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The demand for public buses in sub-Saharan African cities: Case studies from Maputo and Nairobi
IATSS Research 43 (2019) 122–130
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IATSS Research
Original research paper
The demand for public buses in sub-Saharan African cities: Case studies from Maputo and Nairobi Atanasio Tembe ⁎, Fumihiko Nakamura, Shinji Tanaka, Ryo Ariyoshi, Shino Miura Graduate School of Urban Innovation, Yokohama National University, 79-5 Tokiwadai, Hodogaya-ku, Yokohama 240-8501, Japan)
a r t i c l e
i n f o
Article history: Received 13 July 2018 Received in revised form 23 October 2018 Accepted 30 October 2018 Available online 11 November 2018 Keywords: Person Trip data Chapas Matatus Sub-Saharan Africa Logistic regression
a b s t r a c t Despite the limited transportation options, public buses have been proved unpopular in many Sub-Saharan African cities. While the declining use of local buses in the developed world is often associated with increasing number of transport alternatives, relatively little is known about African cities. A substantial number of studies are focused on how to replace buses with the rapid transportation modes and less diagnosis is applied to the factors affecting the use of buses. Using household information survey, this paper attempts to investigate the factors affecting the demand for buses in Maputo and Nairobi by employing logistic regression approach. Results show both similarities and variations among the factors affecting the likelihood of using buses. For example, the likelihood of using buses decreases with vehicle ownership and employment status in the two cities. In addition, gender (female) has no effect on the likelihood of choosing buses over paratransit in both cities. Second, while age decreases the likelihood of choosing buses and residence location increases that in Maputo, the opposite is observed in the case of Nairobi City. The main implication of these findings is that there is a need to share the roles of public transportation between buses and paratransit. © 2018 International Association of Traffic and Safety Sciences. Production and hosting by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
1. Introduction Conventional buses face a continued service deterioration in SubSaharan African cities despite the rapidly growing demand for public transportation. According to [1], the sources of the declining urban transport services in developing countries include the high concentration of urban population, economic activities and motorization or increase in car ownership in major cities, the limited supply side and weak regulatory institutions. Different cities have adopted varying approaches to tackle this problem; for example, Maputo managed to maintain bus operations by subsidizing the bus company Transportes Publicos de Maputo (TPM), while in the case of Nairobi Kenyan Bus Services (KBS) was privatized. Despite these measures, service declining seems relentless considering the modal share of buses. Currently, a significant number of the existing studies in developing countries are more focused on how to replace the conventional bus systems with the rapid transportation modes mainly Bus Rapid Transit (BRT), and less diagnosis is devoted to investigating the factors contributing to declining modal share of local buses. An understanding of these
factors is critical at least for two reasons: first, many cities in the developing world are unable to replace the existing bus systems. Secondly, even in cities where rapid transportation modes have been established, conventional buses can still play a major role as feeder modes. In a study on the market share of public transport in South Africa, [2] argue that access to public transport is reduced by long walking distances and long waiting times due to inadequate service and route coverage, as well as poorly arranged schedules. As case studies, Maputo and Nairobi are selected primarily because the two cities share similarities and variations in terms of public transportation and socioeconomic characteristics. The two cities initially relied on publicly-owned bus companies to provide most of the public transport services, which were later largely replaced by paratransit services. While Maputo still subsidizes the public bus company, in Nairobi it has been split into two competitive bus companies. Second, both cities have a young and rapidly growing urban population. As such, they face more increasing challenges to service the demand side. This comparison can help us to understand the factors affecting the use of buses, and to assess the policy options adopted in these two cities. 1.1. Objectives
⁎ Corresponding author. E-mail address: [email protected] (A. Tembe). Peer review under responsibility of International Association of Traffic and Safety Sciences.
• To identify the similarities and variations on the demand for public buses between Maputo City and Nairobi City; and • To investigate the factors affecting the use of public buses.
https://doi.org/10.1016/j.iatssr.2018.10.003 0386-1112/© 2018 International Association of Traffic and Safety Sciences. Production and hosting by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http:// creativecommons.org/licenses/by-nc-nd/4.0/).
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1.2. Structure The paper is organized as follows. In Section 2, previous studies are reviewed. Section 3 describes the evolution of bus transport in the selected cities. A detailed analysis of the demand side is provided from Section 4 to Section 6. Section 7 is devoted to model estimation and discussion of results. Finally, the conclusion is presented in Section 8. 2. Factors affecting bus service demand and supply In this section, we review the literature on the travel behavior to understand the factors influencing the use of buses. According to [3–5], transport mode choice is influenced by the characteristics of the decision maker or individual, the number and attributes of alternatives, and the decision rule. Under travel choice theories a decision maker is assumed to rank possible alternatives in order of his or her preference and to choose from the available alternatives the option which he or she considers the most desirable considering his or her preferences and the relevant constraints placed on decision-making. The decision maker can be an individual or a group of people. Individuals not only face different choice situations, they also have varying tastes [3]. Any transport alternative is assumed to comprise a set of options that are feasible and known during the decision process by the decision maker. The attributes of alternatives are measured on a scale of attractiveness such as speed, travel time or travel costs. Lastly, the choice of transportation mode follows some rules including dominance and satisfaction. Despite the appealing theoretical aspects of choice theories, their applicability in Sub-Saharan African context should raise concerns. The main reason is that most of the assumptions do not hold. For example, decision makers usually do not have complete information relative to the attributes of the transportation modes. As noted by [6], most people in developing countries have no choice than to use public transportation. Several studies have analyzed the relationships between buses and private transport. [7] associated the rise in car use with the decline of bus services in developing countries. Although the empirical evidence at the country level may suggest a strong relationship between the rising income and car use at the urban level, local characteristics, traffic congestion, and policies do affect travel decisions [7]. A similar result is reported by [8], who indicates that use of local buses is increasingly affected by reliance on private transport. Public bus services in Japanese cities are less profitable compared to private operations. The main reason is that there are restrictions imposed upon public bus operators to continue providing services on unprofitable routes [9]. One of the most informative literature on the travel demand in the context of developing countries is the publication on public transportation in developing countries by [1]. The supply of public transport in developing cities is determined by various demographical, environmental, institutional, and economic factors. The author states that population density, distribution, and growth are three important factors determining the demand for transport. In terms of modeling approaches, [10] argue that logit models are by far the most widely applied discrete choice models due to the fact that the formula for the choice probabilities takes a closed form and is readily interpretable. Other authors including [11,12] used logistic regression to model the travel behavior in developing cities. In this paper, factors affecting the use of buses were investigated using a logistic regression as well. We selected this approach because the model can be easily estimated. The amount of research on bus transport in Sub-Saharan African cities is minimal. As a result, relatively little is known about the factors influencing the demand and supply sides. A study on the public transportation in Dar es Salaam by [13] concluded that public services have deteriorated rapidly due to problems such as an aged and obsolete fleet, poor maintenance and a shortage of foreign exchange to purchase spare parts. Similarly, the decline in terms of quantity and quality of bus services in Ghana is associated with the deterioration of the vehicle fleet and the inability to expand with the rapid population growth [14].
