 10,000 12,000 14,000 16,000 18,000 20,000

Urban Density (pax/km2)

Note: The number in parentheses after each city name indicates the year of the JICA master plan study. Urban density and the passenger car ownership pertain to the year when each JICA study was conducted. Sources: Various JICA Study Reports and UITP (2000) for cities marked with * Figure 4. Urban Density and Passenger Car Ownership (JICA-studied Cities)

3.3 Passenger Car Ownerships and Modal Share of Public Transport

Figure 5 plots the relationships between passenger car ownership and the modal share of public transport in terms of population size, per capita GDP and regional distribution. A certain correlation can be read between higher car ownership and the lower share of public transport. The coefficient (R2 =0.3869) among 100 global cities, however, is not strong enough. The correlation is strong in high-income countries but diverges from this tendency in medium- to low-income countries. As for region, the cities of Europe and North America are respectively found concentrated in a certain range of car ownership ratios and public transport shares, although the correlation coefficient is not high in these regions. There is no distinct correlation in Southeast Asia, Middle East and Latin America. This implies the diversity of cities in these regions. Among the JICA-studied cities in Figure 6, non-Asian cities indicate a general tendency of inverse correlation between car ownership and the share of public transport. Especially in Latin America, the cities of lower car ownership have a higher public transport share in all cases (Figure 6). The cities in Asia show somewhat peculiar characteristics compared with those in other regions. Passenger car ownership is on the whole low in Asian cities. It is considered reflecting the low economic development level. There are cities with a high public transport share, whereas others, like Hanoi, Ho Chi Minh City, Phnom Penh and Vientiane, have a pronouncedly low share of public transport. Such a divergence from the general tendency of inverse correlation is mostly found in those cities dependent on two-wheelers.

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90 GDP per capita (R2) 90 > US$25,000 (0.5693) 2 80 80 Population (R ) US$ 10,000-25,000(0.4577) > 10 mil (0.5623) US$ 3,000-10,000 (0.1347) 70 70 3 mil-10 mil(0.3426) < US$3,000 (0.0796) 60 < 3 mil (0.3796) 60 all 線形 (all) 50 50 y = -0.0636x + 50.408 y = -0.0636x + 50.408 40 40 R² = 0.3869 R² = 0.3869 30 30 20 20 10 10

0 of Share TransportPublic (%) Modal 0

Modal ofShare Public Transport (%) 0 200 400 600 800 0 200 400 600 800 Passenger Car Ownership (no/1000 people) Passenger Car Ownership (no/1000people)

90 Region (R2) 80 EA&SEA (0.0002) 70 South&Cen.Asia Middle East 60 Latin America 50 Africa Europe (0.0749) 40 North America (0.388) all 30 線形 (all) 20 y = -0.0636x + 50.408 10 R² = 0.3869 0 Modal Share of Share TransportPublic (%) Modal 0 100 200 300 400 500 600 700 800 Passenger Car Ownership (no/1000 people)

Note: R2 is shown only on the groups of 10 or more samples. Regression line is shown on the all plotted data. Source: UITP (2000) Figure 5. Passenger Car Ownership and Modal Share of Public Transport (100 Cities in 1995)

100

Chongqing (86) 90

Dar es Salaam (07) Lima (04) 80 Manila (96) Mumbai (95)* Bogota (95) Damascus (98) Baku (01) Santiago (95)* 70 Nairobi (04) Belem (02) Bucharest (98) Buenos Aires (95)* Manila (71) Ulaanbaatar (07) 60 Lilongwe (09) Kathmandu(91) Jakarta (02) East & SE-Asia Delhi (95)* Panama (81) Rio de Janeiro (95)* South&Cen.Asia 50 Bangkok (89) Mexico City (95)* Sao Paulo (95)* Bangkok (95)* Middle East Medan (79) Jakarta (95)* Surabaya(96) Seoul (95) Latin America 40 Surabaya(82) Africa Cairo (95) * Singapore (95)* Europe ModalPublicofTransportShare (%) Curitiba (95)* 30 Beijing (95)* Dalian (94) Tehran (95)* Guangzhou (95)* Chendgu (00) 20 Phnom Penh (00)

10 Hanoi (05) HCMC (02) KL (95)* Vientiane (05) Hanoi (95) 0 0 50 100 150 200 250 300 350

Passenger Car Ownership (no/1000 people)

