# Comparing Urban Transportation Services in São Paulo, Brazil. A Decision Making Model Using AHP

Gabriel Santos Rodrigues, João Reis, Sivanilza Teixeira Machado

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Gabriel Santos Rodrigues, João Reis, Sivanilza Teixeira Machado. Comparing Urban Transportation Services in São Paulo, Brazil. A Decision Making Model Using AHP. IFIP International Conference on Advances in Production Management Systems (APMS), Sep 2022, Gyeongju, South Korea. pp.593-600, 10.1007/978-3-031-16407-1_69 . hal-05386138

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# Comparing Urban Transportation Services in São Paulo, Brazil. A Decision Making Model Using AHP

Gabriel Santos Rodrigues<fnref n="1" />(✉), João Gilberto Mendes dos Reis<fnref n="1" />(✉), and Sivanilza Teixeira Machado<fnref n="2" />(✉)

**Abstract.** Metropolitan Area of São Paulo is the main Brazilian urban region, where approximately 21 million people generate 42 million trips daily being 54% by Public Transportation. This paper aims to investigate how transport attributes affect the way of commuters use Public Transportation. To do so, a model using the AHP (Analytic Hierarchy Process) a multi-criteria decision was established. We used six criteria: Environment; Safety; Cost; Accessibility; Confort and Speed and five alternatives: Bus, Metropolitan Train, Subway, Ridesharing, and Bike-sharing. The results showed an advantage for Bike-sharing (34.9%), Metropolitan Train (23.5%), Subway (23%), Bus (10.4%), and finally Ridesharing (8%).

**Keywords:** Passenger transport · AHP · Multi criteria decision making and urban mobility

## 1 Introduction

Metropolitan Area of São Paulo is the main Brazilian urban region, comprising 39 cities where approximately 21 million people live and accounts for about 18% of the country's GDP [1,2]. According to Companhia do Metropolitano de São Paulo - METRÔ in 2017 42 million trips are generated daily, the main reasons are work 44%, education 35%, and personal matters 7% [1]. Regarding the mode, they are conveyed by motorized means 67% and 33% by non-motorized modes.

Among the motorized modes, in the MASP, car trips, as the main mode, are the ones with the highest volume in 2017 (40.1%) and motorcycles (3.8%) [1]. Individual transport uses a large amount of public space in large and medium-sized cities [3], which creates congestion. In addition, this type of transport is responsible for about 80% of the costs of traffic accidents [3]. Thus, it is

<footnote n="1">¹ RESUP - Research Group in Supply Chain Management - Postgraduate Program in Production Engineering, Universidade Paulista, São Paulo, Brazil
biel.rodrigues@outlook.com</footnote>

<footnote n="2">² Instituto Federal de Educação, Ciência e Tecnologia de São Paulo - Câmpus Suzano, Suzano, Brazil</footnote>
necessary to have urban planning that involves the use of public transport and non-motorized methods of transport such as walking and cycling.

For cities, the choice of collective public transport systems is a complex task with numerous variables [3]. Schmidt [4] states that with a dense transport network it is possible to reach destinations in different ways, reducing travel times. Scott [5] states that to improve the acceptance of public transport, it is necessary to understand what makes passengers choose this mode.

According to Cruz [6] active mobility can be a positive strategy to encourage healthier behaviors, raise awareness about the use of urban spaces and discourage the indiscriminate use of the car, favoring efficient and sustainable transport. For Costa et al. [7] the bicycle can allow greater accessibility to the public transport system.

Bicycles are responsible for 377 thousand daily trips in the MASP and the reasons that lead to their use are: the short distance to be covered (50.9%), physical activity (18%), and the high cost of the transport fee (15.9%). The majority (72%) do not have segregated infrastructure (cycle lanes and/or cycle lanes) during travel [1]. People walk mainly because of the short distances (88.6%), the high cost of transport fares (5%) and physical activity (2%) [1].

The objective of this article is to identify and measure the decision factors that determine the choice of urban transport mode. For this, a decision model is adopted using the Analytic Hierarchy Process. The criteria considered in the model are Environment, Safety, Cost, Accessibility, Comfort and Speed and the available systems are: Bus, Metro, Metropolitan Train, Ridesharing and Bicycle.