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A study on mobility in 6 Sub-Saharan African cities by [15] revealed a relatively small public transport sector concentrated on the major radial roads, and an increasing informal sector or paratransit. In addition, household ownership of motorized two-wheelers and the private car is reported to be very low. A comparison of urban transport systems across some African cites by [16] identified the affordability as the main concern for the use of buses. A substantial share of urban dwellers was found to have no daily access to public transport services because these are expensive and not easily accessible. Bus service supply can be expressed in terms of capacity, i.e. frequency, headway, fleet size, subsidy availability, number of routes, coverage, and vehicle size. Bus service supply is mostly influenced by institutional and policy factors. Bus service demand is often expressed in terms of urban structure, economic indicators, and demographic indicators. These are for example the density of population and economic activities, geographical distribution of income, economic growth and car ownership. From the literature review we can, therefore, conclude that the factors affecting transport mode choice are as follows: Characteristics of the trip maker • • • • • •
Car ownership Possession of a driving license Household composition Income Residential density Other decisions Characteristics of the trip
• Trip purpose • Time of the day • Individual or group trip Characteristics of transportation mode • • • • •
Total travel time: walking and waiting times, in-vehicle, and transfers Monetary costs Parking costs Reliability of services Comfort, convenience, and safety standards
3. Bus supply: evolution of bus transport It is important to review the foundations upon which urban transportation systems are built on to better understand the declining urban transport services both in Maputo and Nairobi City. First, we analyze how urban transportation services evolved in Maputo followed by Nairobi. 3.1. Maputo Mozambique has been an independent country since 1975. Urban transportation systems were nationalized at the outset, yielding to a regulated public transport system. Urban transportation services were exclusively provided by a state-owned bus company, Transportes Urbanos de Maputo (TPM). Fares were regulated with a purpose to ensure affordability of most population. At the beginning of the 1980s, Maputo witnessed an increased immigration of people from rural areas to the capital city [17]. Meanwhile, the bus company experienced difficulties with providing bus services due to economic policies. For example, the bus utilization ratio which is the proportion of buses in services over the total bus fleet reduced from approximately 37% in 1975 to roughly 18% as of 2015. Nominal buses stand for the bus fleet owned by the company while operating buses represent the effective number of buses in service. Because of these problems, the number of passengers gradually declined from 60 million users in 1975 to 10 million or 17% in 2015 (Fig. 1). As reported by [18], at the beginning public bus companies
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Fig. 1. Bus services (based on Maputo City data).
were able to operate without subsidies in Africa, however, as deficits grew and subsidies did not grow commensurately, bus operators faced enormous difficulties either to maintain or to replace bus fleet. According to [19], in order to meet the increasing demand in urban areas, individuals initiated the paratransit services and gradually their number increased. These paratransit operators were formally acknowledged at the end of the 1980s when the government gave them permission to operate urban transportation services. This yielded to the development of chapas industry (Fig. 2). The designation chapas is due to the flat fare that was charged. The seating-capacity of chapas ranges from 15 to 25 passengers. By the beginning of the 1990s, paratransit operators had a significant share of the market. As of 2013, the urban transport modal share is dominated by walk (45%), followed by chapas (32.9%), private car (10.2%), bus (9.2%), rail (0.6%) and others (1.3%) (Tables 1 and 2). 3.2. Nairobi According to [20], urban transport services in Nairobi were exclusively provided by Kenyan Bus Services (KBS) until 1973. Kenya Bus Services was established in 1934 and it operated urban transport services utilizing high capacity buses (double-decker). Bus fares were controlled at the level that allowed the company to operate profitably and to expand. KBS also encountered difficulties due to regulatory institutions which resulted in the declining market share. To meet the growing needs for transportation, Kenyan authorities legalized the paratransit matatus in 1973 [18]. However, [20] state that matatus are reported to have started their transport services in the 1950s and they were considered as illegal commercial operators. Like chapas, the seating-capacity of matatus varies from 14 to 25 passengers (Fig. 3).
3.3. Comparison According to [18], matatus were mainly used as a transportation mode of native Africans during the colonial period. The word matatu means 30 cents which was the standard flat fare that was charged. Despite the legalization of matatus, Kenyan Bus Services retained the monopoly status which was only broken at the onset of the 1980s with the formation of another state bus company Nyayo Bus services. Similar to Maputo, the beginning of the 1990s witnessed an increasing transport modal share in favor of matatus. The current public transportation (Fig. 4) is a complex paratransit system [21]. Despite its demand flexibility, competitiveness, and spatial range the authors argue that the paratransit system can never be sustained as the primary transport for the metropolitan area because the quantity of service supplied is completely disconnected from the capacity and quality of the infrastructure. This is because matatus with seating capacity ranging from 14-to-25 passengers make use of road space less efficiently than buses with a minimum seating capacity of 50 passengers. The current road infrastructure barely accommodates the current number of matatus. As the city is growing more rapidly (city expansion and increases of urban population) to rely all demand on matatus seems to be technically impossible when considering corridors with the higher population density. However, if the budget is available and city authorities can strongly control density, matatus may become the primary transportation. The modal share has experienced some variations between 2003 and 2013. As of 2013, the market share is primarily by walk (39.7%), followed by matatus (28.4%), private car (13.5%), bus (12.2%), rail (0.2%) and others (5.9%).
Fig. 2. Public buses (left-hand side) and chapas (right-hand side) in Maputo. (Source:https://www.google.com/Transportes Urbanos de Maputo)
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4. Bus demand: urban structure data
Table 1 Characteristics of the cities. Maputo Metropolitan Area Regulated to Subsidies Difficulties
Nairobi City
Ensure Affordability 1 subsidized 1980s – reduction in fleet size Legal paratransit 1980s Paratransit/bus fares Flat: $0.12 Present bus mode Bus: Paratransit 9%:33% share Vehicle Size Bus: Paratransit Normal: Minibus/Mid-bus Fleet Size Bus: Paratransit Chapas 280:4150 Bus: Paratransit 65:130 Route Coverage/number of routes
Operate profitably and expand 2 competing privately owned 1980s – increase in operational costs 1973 $0.50–$0.99 Bus: Paratransit 12%:28% Bus: Paratransit Normal/Double Decker: Minibus/Mid-bus Bus: Paratransit Matatus 23,000:72000 n/a
In this section, we describe the geographic features of the cities; for example, the area, employment, and population. For a better understanding, we divided the cities into comparable zones. Zone 1 encompasses the Central Business Area (CBA), zone 2 is the built-up area while zone 3 includes the suburban area. Lastly, zone 4 comprises the rest of the metropolitan area. 4.1. Maputo Maputo, the capital city of Mozambique is the economic and political center of Mozambique. It is situated in the southern region of the country. The city (Fig. 5) was established as the main urban center following the development of transport infrastructures such as road and railway connecting Mozambique and South Africa. Maputo is still growing rapidly and expanding outwards. It is here where Maputo port, one of the major ports in southern Africa is located. 4.2. Nairobi
Table 2 Summary description of zones. Maputo Metropolitan Area
Nairobi City
Area Population Established Port
1228 sqm 2.3 million (2007 Census) 1884 Yes
4784 sqm 5.0 million (2009 Census) 1907 No
Zone 1 CBD Area Population Population Density Employment Density
8 sqm 45,734 5614 1.7
11 sqm 274,607 24,964 n/a
Zone 2 Build Up Area Population Population Density Employment Density
10 sqm 76,863 7971 2.1
12 sqm 261,855 21,821 n/a
Zone 3 Sub-Urban Area Population Population Density Employment Density
605 sqm 1 million 960 0.3
555 sqm 2 million 4688 n/a
Zone 4 Metropolitan Area Population Population Density Employment Density
605 sqm 1 million 960 0.2
4206 sqm 2 million 446 n/a
According to [22], Nairobi (Fig. 5) is one of the most important economic centers in East and Central Africa. It accounts for 50% of formal employment in Kenya and generates over 50% of Gross Domestic Product. Nairobi plays an important role, not only as the political center but also as a model for economic development. In line with [21], Nairobi was established as the colonial capital of British East Africa. It was initially a way station between the Port of Mombasa on the Indian Ocean and Uganda. From the initial settlement of the city, the British imposed a clear and distinct spatial segregation between European and African residents. 4.3. Comparison 4.3.1. Bus demand: demographic data 4.3.1.1. Maputo and Nairobi. The household information survey (HIS) used in this paper was conducted in 2012 and 2013 in Maputo and Nairobi, respectively, by the Japan International Cooperation Agency (JICA). The sample dataset for two cities are is described in Table 3. It should be noted that: • Person-trip data only covered the Nairobi City while in the case of Maputo the whole metropolitan area was surveyed. One of the implication is that we are not able to judge accurately if there is a difference in the travel behavior between residents of these two cities. This is mostly because city areas in Sub-Saharan Africa tend to have a relatively denser bus network compared to outside zones. As a result,
Fig. 3. Number of trips (1000 trips) by mode between 2004 and 2013. (Source: HIS).