Note: The number in parentheses after each city name indicates the year of the JICA master plan study. The ratio of passenger car ownerships and the modal share of public transit pertain to the year when each JICA study was conducted. Sources: Various JICA Study Reports and UITP (2000) for cities marked with * Figure 6. Passenger Car Ownership and Modal Share of Public Transport (JICA-Studied Cities)

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3.4 Modal Share of Public Transport and Per Capita GDP

The relationship between the level of per capita GDP and the share of public transport is plotted in Figure 7 in terms of population size, urban density and regional distribution. The plotting shows a tendency of inverse correlation between the higher level of per capita income and the lower share of public transport. This general tendency is in agreement with the correlation between the higher economic level and the higher car ownership. The analysis of the JICA-studied cities plotted in Figure 8 does not indicate a similar general tendency of inverse correlation between the higher per capita income and the lower share of public transport. The divergence is especially pronounced among the cities of East and Southeast Asia, many of which rely on two-wheelers for intra-city trips. There is no clear relationship between two indices in Latin American cities where the share of public transport ranges from 50 to 80%. Cities in South and Central Asia and Africa are very low in per capita GDP and accordingly low in passenger car ownership. Intra-city travels are done mostly on foot. If such trips on foot are excluded from the total traffic, the share of public transport rises to a high level.

90 90 80 80 70 70 60 60 y = -0.0005x + 39.3 50 50 y = -0.0005x + 39.3 R² = 0.1628 40 R² = 0.1628 40 2 30 30 Pop Density (R ) Population (R2) >100 pax/ha (0.0046) 20 > 10mil (0.3906) 20 50-100 pax/ha (0.1029) 3-10 mil (0.1994) < 30 pax/ha (0.0087) 10 < 3mil (0.1515) 10 all 線形 (all) Modal Share of Public Public of ShareTransportModal (%) 0 0

100 1,000 10,000 100,000 of Share TransportPublic (%) Modal 100 1,000 10,000 100,000 Metropolitan GDP per capita (US$) Metropolitan GDP per Capita US$)

90 Region (R2) 80 EA&SEA (0.0195) South&Cen.Asia 70 Middle East Latin America 60 Africa 50 Europe (0.2611) North America (0.1331) 40

30 y = -0.0005x + 39.3 20 R² = 0.1628 10 Modal Share ofShare Public Transport(%) Modal 0 100 1,000 10,000 100,000 Metro politan GDP per capita (US$)

Notes: R2 is shown only on the groups of 10 or more samples. Regression line is shown on the all plotted data. Source: UITP (2000) Figure 7. GDP Per Capita and Modal Share of Public Transport (100 Cities in 1995)

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100.0

90.0 Chongqing (86)

Dar es Salaam (07) Lima (04) Mumbai (95)* 80.0 Manila (96) Barranquilla (83) Belem (90) Bogota (95) Dar es Salaam (93) Damascus (98) 70.0 Baku (01) Nairobi (04) Davao (79) Santiago (95)* Bucharest(98) Lilongwe (09) Ulaanbaatar (07) Belem (02) 60.0 Jakarta (02) Asuncion (84) Kathmandu(91) Panama (80) East & SE-Asia Guayaquil (82) Rio de Janeiro(95)* South&Cen.Asia 50.0 Delhi (95)* Bangkok (89) Jakarta (95)* Bangkok (95) Mexico city(95)* São Paulo(95)* Middle East Medan (79) Surabaya(96) Latin America 40.0 Surabaya(82) Ujung Pandang(88) Africa Cairo (95)* Curitiba(95) Europe Beijing (95)*

ModalPublicofTransportShare (%) 30.0 Dailan (94) Tehran (95)* Shanghai(95)* Chendgu (00) 20.0 Phnom Penh (00)

Hanoi (05) Kuala Lumpur(95) 10.0 HCMC (02) Hanoi (95) Vientiane (05) 0.0 0 500 1,000 1,500 2,000 2,500 3,000 3,500 4,000 4,500 5,000 GDP per capita (US$, constant price 2000)

Note: The number in parentheses after each city name indicates the year of the JICA master plan study. The ratio of passenger car ownerships and the modal share of public transit pertain to the year when each JICA study was conducted. Cities with GDP per capita more than US$ 8,000 are excluded. Sources: Various JICA study reports and UITP (2000) for cities marked with * Figure 8. Per Capita GDP and Modal Share of Public Transport (JICA-Studied Cities)