## 2 Analytic Hierarchy Process

Analytic Hierarchy Process (AHP) is a multi-criteria decision method developed by Professor Thomas Saaty based on the hierarchy of the importance degree of a criteria over the others that is applied when a decision needs to be made [8].

In AHP, the decision problem must be divided into hierarchical levels to facilitate understanding and evaluation. In this evaluation, the use of qualitative and quantitative criteria in the structuring of the problem is allowed [8]. For Vieira [9] the AHP is built by three principles: construction of hierarchies, when the hierarchy of complex processes is created; prioritization and logical consistency.

Afterwards, it is necessary to define which criteria is more important than the other based on the opinion of decision-makers [10], making a parity comparison - two by two - of one criteria over the other in the observation of the specialists, in this case, the authors, and this determines the relative importance between them [11]. This process is shown through the following mathematical equation also known as the judgment matrix:

$$A = \begin{bmatrix} 1 & a_{1,2} & a_{1,3} & \cdots & a_{1,n} \\ a_{2,1} & 1 & a_{2,3} & \cdots & a_{2,n} \\ a_{3,1} & a_{3,2} & 1 & \cdots & a_{3,n} \\ \vdots & \vdots & \vdots & \ddots & \vdots \\ a_{n,1} & a_{n,2} & a_{n,3} & \cdots & 1 \end{bmatrix} \quad (1)$$
To define these preferences, the verbal form may be easier to measure than numerical values. For this reason, Saaty also created an intensity scale that correlates the verbal judgments with the number of judgments that must be filled in the judgment matrix. This scale is in the Table 1.

<table><caption>**Table 1.** Parity comparison scales</caption><thead><tr><td>Scale</td><td>Meaning</td></tr></thead><tbody><tr><td>1</td><td>Equally important</td></tr><tr><td>3</td><td>Moderate importance</td></tr><tr><td>5</td><td>Strong importance</td></tr><tr><td>7</td><td>Very strong importance</td></tr><tr><td>9</td><td>Extremely important</td></tr><tr><td>2–4–6–8</td><td>Intermediate values</td></tr></tbody></table>

Peinaldo [12], and Lucena e Mori [13] point out there must be the principle of reciprocity when performing a comparison when using this scale, that is, When criteria A is much more important than B on scale 5, criteria B must be 5 times less important than the criteria an i.e. 1/5. To facilitate the understanding of this scale and methodology. Lucena e Mori [13] adapted Saaty's original table to a more didactic form, as can be seen below.

After that, where the weight of each element is decided in the decision-making model, the software superdecisions was used. The software calculates the alternatives and checks whether the mathematical model is consistent or inconsistent. When the model shows inconsistency and there is a need for some adjustment, it goes back to the previous step and defines a new value in the decision matrix. To be consistent the model must have a CR (Consistency Ratio) that must be < 0.1.

To calculate the Consistency Ratio (CR) this number that determines if a model is consistent or inconsistent, the first step is to calculate the Consistency Index (CI) (Eq. 2).

$$CI = \frac{(\lambda max - n)}{(n - 1)} \qquad (2)$$

where the $\lambda$ max is the biggest eigenvalue of the pairwise comparison. After calculating the CI, it is possible to calculate the CR, this number is obtained by dividing the CI by the RI (Eq. 4). The Random Index (RI) and the RI is obtained in the table developed by Saaty in 1991 (Table 2).

$$CR = \frac{CI}{RI} \qquad (3)$$
<table><caption>**Table 2. Random index**</caption><thead><tr><th>n</th><th>1</th><th>2</th><th>3</th><th>4</th><th>5</th><th>6</th><th>7</th><th>8</th><th>9</th><th>10</th></tr></thead><tbody><tr><th>RI</th><td>0.00</td><td>0.00</td><td>0.58</td><td>0.90</td><td>1.12</td><td>1.24</td><td>1.32</td><td>1.41</td><td>1.45</td><td>1.49</td></tr></tbody></table>

## 2.1 Methodology

The decision tree, Fig. 1, is composed of the following attributes: Decision problem that is the highest degree of complexity and is at the highest level, the criteria that are the factors that influence the decision defined by the decision-maker and the alternatives that are the actions possible to be taken to solve the decision problem [10].