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Fig. 4. Matatus (left-hand side) Public buses (right-hand side) in Nairobi. (Source: https://www.google.co.jp/kenyan bus services)
the service frequency of public transport is higher in the city areas than outside zones. Given that paratransit tend to have a higher service frequency than buses, they are more likely to attract more users than buses at city area level. Second, we expect a difference in the daily traffic volume attracted by each city center. Maputo metropolitan area is expected to generate more traffic volume than Nairobi City. • Lastly, considering that Maputo and Nairobi share some similarities such as lower population density in the suburbs, and high employment concentration in the central areas, commuters are likely to exhibit similarities in their travel behavior. This is because most people tend to commute from the suburbs to the city centers. • The occupation status information from Maputo was more disaggregated than that in Nairobi and we had to adjust for comparison purposes. Figs. 6-7 provide information about the age distribution and household size in the selected cities. Fig. 6 shows that approximately 47% of individuals in Nairobi and 45% in Maputo are b19 years old. The proportion of individuals aged between 20 years and 60 years old is approximately 50% in both Nairobi and Maputo. Maputo, with 5% of individuals over 60 years old has a slightly higher figure than Nairobi with 2%. Approximately 87% of households in Nairobi have b3 persons compared to 11% in Maputo (Fig. 7). Nairobi has b12% of households with at least 3 persons while the same figure amounts to 89% of households in Maputo. With respect to occupation, the combined ratio of persons with no job, housewives, and students, is at 67% in Maputo and 24%
for Nairobi. As it would be expected under this situation, the proportion of employed persons is higher in Nairobi than Maputo, 57% to 20%, respectively. 4.4. Comparison The data confirms: • The Findings of [23] who considered East African cities as being “young cities” with a substantial proportion of the population being the first generation of the urban dwellers.
Table 3 Household attributes.
Population Zone 1–3 Households Surveyed Individuals Surveyed Sample size b 19 years old 20–60 years old N 60 years old HH b 3 persons HH N 3 persons No job, housewives or students Employed
Fig. 5. Major bus routes in Maputo (left) and (right) Nairobi (source: Bing maps).
Maputo Metropolitan Area
Nairobi City
1.1 million 9530 43,927 2% 45% 50% 5% 11% 89% 67% 20%
3.1 million 9973 27,618 1% 47% 50% 2% 87% 12% 24% 57%
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Fig. 6. Age distribution in Maputo and Nairobi (Source: HIS).
Fig. 7. Household size distribution in Maputo and Nairobi (Source: HIS).
• Households surveyed in the Maputo Metropolitan Area were relatively big compared to the smaller households in Nairobi City. This may seem surprising because, in a study on urban travel behavior by [24], it was indicated that the households in Nairobi City are relatively large with an average of 3.5 or 4 people. However, [24] also noted that many Kenyans often move out of Nairobi City when they retire, and considering that the survey only covered Nairobi City and not the metropolitan area, the findings can, therefore, be substantiated. • Unemployment in Maputo Metropolitan Area is significantly higher than in Nairobi City.
5. Bus demand: mode choice data 5.1. Relative to time of day Fig. 8 provides some key insights to understand the temporal distribution of trips in both cities. Unlike the commonly two peak-hour periods found in Nairobi City, the temporal distribution of trips in Maputo is characterized by three peak-hour periods, namely, at 6 a.m., around 12:00, and 5 p.m., respectively. On the word of [23], the increasing importance of informal sector activities as a source of income in the
Fig. 8. Mode choice by the time of day in Maputo and Nairobi (Source: HIS).
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Fig. 9. Mode choice by trip purpose in Maputo and Nairobi (Source: HIS).
developing cities is changing the nature of travel behavior. They state that even though commuting peaks remain, they are overlaid in both space and time by more complex irregular movements associated with trading, hawking, and employment-seeking. However, to better understand differences in trip patterns we investigate trip purpose in the next sub-section. 5.2. Relative to trip purpose Fig. 9 displays the temporal trip distribution by trip purpose. The morning peak-hour periods in both cities mostly comprise work and school trips, whereas the evening peaks are dominated by home trips. As it was noted, a day peak is observed in Maputo and it is constituted by trips to school and to work. Due to an increased number of children in school age and limited supply capacity, the Government introduced in the 1980s 3 distinct school periods; a substantial proportion of students have classes from 7:30 a.m. to 12:30, the second and third periods from 12:30 to 17:30 and 17:30 to 22:30 respectively. 5.3. Relative to zone
In the case of Maputo, chapas constitute the most popular transport mode. Part of the explanation for the popularity of paratransit is because the service area of buses is constrained by the limited road coverage in Maputo (0.24 km per 1000 people) and Nairobi (0.22 km per 1000 people). In the analysis of mobility in 6 African cities [15] and conclude that public transport services in these cities are concentrated on the major paved radial roads. 6. Model estimation and discussion The literature reviewed suggests some associations between transportation mode and household/individual characteristics. To analyze these relationships, we have estimated two logistic regression models. The variables in the models were selected in accordance with the literature review. Only individuals commuting by paratransit and public transportation were considered in the analysis. Table 6 shows the variables that are included in the logistic regression models. The following assumptions are made: • The dependent variable is the transport mode – public buses and paratransit mode (chapas or matatus). • Explanatory variables are individual/household attributes – gender, age, employment status, vehicle ownership, income, and residential location.
Tables 4 and 5 show the mode choice by residential location. Due to some problems related to Nairobi dataset, transport modes included in Table 4 only considered the original travel mode. From Table 4, most residents commuting by walk, chapas and car originate from zones 3 and 4. For Nairobi, from Table 5 we can see that a substantial proportion of people commuting by walk and driving originate from zones 2 and 3.
In this subsection, we present the results of logistic regressions. Overall, statistical indicators show that the models in Tables 7-8 are
Table 4 Mode choice by zone in Maputo.
Table 5 Mode choice by zone in Nairobi. Zones
Mode
Bus Car Chapas Motorcycle Others Rail Walk
Total
N % N % N % N % N % N % N % N %
Total
Zone 1
Zone 2
Zone 3
Zone 4
13 2.2% 252 15.6% 134 1.6% 0 0.0% 29 4.4% 0 0.0% 283 1.9% 711 2.8%
14 2.4% 241 14.9% 243 3.0% 3 11.5% 43 6.5% 0 0.0% 469 3.2% 1013 3.9%
298 51.0% 612 37.8% 4350 53.1% 8 30.8% 267 40.3% 9 19.1% 6881 47.2% 12,425 48.3%
259 44.3% 512 31.7% 3468 42.3% 15 57.7% 323 48.8% 38 80.9% 6952 47.7% 11,567 45.0%
584 100.0% 1617 100.0% 8195 100.0% 26 100.0% 662 100.0% 47 100.0% 14,585 100.0% 25,716 100.0%
Zones
Mode
Bus Car Matatus Motorcycle Others Rail Walk
Total
N % N % N % N % N % N % N % N %
Total
Zone 1
Zone 2
Zone 3
Zone 4
254 12.6% 102 4.7% 1059 16.8% 62 6.8% 40 6.2% 0 0.0% 4371 22.6% 5888 18.7%
171 8.5% 107 4.9% 548 8.7% 45 5.0% 56 8.7% 0 0.0% 1978 10.2% 2905 9.2%
1592 78.7% 1979 90.4% 4672 74.3% 797 87.7% 546 85.0% 58 100.0% 12,923 66.7% 22,567 71.7%
6 0.3% 1 0.0% 11 0.2% 5 0.6% 0 0.0% 0 0.0% 100 0.5% 123 0.4%
2023 100.0% 2189 100.0% 6290 100.0% 909 100.0% 642 100.0% 58 100.0% 19,372 100.0% 31,483 100.0%
A. Tembe et al. / IATSS Research 43 (2019) 122–130 Table 6 Modeling variables. Transportation mode Characteristics of the Trip maker Age Gender (Female) Worker Motorcycle Owner Car Owner Residence Location
Income (Kenyan shillings)
Table 8 Regression results for Maputo. 1 = Bus; 0 = Paratransit (chapas and matatus) Age is years 1 = Female; 0 = Male 1 = Yes; 0 = No 1 = Yes; 0 = No 1 = Yes; 0 = No Distance between zones and City center 1 = 0–5 km 2 = 5–10 km 3 = 10–20 km 4 = 20 km or more Income levels (Kshs) 1 = Kshs 4999 less 2 = Kshs 5000–14,999 3 = Kshs 15,000–29,999 4 = Kshs 30,000–49,999 5 = Kshs 50,000 over
relatively appropriate and a good fit with the Pseudo R-square at 66% and 41%, while the p-value (the exact level of significance level) is almost zero. Nonetheless, [25] argues that the Pseudo R-Square value is not a measure of the proportion of the variance in the regressand explained by the regressors included in the model. Therefore, the Pseudo R-Square value should be carefully considered. Most of the variables included in the models are statistically significant with their p-values being practically zero. Table 7 shows the likelihood of choosing bus over matatus (paratransit) in Nairobi City. From Table 7, we observe that the following variables are significant when choosing buses: • • • •
age, income, vehicle ownership, and; residential location.