3.5 Patterns of Urban Two-wheeler Traffic

The foregoing analysis has cleared that the cities of developing countries heavily dependent on two-wheelers diverge distinctly from the general correlation found elsewhere. Separate analysis is necessary on such cities with a higher share of two-wheelers in urban traffic in terms of modal share, as shown in Figure 9. Of the JICA-studied cities where information is available on two-wheeler traffic, the 10 cities fit the condition of having a high share of two-wheelers in urban traffic. The 10 cities are listed in Table 2 with their modal shares. It should be noted that three Chinese cities, Beijing, Shanghai and Guangzhou, are not applicable to two-wheeler cities at present because of the rapid motorization since 1995 that put them wide apart from the other 7 cities. These two-wheeler cities are all found in Asian countries in the early economic development phase. Urban and transport indices of 10 Asian cities are examined in the comparative perspective with the findings about 100 global cities as shown in Figures 10 and 11. The different data definitions do not warrant straightforward comparison. For example, the urban density cited in the UITP-compiled database on 100 cities pertains to the central area of each city, whereas the equivalent figure available in JICA studies concerns the entire administrative area of a city. Asian cities heavily reliant on two-wheelers distinctively diverges from the strong correlations found among 100 cities between urban density and passenger car ownership and between car ownership and the modal share of public transport.

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Guangzhou (95)

Shanghai (95)

Beijing (95)

Phnom Penh (00)

Vientiane (07)

Ho Chi Minh (02)

Hà Noi (05)

Makassar (88)

Surabaya (96)

Chengdu (00)

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Public Organized Public para-transit Semi-public Private Car, truck Private 2-wheeler

Notes: Indicates the year when each JICA study was conducted. In Guangzhou, Shanghai and Beijing, bicycles are regarded as “mechanized non-motorized mode (NMT)” and classified as two-wheelers, and thus motorbikes are not classified as two-wheelers. The available data do not distinguish “public (para-transit)“ and “semi-public” modes. Sources: Various JICA study reports, the data on Guangzhou, Shanghai and Beijing taken from UITP (2000) Figure 9. Modal Share in Ten Two-wheeler Cities

Table 2. Summary Profiles of Ten Two-wheeler Cities City Year1) Pop. Density GDP Per Modal Share (%) 3) Car Ownership (000) (prs/km2)2) Capita (US$) Two-wheeler Public Trans. (vehicles/1000prs) Surabaya 1996 2,473 8,477 878 41.9 21.5 54.3 Makassar 1988 779 4,610 534 41.2 29.9 15.1 Hanoi 2005 3,183 3,456 539 86.7 6.2 51.0 Ho Chi Minh City 2002 5,285 2,461 448 91.5 2.5 12.1 Vientiane 2007 422 1,110 450 82.5 1.2 14.9 Phnom Penh 2000 1,152 2,980 293 70.2 0.0 41.8 Chengdu, 2000 3,068 5,240 949 67.1 14.7 48.3 Beijing 1995 8,164 2,457 658 42.6 30.7 42.9 Shanghai 1995 9,570 3,277 658 67.1 22.5 15.2 Guangzhou 1995 3,854 1,920 658 52.8 21.8 20.2 Notes: 1) Indicates the year when each JICA study was conducted 2) For Guangzhou, Shanghai and Beijing, the density is estimated on the basis of density figures in Demographia 2010, by using population estimates of Demographia and the UITP publication. 3) In Guangzhou, Shanghai and Beijing, bicycles are regarded as “mechanized non-motorized mode (NMT)” and classified as two-wheelers, and thus motorbikes are not classified as two-wheelers. The available data do not distinguish “ public (para-transit) “and “semi-public” modes. Sources: Various JICA study reports, data on Guangzhou, Shanghai and Beijing taken from UITP (2000), and per capita GDP from WB (2008)

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800 90 Pop. > 10mil 700 y = -0.0636x + 50.408 Pop. > 10 mil Pop. 3-10 mil 80 R² = 0.3869 Pop. 3 mil-10 mil 600 Pop. < 3mil 70 2-wheeler City Pop. < 3 mil 60 500 2-wheeler City 50 400 40 300 30 200 20 y = 535.46e-0.01x 10 100 R² = 0.5413

Modal ofShare Public Transport (%) 0 0 0 100 200 300 400 500 600 700 800 Passenger Car Ownership (no/1000people) PassengerOwnership Car 0 100 200 300 400 Urban Density (pax/ha) Passenger Car Ownership (no/ 1000 people )

Note: Regression line is shown on the all plotted data. Sources: UITP (2000), various JICA Study Reports, and Wendell Cox (2010)