Table 8 shows the odds of selecting buses over chapas (paratransit) in Maputo Metropolitan Area. The choice of buses depends on different variables, namely: • • • •
age, employment status (worker), vehicle ownership, and; residence location
A positive coefficient suggests increased odds of an alternative over the other, whereas a negative coefficient implies the opposite. From Table 7 we observe that income, vehicle ownership, and residence location decrease the probability of selecting buses over matatus, holding other variables constant. The negative relationships between the choice Table 7 Regression results for Nairobi. Variables
Coefficient
P-value
Intercept Age 6+ Gender (Female) Income Motorcyle Owner Car Owner Residence Location
3.12 0.02 −0.01 −0.17 −2.39 −2.97 −0.01
0.00⁎⁎⁎, ⁎⁎, ⁎ 0.00⁎⁎⁎ 0.92 0.00⁎⁎⁎ 0.00⁎⁎⁎ 0.00⁎⁎⁎ 0.00⁎⁎⁎
Number of observations: 15932. Log likelihood: −3191.09 (7 df). Chi-square p-value: 0.00. Pseudo R-Square: 0.66. ⁎⁎⁎ 0.001. ⁎⁎ 0.01. ⁎ 0.1.
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Variables
Coefficient
P-value
Intercept Age 6+ Gender (Female) Worker Motorcycle Owner Car Owner Residence Location
1.56 −0.04 −0.05 −0.85 −0.26 −0.91 0.01
0.00⁎⁎⁎, ⁎⁎, ⁎ 0.00⁎⁎⁎ 0.11 0.00⁎⁎⁎ 0.08 . 0.00⁎⁎⁎ 0.00⁎⁎⁎
Number of observations: 25717. Log likelihood: −10,695.73 (7 df). Null/Residual deviance difference: 3239.88 (6 df). Chi-square p-value: 0.00. Pseudo R-Square: 0.41. ⁎⁎⁎ 0.001. ⁎⁎ 0.01. ⁎ 0.1.
of buses and the two explanatory variables (income and vehicle ownership) appear to be consistent with travel demand theories [3,4]. As it would be expected, motorcycles and private cars constitute alternative transport options. This situation is more critical when public transport is not adequate. As the income increases buses become less popular as a transportation mode. This might indicate affluent individuals either commute by matatus or private transport. Likewise, the likelihood of using public transport is influenced by increases in distance. This result is likely to be influenced by the attributes of the area covered by the person trip survey. City areas in Sub-Saharan Africa tend to have a relatively denser bus network compared to outside zones. As a result, the service frequency of public transport is higher in the city areas than outside zones. Given that paratransit tends to have a higher service frequency than buses, they are more likely to attract more users than buses at city area level. This result is consistent with findings in other African cities. For instance, according to [2], access to public transport in South Africa is reduced by long walking distances and long waiting times due to inadequate service and route coverage. In contrast, gender (female) has no effect on the likelihood of choosing buses over matatus. This might indicate that men and women are equally likely to use buses. Table 8 exhibits similar results with respect to Maputo. Age, employment status, and vehicle ownership also decrease the odds of selecting buses over chapas, holding other variables constant. Unlike Nairobi, the likelihood of using public buses increases with residential location. This might result from the difference on the area conducted person trip survey in both cities. Most commuters residing outside Maputo City make fewer transfers when using buses compared to chapas. Despite having less frequent services compared to chapas buses have longer routes allowing travellers to reduce their transportation costs. Second, buses are less popular among workers probably due to their poor service frequency. Table 9 summarizes the similarities and variations among factors affecting the likelihood of using buses. The likelihood of using bus decreases with vehicle ownership, and employment status in the two cities. Second, while age decreases the likelihood of choosing buses and residence location increase that in Maputo, the opposite is observed Table 9 Comparison. Attribute
Age 6+ Gender (Female) Worker Income Motorcycle Owner Car Owner Residence Location
Significant walk coefficient relative to public transport Maputo
Nairobi
− − − n/a − − +
+ − n/a − − − −
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in the case of Nairobi City. It is true that difference in the area conducted person trip survey might have affected these results. However, it should be noted that these cities share some similarities in terms of land use patterns. Generally, population density decreases from the city center to suburbs (zones 3 and 4). Second, employment density is higher in the central areas compared to suburbs (e.g. zones 3 and 4). As a result, most people have to commute from suburbs to central areas using either buses or paratransit. In the case of Maputo, commuters residing in suburbs make fewer transfers when using buses compared to chapas. This is because buses tend to have longer routes than chapas allowing travellers to reduce their transportation costs. Overall, these results confirm the findings of previous studies in African cities [15,24,26]. 7. Conclusion This paper investigated the demand for public buses in two SubSaharan African cities: Maputo and Nairobi. Person trip data provided by JICA (Japan International Cooperation Agency) were used to estimate regression models. Although the difference on the area covered by person trip survey in both cities might have affected the results of this study, this comparison can help us to understand the factors affecting the use of buses, as well as the merits and weakness of the policy options adopted by these cities. The study estimated two logistic regressions. Results show both similarities and variations among factors affecting the likelihood of bus use. The likelihood of using buses decreases with vehicle ownership, income, and employment status in the two cities. In addition, gender (female) has no effect on the likelihood of choosing buses over paratransit in both cities. This might indicate that men and women are equally likely to use buses. While age decreases the likelihood of choosing buses and residence location increase that in Maputo, the opposite is observed in the case of Nairobi City. Second, the factors affecting the use of buses can be summarized as follows: age, employment status, income, vehicle ownership, and residential location. The main implication of these findings is that there is a need to share the roles of public transportation between buses and paratransit. Finally, it should be noted that this study did not consider the attributes of the transport alternatives when selecting transport options. As it was discussed in the reviewed literature mode choice is highly influenced by its attributes. Acknowledgements The authors would like to thank the Japan International Cooperation Agency for providing Person trip dataset. Without this information, this research would not have been conducted.
References [1] R. Iles, Public Transport in Developing Countries, Elsevier, 2005. [2] M.A.M. Mashiri, P.N. Moeketsi, V. Baloyi, Increasing public transport market share in South Africa, The Options: International Conference Series on Competition and Ownership in Land Passenger Transport, Cape Town, 2010. [3] M.E. Ben-Akiva, S.R. Lerman, Discrete Choice Analysis: Theory and Application to Travel Demand, MIT Press, 1985. [4] T.A. Domencich, D. McFadden, Urban Travel Demand: A Behavioral Analysis, NorthHolland, 1975. [5] J. De, D. Ortúzar, L.G. Willumsen, Modelling Transport, John Wiley & Sons, 2011. [6] E.A. Vasconcellos, The demand for cars in developing countries, Transp. Res. Part A 31 (1997) 245–258. [7] M.J. Kutzbach, Motorization in developing countries: Causes, consequences, and effectiveness of policy options, J. Urban Econ. 65 (2009) 154–166. [8] D.S. Santoso, M. Yajima, K. Sakamoto, H. Kubota, Opportunities and strategies for increasing bus ridership in rural Japan: A case study of Hidaka City, Transp. Policy 24 (2012) 320–329. [9] H. Sakai, K. Shoji, The effect of governmental subsidies and the contractual model on the publicly-owned bus sector in Japan, Res. Transp. Econ. 29 (1) (Jan. 2010) 60–71. [10] P. Chacha, H. Bwire, Analysis of factors affecting school children travel mode choice in Dar es Salaam, 32nd Annual Southern African Transport Conference, South Africa, 2013. [11] M. Matsuyuki, D. Rinchumphu, Study on transportation behavior of middle-class households with children in Bangkok, Thailand, Proc. Eastern Asia Soc. Transp. Stud. (2017) 13. [12] A.T.M. Oakil, D. Manting, H. Nijland, Determinants of car ownership among young households in the Netherlands: The role of urbanization and demographic and economic characteristics, J. Transp. Geogr. 51 (2016) 229–235. [13] M. Sohail, D.A.C. Maunder, D.W.J. Miles, Managing public transport in developing countries: Stakeholder perspectives in Dar es Salaam and Faisalabad, Int. J. Transp. Manag. 2 (2004) 149–160. [14] B. Finn, Market role and regulation of extensive urban minibus services as large bus service capacity is restored – Case studies from Ghana, Georgia, and Kazakhstan, Res. Transp. Econ. 22 (2008) 118–125. [15] L. Diaz Olvera, D. Plat, P. Pochet, The puzzle of mobility and access to the city in SubSaharan Africa, J. Transp. Geogr. 32 (2013) 56–64. [16] X. Godard, Comparisons of urban transport sustainability: Lessons from West and North Africa, Res. Transp. Econ. 40 (2013) 96–103. [17] Urban Structure Plans for the Municipalities of Maputo and Matola2008 Maputo. [18] A. Kumar, F. Barret, Africa Infrastructure Country Diagnostic: Stuck in Traffic, Urban Transport in Africa, World Bank and SSATP, Africa, 2008. [19] S. Matos, Planeamento de um sistema de transportes colectivos de passageiros numa zona metropolitana O caso da Província de Maputo, Ph.D. Thesis Universidade de Porto, Portugal, 2003. [20] UITP, Overview of public transport in Sub-Saharan Africa, Trans-Africa Consortium, 2008. [21] H.T. Dimitriou, R. Gakenheimer, Urban Transport in the Developing World: A Handbook of Policy and Practice, Edward Elgar Publishing, 2011. [22] K. Sanui, K. Mutahi, The Project on Integrated Urban Master Plan for the City of Nairobi in the Republic of Kenya, 2014 (Final report). [23] J. Howe, D. Bryceson, Poverty and Urban Transport in East Africa: Review of Research and Dutch Donor Experience, 2010. [24] D. Salon, E.M. Aligula, Urban travel in Nairobi, Kenya: analysis, insights, and opportunities, J. Transp. Geogr. 22 (2012) 65–76. [25] D.N. Gujarati, Econometrics by Example: Hound Mills, Basingstoke, Palgrave Macmillan, New York, 2011. [26] T. Salau, Public transportation in metropolitan Lagos, Nigeria: analysis of public transport users' socioeconomic characteristics, Urban, Plann. Transp. Res. 3 (1) (2015) 132–139.
Contents lists available at ScienceDirect
IATSS Research
Original research paper
The demand for public buses in sub-Saharan African cities: Case studies from Maputo and Nairobi Atanasio Tembe ⁎, Fumihiko Nakamura, Shinji Tanaka, Ryo Ariyoshi, Shino Miura Graduate School of Urban Innovation, Yokohama National University, 79-5 Tokiwadai, Hodogaya-ku, Yokohama 240-8501, Japan)
a r t i c l e
i n f o
Article history: Received 13 July 2018 Received in revised form 23 October 2018 Accepted 30 October 2018 Available online 11 November 2018 Keywords: Person Trip data Chapas Matatus Sub-Saharan Africa Logistic regression
a b s t r a c t Despite the limited transportation options, public buses have been proved unpopular in many Sub-Saharan African cities. While the declining use of local buses in the developed world is often associated with increasing number of transport alternatives, relatively little is known about African cities. A substantial number of studies are focused on how to replace buses with the rapid transportation modes and less diagnosis is applied to the factors affecting the use of buses. Using household information survey, this paper attempts to investigate the factors affecting the demand for buses in Maputo and Nairobi by employing logistic regression approach. Results show both similarities and variations among the factors affecting the likelihood of using buses. For example, the likelihood of using buses decreases with vehicle ownership and employment status in the two cities. In addition, gender (female) has no effect on the likelihood of choosing buses over paratransit in both cities. Second, while age decreases the likelihood of choosing buses and residence location increases that in Maputo, the opposite is observed in the case of Nairobi City. The main implication of these findings is that there is a need to share the roles of public transportation between buses and paratransit. © 2018 International Association of Traffic and Safety Sciences. Production and hosting by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
1. Introduction Conventional buses face a continued service deterioration in SubSaharan African cities despite the rapidly growing demand for public transportation. According to [1], the sources of the declining urban transport services in developing countries include the high concentration of urban population, economic activities and motorization or increase in car ownership in major cities, the limited supply side and weak regulatory institutions. Different cities have adopted varying approaches to tackle this problem; for example, Maputo managed to maintain bus operations by subsidizing the bus company Transportes Publicos de Maputo (TPM), while in the case of Nairobi Kenyan Bus Services (KBS) was privatized. Despite these measures, service declining seems relentless considering the modal share of buses. Currently, a significant number of the existing studies in developing countries are more focused on how to replace the conventional bus systems with the rapid transportation modes mainly Bus Rapid Transit (BRT), and less diagnosis is devoted to investigating the factors contributing to declining modal share of local buses. An understanding of these
factors is critical at least for two reasons: first, many cities in the developing world are unable to replace the existing bus systems. Secondly, even in cities where rapid transportation modes have been established, conventional buses can still play a major role as feeder modes. In a study on the market share of public transport in South Africa, [2] argue that access to public transport is reduced by long walking distances and long waiting times due to inadequate service and route coverage, as well as poorly arranged schedules. As case studies, Maputo and Nairobi are selected primarily because the two cities share similarities and variations in terms of public transportation and socioeconomic characteristics. The two cities initially relied on publicly-owned bus companies to provide most of the public transport services, which were later largely replaced by paratransit services. While Maputo still subsidizes the public bus company, in Nairobi it has been split into two competitive bus companies. Second, both cities have a young and rapidly growing urban population. As such, they face more increasing challenges to service the demand side. This comparison can help us to understand the factors affecting the use of buses, and to assess the policy options adopted in these two cities. 1.1. Objectives
⁎ Corresponding author. E-mail address: [email protected] (A. Tembe). Peer review under responsibility of International Association of Traffic and Safety Sciences.
• To identify the similarities and variations on the demand for public buses between Maputo City and Nairobi City; and • To investigate the factors affecting the use of public buses.
https://doi.org/10.1016/j.iatssr.2018.10.003 0386-1112/© 2018 International Association of Traffic and Safety Sciences. Production and hosting by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http:// creativecommons.org/licenses/by-nc-nd/4.0/).
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1.2. Structure The paper is organized as follows. In Section 2, previous studies are reviewed. Section 3 describes the evolution of bus transport in the selected cities. A detailed analysis of the demand side is provided from Section 4 to Section 6. Section 7 is devoted to model estimation and discussion of results. Finally, the conclusion is presented in Section 8. 2. Factors affecting bus service demand and supply In this section, we review the literature on the travel behavior to understand the factors influencing the use of buses. According to [3–5], transport mode choice is influenced by the characteristics of the decision maker or individual, the number and attributes of alternatives, and the decision rule. Under travel choice theories a decision maker is assumed to rank possible alternatives in order of his or her preference and to choose from the available alternatives the option which he or she considers the most desirable considering his or her preferences and the relevant constraints placed on decision-making. The decision maker can be an individual or a group of people. Individuals not only face different choice situations, they also have varying tastes [3]. Any transport alternative is assumed to comprise a set of options that are feasible and known during the decision process by the decision maker. The attributes of alternatives are measured on a scale of attractiveness such as speed, travel time or travel costs. Lastly, the choice of transportation mode follows some rules including dominance and satisfaction. Despite the appealing theoretical aspects of choice theories, their applicability in Sub-Saharan African context should raise concerns. The main reason is that most of the assumptions do not hold. For example, decision makers usually do not have complete information relative to the attributes of the transportation modes. As noted by [6], most people in developing countries have no choice than to use public transportation. Several studies have analyzed the relationships between buses and private transport. [7] associated the rise in car use with the decline of bus services in developing countries. Although the empirical evidence at the country level may suggest a strong relationship between the rising income and car use at the urban level, local characteristics, traffic congestion, and policies do affect travel decisions [7]. A similar result is reported by [8], who indicates that use of local buses is increasingly affected by reliance on private transport. Public bus services in Japanese cities are less profitable compared to private operations. The main reason is that there are restrictions imposed upon public bus operators to continue providing services on unprofitable routes [9]. One of the most informative literature on the travel demand in the context of developing countries is the publication on public transportation in developing countries by [1]. The supply of public transport in developing cities is determined by various demographical, environmental, institutional, and economic factors. The author states that population density, distribution, and growth are three important factors determining the demand for transport. In terms of modeling approaches, [10] argue that logit models are by far the most widely applied discrete choice models due to the fact that the formula for the choice probabilities takes a closed form and is readily interpretable. Other authors including [11,12] used logistic regression to model the travel behavior in developing cities. In this paper, factors affecting the use of buses were investigated using a logistic regression as well. We selected this approach because the model can be easily estimated. The amount of research on bus transport in Sub-Saharan African cities is minimal. As a result, relatively little is known about the factors influencing the demand and supply sides. A study on the public transportation in Dar es Salaam by [13] concluded that public services have deteriorated rapidly due to problems such as an aged and obsolete fleet, poor maintenance and a shortage of foreign exchange to purchase spare parts. Similarly, the decline in terms of quantity and quality of bus services in Ghana is associated with the deterioration of the vehicle fleet and the inability to expand with the rapid population growth [14].