Figure 10. Urban Density and Passenger Figure 11. Passenger Car Ownership and Car Ownership: Comparison between 100 Modal Share of Public Transport: Cities and Two-wheeler Cities Comparison between 100 Cities and Two-wheeler Cities

4. CONDITIONS FOR INTRODUCING URBAN MASS TRANSIT SYSTEM

It is now generally acknowledged regarding large cities in the world that it would be no longer possible to continue servicing the growing motorized traffic by constructing more and more . The most fundamental issue shared by global urban policymakers is how to facilitate a shift in transport demand from the private use of passenger cars to public transport means. The situation is the same in many large cities in the developing world. Those cities, however, are poorly provided with public transport means against the increasing trend in private car ownership and use. The available public transport means would not be capable of absorbing the shift of passenger traffic. An urban transport strategy in such a context must deal with the issue of selecting the most suitable mode which will play the central role in public transport over the medium to long time span. The study clarifies the conditions for selecting the central public transport mode by examining the JICA-studied cities with regard to their different social and economic development phases, and their choices of urban mass transits.

4.1 Current Situations of Urban Mass Transit in Operation

The time of starting the operation of urban mass transit system including metro and BRT system is summarized in Table 3 based on the JICA-studied cities. In the Study, metro systems include subway, LRT and monorail systems. A metro system is defined as an urban electric passenger transport system with high capacity and high frequency of service, which is totally independent from other traffic, or pedestrian. The terms heavy rail (mainly in North America) and heavy urban rail often have similar definitions. The term BRT is applied to a variety of public transport systems using buses to provide faster, more efficient service than conventional bus services. Often this is achieved by improving existing infrastructure, vehicles and scheduling. The goal of BRT systems is to achieve the service quality comparable to railway transits with low cost and the flexibility of bus transits.

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Including those cities where a system is under construction, 11 cities chose the operation of a metro system only and 10 cities decided on a BRT system, while 17 cities chose both metro and BRT systems. Table 4 shows the shift direction between metro and BRT system. Table 3.Time of Starting Operation of Metro and/or BRT Systems (1/2) City Time of Starting Operation City Country Metro System BRT System Asia Seoul Korea 1974 2005 Hangzhou China - 2006 Chongqing China 2005 (Monorail) 2008 Dalian China 2003 2008 Chengdu China 2010 - Beijing China 1969 2004 Shanghai China 1995 - Guangzhou China 1997 2010 Ulaanbaatar Mongolia - - Manila Philippines 1984 (LRT) - Davao Philippines - - Hanoi Vietnam Under construction Under planning Ho Chi Minh City Vietnam Under construction - Vientiane Laos - - Phnom Penh Cambodia - - Bangkok Thailand 1999 2010 Kuala Lumpur Malaysia 1996 (LRT) - Johore Bharu Malaysia - - Singapore Singapore 1987 - Jakarta Indonesia Under construction 2004 Surabaya Indonesia - Under planning Medan Indonesia - Under planning Makassar Indonesia - - Dhaka Bangladesh - - Kolkata (Calcutta) India 1984 Under planning Mumbai (Bombay) India Under construction 2008 Delhi India 2002 2008 Hyderabad India Under planning Under construction Pune India Under construction 2008 Lahore Pakistan - - Karachi Pakistan - - Colombo Sri Lanka - - Kathmandu Nepal - - Kabul Afghanistan - - Baku Azerbaijan 1967 - Middle East Teheran Iran 2000 2008 Baghdad Iraq - - Damascus Syria - - Cairo Egypt 1987 2009 Latin America Guadalajara Mexico - - Mexico City Mexico 1969 2005 Guatemala City Guatemala - 2007 Managua Nicaragua - - Panama City Panama - - Barranquilla Colombia 2010 Cartagena Colombia - Under construction Bogota Colombia - 2000

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Table 3. Time of Starting Operation of Metro and/or BRT Systems (2/2) City Time of Starting Operation City Country Metro System BRT System Caracas Venezuela 1983 Under construction Guayaquil Ecuador - 2006 Lima Peru 2003 2010 Santiago Chile 1975 2007 Belem Brazil - - Sao Paulo Brazil 1974 2003 Rio de Janeiro Brazil 1979 Under construction Curitiba Brazil - 1972 Asuncion Paraguay - - Buenos Aires Argentina 1923 - Africa Kampala Uganda - - Nairobi Kenya - - Lusaka Zambia - - Dar es Salaam Tanzania - - Lilongwe Malawi - - Monrovia Liberia - - Europe Istanbul Turkey 2000 2007 Bucharest Romania 1979 - For reference Tokyo Japan 1927 - Sources: Metro information from the Global List of Metro Systems, (home page of the Japan Association of Metro Companies), and BRT information from relevant sources.