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A study on mobility in 6 Sub-Saharan African cities by [15] revealed a relatively small public transport sector concentrated on the major radial roads, and an increasing informal sector or paratransit. In addition, household ownership of motorized two-wheelers and the private car is reported to be very low. A comparison of urban transport systems across some African cites by [16] identified the affordability as the main concern for the use of buses. A substantial share of urban dwellers was found to have no daily access to public transport services because these are expensive and not easily accessible. Bus service supply can be expressed in terms of capacity, i.e. frequency, headway, fleet size, subsidy availability, number of routes, coverage, and vehicle size. Bus service supply is mostly influenced by institutional and policy factors. Bus service demand is often expressed in terms of urban structure, economic indicators, and demographic indicators. These are for example the density of population and economic activities, geographical distribution of income, economic growth and car ownership. From the literature review we can, therefore, conclude that the factors affecting transport mode choice are as follows: Characteristics of the trip maker • • • • • •
Car ownership Possession of a driving license Household composition Income Residential density Other decisions Characteristics of the trip
• Trip purpose • Time of the day • Individual or group trip Characteristics of transportation mode • • • • •
Total travel time: walking and waiting times, in-vehicle, and transfers Monetary costs Parking costs Reliability of services Comfort, convenience, and safety standards
3. Bus supply: evolution of bus transport It is important to review the foundations upon which urban transportation systems are built on to better understand the declining urban transport services both in Maputo and Nairobi City. First, we analyze how urban transportation services evolved in Maputo followed by Nairobi. 3.1. Maputo Mozambique has been an independent country since 1975. Urban transportation systems were nationalized at the outset, yielding to a regulated public transport system. Urban transportation services were exclusively provided by a state-owned bus company, Transportes Urbanos de Maputo (TPM). Fares were regulated with a purpose to ensure affordability of most population. At the beginning of the 1980s, Maputo witnessed an increased immigration of people from rural areas to the capital city [17]. Meanwhile, the bus company experienced difficulties with providing bus services due to economic policies. For example, the bus utilization ratio which is the proportion of buses in services over the total bus fleet reduced from approximately 37% in 1975 to roughly 18% as of 2015. Nominal buses stand for the bus fleet owned by the company while operating buses represent the effective number of buses in service. Because of these problems, the number of passengers gradually declined from 60 million users in 1975 to 10 million or 17% in 2015 (Fig. 1). As reported by [18], at the beginning public bus companies
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Fig. 1. Bus services (based on Maputo City data).
were able to operate without subsidies in Africa, however, as deficits grew and subsidies did not grow commensurately, bus operators faced enormous difficulties either to maintain or to replace bus fleet. According to [19], in order to meet the increasing demand in urban areas, individuals initiated the paratransit services and gradually their number increased. These paratransit operators were formally acknowledged at the end of the 1980s when the government gave them permission to operate urban transportation services. This yielded to the development of chapas industry (Fig. 2). The designation chapas is due to the flat fare that was charged. The seating-capacity of chapas ranges from 15 to 25 passengers. By the beginning of the 1990s, paratransit operators had a significant share of the market. As of 2013, the urban transport modal share is dominated by walk (45%), followed by chapas (32.9%), private car (10.2%), bus (9.2%), rail (0.6%) and others (1.3%) (Tables 1 and 2). 3.2. Nairobi According to [20], urban transport services in Nairobi were exclusively provided by Kenyan Bus Services (KBS) until 1973. Kenya Bus Services was established in 1934 and it operated urban transport services utilizing high capacity buses (double-decker). Bus fares were controlled at the level that allowed the company to operate profitably and to expand. KBS also encountered difficulties due to regulatory institutions which resulted in the declining market share. To meet the growing needs for transportation, Kenyan authorities legalized the paratransit matatus in 1973 [18]. However, [20] state that matatus are reported to have started their transport services in the 1950s and they were considered as illegal commercial operators. Like chapas, the seating-capacity of matatus varies from 14 to 25 passengers (Fig. 3).
3.3. Comparison According to [18], matatus were mainly used as a transportation mode of native Africans during the colonial period. The word matatu means 30 cents which was the standard flat fare that was charged. Despite the legalization of matatus, Kenyan Bus Services retained the monopoly status which was only broken at the onset of the 1980s with the formation of another state bus company Nyayo Bus services. Similar to Maputo, the beginning of the 1990s witnessed an increasing transport modal share in favor of matatus. The current public transportation (Fig. 4) is a complex paratransit system [21]. Despite its demand flexibility, competitiveness, and spatial range the authors argue that the paratransit system can never be sustained as the primary transport for the metropolitan area because the quantity of service supplied is completely disconnected from the capacity and quality of the infrastructure. This is because matatus with seating capacity ranging from 14-to-25 passengers make use of road space less efficiently than buses with a minimum seating capacity of 50 passengers. The current road infrastructure barely accommodates the current number of matatus. As the city is growing more rapidly (city expansion and increases of urban population) to rely all demand on matatus seems to be technically impossible when considering corridors with the higher population density. However, if the budget is available and city authorities can strongly control density, matatus may become the primary transportation. The modal share has experienced some variations between 2003 and 2013. As of 2013, the market share is primarily by walk (39.7%), followed by matatus (28.4%), private car (13.5%), bus (12.2%), rail (0.2%) and others (5.9%).
Fig. 2. Public buses (left-hand side) and chapas (right-hand side) in Maputo. (Source:https://www.google.com/Transportes Urbanos de Maputo)
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4. Bus demand: urban structure data
Table 1 Characteristics of the cities. Maputo Metropolitan Area Regulated to Subsidies Difficulties
Nairobi City
Ensure Affordability 1 subsidized 1980s – reduction in fleet size Legal paratransit 1980s Paratransit/bus fares Flat: $0.12 Present bus mode Bus: Paratransit 9%:33% share Vehicle Size Bus: Paratransit Normal: Minibus/Mid-bus Fleet Size Bus: Paratransit Chapas 280:4150 Bus: Paratransit 65:130 Route Coverage/number of routes
Operate profitably and expand 2 competing privately owned 1980s – increase in operational costs 1973 $0.50–$0.99 Bus: Paratransit 12%:28% Bus: Paratransit Normal/Double Decker: Minibus/Mid-bus Bus: Paratransit Matatus 23,000:72000 n/a
In this section, we describe the geographic features of the cities; for example, the area, employment, and population. For a better understanding, we divided the cities into comparable zones. Zone 1 encompasses the Central Business Area (CBA), zone 2 is the built-up area while zone 3 includes the suburban area. Lastly, zone 4 comprises the rest of the metropolitan area. 4.1. Maputo Maputo, the capital city of Mozambique is the economic and political center of Mozambique. It is situated in the southern region of the country. The city (Fig. 5) was established as the main urban center following the development of transport infrastructures such as road and railway connecting Mozambique and South Africa. Maputo is still growing rapidly and expanding outwards. It is here where Maputo port, one of the major ports in southern Africa is located. 4.2. Nairobi
Table 2 Summary description of zones. Maputo Metropolitan Area
Nairobi City
Area Population Established Port
1228 sqm 2.3 million (2007 Census) 1884 Yes
4784 sqm 5.0 million (2009 Census) 1907 No
Zone 1 CBD Area Population Population Density Employment Density
8 sqm 45,734 5614 1.7
11 sqm 274,607 24,964 n/a
Zone 2 Build Up Area Population Population Density Employment Density
10 sqm 76,863 7971 2.1
12 sqm 261,855 21,821 n/a
Zone 3 Sub-Urban Area Population Population Density Employment Density
605 sqm 1 million 960 0.3
555 sqm 2 million 4688 n/a
Zone 4 Metropolitan Area Population Population Density Employment Density
605 sqm 1 million 960 0.2
4206 sqm 2 million 446 n/a
According to [22], Nairobi (Fig. 5) is one of the most important economic centers in East and Central Africa. It accounts for 50% of formal employment in Kenya and generates over 50% of Gross Domestic Product. Nairobi plays an important role, not only as the political center but also as a model for economic development. In line with [21], Nairobi was established as the colonial capital of British East Africa. It was initially a way station between the Port of Mombasa on the Indian Ocean and Uganda. From the initial settlement of the city, the British imposed a clear and distinct spatial segregation between European and African residents. 4.3. Comparison 4.3.1. Bus demand: demographic data 4.3.1.1. Maputo and Nairobi. The household information survey (HIS) used in this paper was conducted in 2012 and 2013 in Maputo and Nairobi, respectively, by the Japan International Cooperation Agency (JICA). The sample dataset for two cities are is described in Table 3. It should be noted that: • Person-trip data only covered the Nairobi City while in the case of Maputo the whole metropolitan area was surveyed. One of the implication is that we are not able to judge accurately if there is a difference in the travel behavior between residents of these two cities. This is mostly because city areas in Sub-Saharan Africa tend to have a relatively denser bus network compared to outside zones. As a result,
Fig. 3. Number of trips (1000 trips) by mode between 2004 and 2013. (Source: HIS).
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Fig. 4. Matatus (left-hand side) Public buses (right-hand side) in Nairobi. (Source: https://www.google.co.jp/kenyan bus services)
the service frequency of public transport is higher in the city areas than outside zones. Given that paratransit tend to have a higher service frequency than buses, they are more likely to attract more users than buses at city area level. Second, we expect a difference in the daily traffic volume attracted by each city center. Maputo metropolitan area is expected to generate more traffic volume than Nairobi City. • Lastly, considering that Maputo and Nairobi share some similarities such as lower population density in the suburbs, and high employment concentration in the central areas, commuters are likely to exhibit similarities in their travel behavior. This is because most people tend to commute from the suburbs to the city centers. • The occupation status information from Maputo was more disaggregated than that in Nairobi and we had to adjust for comparison purposes. Figs. 6-7 provide information about the age distribution and household size in the selected cities. Fig. 6 shows that approximately 47% of individuals in Nairobi and 45% in Maputo are b19 years old. The proportion of individuals aged between 20 years and 60 years old is approximately 50% in both Nairobi and Maputo. Maputo, with 5% of individuals over 60 years old has a slightly higher figure than Nairobi with 2%. Approximately 87% of households in Nairobi have b3 persons compared to 11% in Maputo (Fig. 7). Nairobi has b12% of households with at least 3 persons while the same figure amounts to 89% of households in Maputo. With respect to occupation, the combined ratio of persons with no job, housewives, and students, is at 67% in Maputo and 24%
for Nairobi. As it would be expected under this situation, the proportion of employed persons is higher in Nairobi than Maputo, 57% to 20%, respectively. 4.4. Comparison The data confirms: • The Findings of [23] who considered East African cities as being “young cities” with a substantial proportion of the population being the first generation of the urban dwellers.
Table 3 Household attributes.
Population Zone 1–3 Households Surveyed Individuals Surveyed Sample size b 19 years old 20–60 years old N 60 years old HH b 3 persons HH N 3 persons No job, housewives or students Employed
Fig. 5. Major bus routes in Maputo (left) and (right) Nairobi (source: Bing maps).
Maputo Metropolitan Area
Nairobi City
1.1 million 9530 43,927 2% 45% 50% 5% 11% 89% 67% 20%
3.1 million 9973 27,618 1% 47% 50% 2% 87% 12% 24% 57%
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Fig. 6. Age distribution in Maputo and Nairobi (Source: HIS).
Fig. 7. Household size distribution in Maputo and Nairobi (Source: HIS).
• Households surveyed in the Maputo Metropolitan Area were relatively big compared to the smaller households in Nairobi City. This may seem surprising because, in a study on urban travel behavior by [24], it was indicated that the households in Nairobi City are relatively large with an average of 3.5 or 4 people. However, [24] also noted that many Kenyans often move out of Nairobi City when they retire, and considering that the survey only covered Nairobi City and not the metropolitan area, the findings can, therefore, be substantiated. • Unemployment in Maputo Metropolitan Area is significantly higher than in Nairobi City.
5. Bus demand: mode choice data 5.1. Relative to time of day Fig. 8 provides some key insights to understand the temporal distribution of trips in both cities. Unlike the commonly two peak-hour periods found in Nairobi City, the temporal distribution of trips in Maputo is characterized by three peak-hour periods, namely, at 6 a.m., around 12:00, and 5 p.m., respectively. On the word of [23], the increasing importance of informal sector activities as a source of income in the
Fig. 8. Mode choice by the time of day in Maputo and Nairobi (Source: HIS).
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Fig. 9. Mode choice by trip purpose in Maputo and Nairobi (Source: HIS).
developing cities is changing the nature of travel behavior. They state that even though commuting peaks remain, they are overlaid in both space and time by more complex irregular movements associated with trading, hawking, and employment-seeking. However, to better understand differences in trip patterns we investigate trip purpose in the next sub-section. 5.2. Relative to trip purpose Fig. 9 displays the temporal trip distribution by trip purpose. The morning peak-hour periods in both cities mostly comprise work and school trips, whereas the evening peaks are dominated by home trips. As it was noted, a day peak is observed in Maputo and it is constituted by trips to school and to work. Due to an increased number of children in school age and limited supply capacity, the Government introduced in the 1980s 3 distinct school periods; a substantial proportion of students have classes from 7:30 a.m. to 12:30, the second and third periods from 12:30 to 17:30 and 17:30 to 22:30 respectively. 5.3. Relative to zone
In the case of Maputo, chapas constitute the most popular transport mode. Part of the explanation for the popularity of paratransit is because the service area of buses is constrained by the limited road coverage in Maputo (0.24 km per 1000 people) and Nairobi (0.22 km per 1000 people). In the analysis of mobility in 6 African cities [15] and conclude that public transport services in these cities are concentrated on the major paved radial roads. 6. Model estimation and discussion The literature reviewed suggests some associations between transportation mode and household/individual characteristics. To analyze these relationships, we have estimated two logistic regression models. The variables in the models were selected in accordance with the literature review. Only individuals commuting by paratransit and public transportation were considered in the analysis. Table 6 shows the variables that are included in the logistic regression models. The following assumptions are made: • The dependent variable is the transport mode – public buses and paratransit mode (chapas or matatus). • Explanatory variables are individual/household attributes – gender, age, employment status, vehicle ownership, income, and residential location.
Tables 4 and 5 show the mode choice by residential location. Due to some problems related to Nairobi dataset, transport modes included in Table 4 only considered the original travel mode. From Table 4, most residents commuting by walk, chapas and car originate from zones 3 and 4. For Nairobi, from Table 5 we can see that a substantial proportion of people commuting by walk and driving originate from zones 2 and 3.
In this subsection, we present the results of logistic regressions. Overall, statistical indicators show that the models in Tables 7-8 are
Table 4 Mode choice by zone in Maputo.