Table 4. Operation or Development of Metro and BRT Systems Metro and BRT Systems Metro System Only BRT System Only Order of Operation Order of Operation BRTMetro MetroBRT Asia Chengdu (2010) Hangzhou(2006) Jakarta Seoul (19742005) Shanghai (1995) (2004UC) Chongqing(monorail) Manila (LRT) Mumbai (20052008) (1984) (2008 UC) Dalian (20032008) Ho Chi Minh Hyderabad Beijing (19692004) City(UC) (UC UP) Guangzhou (19972010) Kuala Lumpur Pune Hanoi (metro UC and BRT (LRT) (1996) (2008UC) UP) Singapore(1987) Bangkok (19992010) Baku (1967) Delhi (20022008) Kolkata/Calcutta(1984UP） Middle － － － Tehran (20002008) East Cairo (19872009) Latin Buenos Aires Guatemala City － Mexico City (19692005) America (1913) (2007) Caracas(1983UC) Barranquilla Lima (20032010) (2010) Santiago (19752007) Cartagena (UC) São Paulo (19742003) Bogotá (2000) Rio de Janeiro(1979UC） Guayaquil(2006) Curitiba (1972) Africa － － － － Europe Bucharest (1979) － － Istanbul (20002007) Note: UC: Under Construction, UP: Under Planning

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4.2 Development Phases and Timing to Start Operation of Metro Systems

Figure 12 has been prepared to show how the development phases of the cities are related to the timing to start operating their metro systems. The trends of urban population (UN, 2010) and GDP per capita (US$, constant 2000) of every 5 years during the period of 1960 -2010 (WB, 2008) are plotted in the figure. The time when each metro system was opened for operation is marked by a circle. Two parallel lines in red signify the same levels of GRDP (gross regional domestic product) among the cities. GRDP of a given city in this context is obtained as the product of the city’s population and GDP per capita. The solid (the start line or S-line) means the GRDP level of US$3 billion, and the dotted line (the end line or E-line) shows the level of US$30 billion. The time to start the metro operation in the respective cities mostly falls between these two GRDP lines. In East and Southeast Asia, Seoul and Singapore are plotted around the E-line. Chengdu, Bangkok and Chongqing are around the S-line, while Kuala Lumpur, Dalian, Shanghai and Manila are found between the two lines. The metro system in Beijing was officially opened in 1969, but the construction and operation as a mass transit system in the true sense of the word began in 2002. Thus, the city is almost on the E-line. Tokyo began the operation of its first metro system in 1927. In South and Central Asia and Middle East, Teheran, Cairo and Delhi are found at midpoints between two GRDP lines, with Kolkata on the S-line. The metro system in Kolkata began its operation in 1984 over a distance of 28km and no extension has been made ever since. A plan of new extension is currently being prepared. The metro system in Delhi started operation in 2002, currently with 6 lines totaling 190km. In Latin America, Buenos Aires was the first city to introduce its metro system in1913, and Mexico City, Rio de Janeiro, Sao Paulo and Lima are found around the E-line. Caracas and Santiago are found between two GRDP lines. In Africa, there is no city that operates a metro system. In Europe, Istanbul is near the E-line, whereas Bucharest is around the S-line, opening its metro system in 1979.

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Tokyo GDP per capita to Urban Population (1960-2010) －East Asia,Southeast Asia－ Seoul 100,000 Hangzhou Chongqing

E line Dalian (GRP: US$30 billion) Chengdu Beijing S line Shanghai (GRP: US$3 billion) Guangzhou

10,000 Ulaanbaatar Manila Davao Hà Noi Ho Chi Minh City Vientiane

GDP per capita (USD 2000 constant) 2000 (USD capita per GDP Phnom Penh 1,000 Bangkok Kuala Lumpur Johore Bharu Singapore Jakarta Surabaya Medan 100 Makassar 100 1,000 10,000 100,000 metro open

Population (x1000, GRDP=$3bln GRDP=$30bln GDP per capita to Urban Population (1960-2010) －South Asia,Central Asia,Mideast－ Dhaka 10,000 Kolkata (Calcutta) Mumbai (Bombay) Delhi Hyderabad Pune (Poona) Lahore Karachi 1,000 Kathmandu Baku Tehran

Baghdad GDP per capita (USD 2000 constant) 2000 (USD capita per GDP Damascus Cairo

100 metro open 100 1,000 10,000 100,000 GRDP=$3bln Population (x1000, UN) GRDP=$30bln Figure 12. Metro Operation Relative to GDP per capita and Urban Population Size (1/2)

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GDP per capita to Urban Population (1960-2010) －Latin America－ Guadalajara 10,000 Mexico City Guatemala City Managua Panama City Barranquilla Cartagena Bogotá Caracas Guayaquil 1,000 Lima Santiago Belém São Paulo Rio de Janeiro

GDP per capita capita per (USD 2000constant)GDP Curitiba Asunción Buenos Aires 100 metro open 100 1,000 10,000 100,000 GRDP=$3bln GRDP=$30bln Population (x1000, UN) GDP per capita to Urban Population (1960-2010) －Africa,Europe－ 10,000 Kampala Nairobi Lusaka Dar es Salaam Lilongwe Istanbul Bucharest 1,000 metro open GRDP=$3bln

GRDP=$30bln GDP per capita capita perconstant) 2000 (USD GDP

100 100 1,000 10,000 100,000 Population (x1000, UN) Note: The red solid line indicates the GRDP of US$3 billion and the red dotted line the GRDP of US$30 billion (constant 2000 US$). GRDP of each city is calculated by multiplying the city population by GDP per capita Source: WB (2008), UN (2010), and Wendell Cox (2010) Figure 12. Metro Operation Relative to GDP per capita and Urban Population Size (2/2)

5. CONCLUSION

Analysis of relationship between urban socio-economic indicators and traffic pattern shows that cities of developing countries have a different trend from global tendencies, which are identified from the UITP database. Among Asian cities, the concept of two wheeler city should be introduced. The larger their modal share of two-wheelers, the more divergent they are from the global tendencies. It is necessary to consider such traffic patterns specific to cities in developing countries, when formulating urban transport development strategies. It is reasonable to argue that the metro operation timing is closely related to the achieved level of socio-economic development of cities. The larger their population, the more likely they are to develop a metro system, even though GDP per capita is not high enough. On

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the other hand, cities blessed with higher levels of GDP per capita opened their metro systems, even if their population is yet small. It is possible to presume that the achievement of a certain level of GRDP be the condition for a given city to introduce a metro system There are some issues for future research. Firstly, while the Study did overall analysis on the relationship of urban transportation patterns and city types, and only focused on 2-wheeler cities, future study should conduct in-depth analysis focusing on specific types of cities, such as low-income cities, Islamic cities, or cities where walking has high share in the modal share. It will be useful to formulate more effective urban transport strategy. Secondly the Study focused on the limited number of urban indicators, including urban population, urban density, and GDP per capita, there are many other indicators which are closely related with urban transport pattern, including geographical conditions, road density, modal share of public transport, etc. Further research should take comprehensive analysis with wide range of urban indicators. Thirdly, since the Study only focused on the timing to start mass transit operation as a major part of urban transport strategies, the future research should analyze relationship of other important transport policies with urban typology, such as development of urban expressway and Traffic Demand Management (TDM) scheme.

ACKNOWLEDGMENT

The authors deeply express our sincere gratitude to Dr. Tetsuro HYODO, Professor of Tokyo University of Marine Science and Technology and Dr. Shinya HANAOKA, Associate Professor of Tokyo Institute of Technology. They provided precious comments and support for the Study.

REFERENCES

Newman, P. and Kenworthy, J. (1999) Sustainability and Cities: Overcoming Automobile Dependence, Island Press, Washington Suzuki, T. and Muromachi, Y. (2007) Population Density Automobile Use Relationship: Re-Examination in Asian and American Mega-Cities, Journal of the Eastern Asia Society for Transportation Studies, Vol.6. Japan International Cooperation Agency (JICA) (2011) The Research on Practical Approach for Urban Transport Planning Wendell Cox Consultancy. (2010) Demographia World Urban Area Edition 6.1, Belleville, Illinoi, USA United Nations (UN) (2010) World Urbanization Prospect, , (http://esa.un.org/unup/index.html) World Bank (WB) (2008) World Development Indicators, Washington D.C. International Association of Public Transport (UITP) (2000) Millennium Cities Database for Sustainable Transport,

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