Table 5 Mode choice by zone in Nairobi. Zones
Mode
Bus Car Chapas Motorcycle Others Rail Walk
Total
N % N % N % N % N % N % N % N %
Total
Zone 1
Zone 2
Zone 3
Zone 4
13 2.2% 252 15.6% 134 1.6% 0 0.0% 29 4.4% 0 0.0% 283 1.9% 711 2.8%
14 2.4% 241 14.9% 243 3.0% 3 11.5% 43 6.5% 0 0.0% 469 3.2% 1013 3.9%
298 51.0% 612 37.8% 4350 53.1% 8 30.8% 267 40.3% 9 19.1% 6881 47.2% 12,425 48.3%
259 44.3% 512 31.7% 3468 42.3% 15 57.7% 323 48.8% 38 80.9% 6952 47.7% 11,567 45.0%
584 100.0% 1617 100.0% 8195 100.0% 26 100.0% 662 100.0% 47 100.0% 14,585 100.0% 25,716 100.0%
Zones
Mode
Bus Car Matatus Motorcycle Others Rail Walk
Total
N % N % N % N % N % N % N % N %
Total
Zone 1
Zone 2
Zone 3
Zone 4
254 12.6% 102 4.7% 1059 16.8% 62 6.8% 40 6.2% 0 0.0% 4371 22.6% 5888 18.7%
171 8.5% 107 4.9% 548 8.7% 45 5.0% 56 8.7% 0 0.0% 1978 10.2% 2905 9.2%
1592 78.7% 1979 90.4% 4672 74.3% 797 87.7% 546 85.0% 58 100.0% 12,923 66.7% 22,567 71.7%
6 0.3% 1 0.0% 11 0.2% 5 0.6% 0 0.0% 0 0.0% 100 0.5% 123 0.4%
2023 100.0% 2189 100.0% 6290 100.0% 909 100.0% 642 100.0% 58 100.0% 19,372 100.0% 31,483 100.0%
A. Tembe et al. / IATSS Research 43 (2019) 122–130 Table 6 Modeling variables. Transportation mode Characteristics of the Trip maker Age Gender (Female) Worker Motorcycle Owner Car Owner Residence Location
Income (Kenyan shillings)
Table 8 Regression results for Maputo. 1 = Bus; 0 = Paratransit (chapas and matatus) Age is years 1 = Female; 0 = Male 1 = Yes; 0 = No 1 = Yes; 0 = No 1 = Yes; 0 = No Distance between zones and City center 1 = 0–5 km 2 = 5–10 km 3 = 10–20 km 4 = 20 km or more Income levels (Kshs) 1 = Kshs 4999 less 2 = Kshs 5000–14,999 3 = Kshs 15,000–29,999 4 = Kshs 30,000–49,999 5 = Kshs 50,000 over
relatively appropriate and a good fit with the Pseudo R-square at 66% and 41%, while the p-value (the exact level of significance level) is almost zero. Nonetheless, [25] argues that the Pseudo R-Square value is not a measure of the proportion of the variance in the regressand explained by the regressors included in the model. Therefore, the Pseudo R-Square value should be carefully considered. Most of the variables included in the models are statistically significant with their p-values being practically zero. Table 7 shows the likelihood of choosing bus over matatus (paratransit) in Nairobi City. From Table 7, we observe that the following variables are significant when choosing buses: • • • •
age, income, vehicle ownership, and; residential location.
Table 8 shows the odds of selecting buses over chapas (paratransit) in Maputo Metropolitan Area. The choice of buses depends on different variables, namely: • • • •
age, employment status (worker), vehicle ownership, and; residence location
A positive coefficient suggests increased odds of an alternative over the other, whereas a negative coefficient implies the opposite. From Table 7 we observe that income, vehicle ownership, and residence location decrease the probability of selecting buses over matatus, holding other variables constant. The negative relationships between the choice Table 7 Regression results for Nairobi. Variables
Coefficient
P-value
Intercept Age 6+ Gender (Female) Income Motorcyle Owner Car Owner Residence Location
3.12 0.02 −0.01 −0.17 −2.39 −2.97 −0.01
0.00⁎⁎⁎, ⁎⁎, ⁎ 0.00⁎⁎⁎ 0.92 0.00⁎⁎⁎ 0.00⁎⁎⁎ 0.00⁎⁎⁎ 0.00⁎⁎⁎
Number of observations: 15932. Log likelihood: −3191.09 (7 df). Chi-square p-value: 0.00. Pseudo R-Square: 0.66. ⁎⁎⁎ 0.001. ⁎⁎ 0.01. ⁎ 0.1.
129
Variables
Coefficient
P-value
Intercept Age 6+ Gender (Female) Worker Motorcycle Owner Car Owner Residence Location
1.56 −0.04 −0.05 −0.85 −0.26 −0.91 0.01
0.00⁎⁎⁎, ⁎⁎, ⁎ 0.00⁎⁎⁎ 0.11 0.00⁎⁎⁎ 0.08 . 0.00⁎⁎⁎ 0.00⁎⁎⁎
Number of observations: 25717. Log likelihood: −10,695.73 (7 df). Null/Residual deviance difference: 3239.88 (6 df). Chi-square p-value: 0.00. Pseudo R-Square: 0.41. ⁎⁎⁎ 0.001. ⁎⁎ 0.01. ⁎ 0.1.
of buses and the two explanatory variables (income and vehicle ownership) appear to be consistent with travel demand theories [3,4]. As it would be expected, motorcycles and private cars constitute alternative transport options. This situation is more critical when public transport is not adequate. As the income increases buses become less popular as a transportation mode. This might indicate affluent individuals either commute by matatus or private transport. Likewise, the likelihood of using public transport is influenced by increases in distance. This result is likely to be influenced by the attributes of the area covered by the person trip survey. City areas in Sub-Saharan Africa tend to have a relatively denser bus network compared to outside zones. As a result, the service frequency of public transport is higher in the city areas than outside zones. Given that paratransit tends to have a higher service frequency than buses, they are more likely to attract more users than buses at city area level. This result is consistent with findings in other African cities. For instance, according to [2], access to public transport in South Africa is reduced by long walking distances and long waiting times due to inadequate service and route coverage. In contrast, gender (female) has no effect on the likelihood of choosing buses over matatus. This might indicate that men and women are equally likely to use buses. Table 8 exhibits similar results with respect to Maputo. Age, employment status, and vehicle ownership also decrease the odds of selecting buses over chapas, holding other variables constant. Unlike Nairobi, the likelihood of using public buses increases with residential location. This might result from the difference on the area conducted person trip survey in both cities. Most commuters residing outside Maputo City make fewer transfers when using buses compared to chapas. Despite having less frequent services compared to chapas buses have longer routes allowing travellers to reduce their transportation costs. Second, buses are less popular among workers probably due to their poor service frequency. Table 9 summarizes the similarities and variations among factors affecting the likelihood of using buses. The likelihood of using bus decreases with vehicle ownership, and employment status in the two cities. Second, while age decreases the likelihood of choosing buses and residence location increase that in Maputo, the opposite is observed Table 9 Comparison. Attribute
Age 6+ Gender (Female) Worker Income Motorcycle Owner Car Owner Residence Location
Significant walk coefficient relative to public transport Maputo
Nairobi
− − − n/a − − +
+ − n/a − − − −
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in the case of Nairobi City. It is true that difference in the area conducted person trip survey might have affected these results. However, it should be noted that these cities share some similarities in terms of land use patterns. Generally, population density decreases from the city center to suburbs (zones 3 and 4). Second, employment density is higher in the central areas compared to suburbs (e.g. zones 3 and 4). As a result, most people have to commute from suburbs to central areas using either buses or paratransit. In the case of Maputo, commuters residing in suburbs make fewer transfers when using buses compared to chapas. This is because buses tend to have longer routes than chapas allowing travellers to reduce their transportation costs. Overall, these results confirm the findings of previous studies in African cities [15,24,26]. 7. Conclusion This paper investigated the demand for public buses in two SubSaharan African cities: Maputo and Nairobi. Person trip data provided by JICA (Japan International Cooperation Agency) were used to estimate regression models. Although the difference on the area covered by person trip survey in both cities might have affected the results of this study, this comparison can help us to understand the factors affecting the use of buses, as well as the merits and weakness of the policy options adopted by these cities. The study estimated two logistic regressions. Results show both similarities and variations among factors affecting the likelihood of bus use. The likelihood of using buses decreases with vehicle ownership, income, and employment status in the two cities. In addition, gender (female) has no effect on the likelihood of choosing buses over paratransit in both cities. This might indicate that men and women are equally likely to use buses. While age decreases the likelihood of choosing buses and residence location increase that in Maputo, the opposite is observed in the case of Nairobi City. Second, the factors affecting the use of buses can be summarized as follows: age, employment status, income, vehicle ownership, and residential location. The main implication of these findings is that there is a need to share the roles of public transportation between buses and paratransit. Finally, it should be noted that this study did not consider the attributes of the transport alternatives when selecting transport options. As it was discussed in the reviewed literature mode choice is highly influenced by its attributes. Acknowledgements The authors would like to thank the Japan International Cooperation Agency for providing Person trip dataset. Without this information, this research would not have been conducted.
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