<figure><img src="image_3.png" /><figcaption>**Fig. 1. Decision tree**</figcaption></figure>

For this study, the weights were given by the authors themselves according to data available in the literature for the following six criteria:

- **Environment:** Which transport mode has the lowest environmental impact per user.

- **Safety:** Which transport mode has the lowest risk of accidents in its use.

- **Cost:** Which transport mode has the lowest tariff cost for the user.

- **Accessibility:** The ease of the user finding access to use the transport system.

- **Confort:** What is the capacity of users in the transport.

- **Speed:** Based on commercial vehicle speed.

The alternatives are: Bus, Metropolitan Train, Subway, Ridesharing and Bike-sharing.
For the Environment criteria, we defined priorities through data obtained by Carvalho [14] and made the following relationship: Emission of the transport/Ridesharing system, which is the most polluting transport mode.

The mode of transport that emits the most gases is Ridesharing (0.1268), Bus (0.016), Metropolitan Train and Subway (0.0035), and Bike-sharing is the last one because it does not emit gases.

For the Safety criteria, we defined priorities through accident risk data obtained by the TPC Guide [3], which evaluated all modalities as Non-existent to Subway, Low to Train, high to Buses and Ridesharing, and very high to Bike-sharing.

The fare cost Criteria was evaluated by the tariffs in effect for daily plans in June 2021 for Metro, Bus, Metropolitan Train, and Bike-sharing. For ridesharing, an average daily rate was used. The most expensive is the Ridesharing we used the Basic fare to 10km (R$ 63.65 or 100%) [15], the Bus, Subway and Metropolitan train we used the daily rate (R$ 16.80 or 26.30%) [16] and Bike-sharing we used the daily rate (R$ 1.00 or 0.03%) [17].

For the accessibility criteria, we used the coverage criterion, being that for Bus and Ridesharing we use the MASP area coverage which is 7,944 Km² [2], for Metropolitan Train the network of lines (273 km - June 2021) [18], for subway the network of lines (101 Km - June 2021) [19] and Bike-sharing the area with bikes available (684 Km²).

For the comfort criteria, the passenger comfort scale in public transport was agreed, which is 6 pass/m² as a base and it was created through a qualitative scale criteria where the number of passengers = 1 to 2 great, 3 to 4 very good, 5 to 6 good/ideal, 7 to 8 bad, 8 or + very bad.

After that, we researched the average number of passengers for the system and classified them by scale. The numbers used were for buses (7.65) [20], Metropolitan train (8.1) [21], Metro (7.1) [22], Ridesharing (1.4) [23], and Bike-sharing (1.0).

For the Speed criteria, we used the following parameters: for bus we used the average of bus services (21,5 km/h), train (50 km/h), and metro (35 km/h) speeds, avaliable of the TPC Guide [3], for ridesharing speeds, the official average speed data of the city of São Paulo (18,84 km/h) [24] and bike-sharing we used the average speed of an experienced cyclist (17,5 km/h) [25].

# 3 Results and Discussion

Our results is presented in Table 3.

In (Table 3), we can observe a preference for Bike-sharing (34.9%). The main motives is that they are cheaper and cleaner, do not generate emissions. However, they are not suitable for long distances and are subject to the weather.

Costa [7] recognize that bicycles are used mainly for commutes of 1.5 km and 4.5 km. For short distances, walking is preferred by commuters due to the time it takes to park the bikes while for far distances the physical effort does not favor the use of bicycles.
<table><caption>Table 3. Results</caption><thead><tr><td>Name</td><td>Normalized by cluster</td><td>Limiting</td></tr></thead><tbody><tr><td>Bike-sharing</td><td>0.34927</td><td>0.261953</td></tr><tr><td>Ridesharing</td><td>0.07996</td><td>0.059971</td></tr><tr><td>Subway</td><td>0.23054</td><td>0.172908</td></tr><tr><td>Metropolitan train</td><td>0.23563</td><td>0.176721</td></tr><tr><td>Bus</td><td>0.10460</td><td>0.078447</td></tr></tbody></table>

Barriola [26] carried out in Mexico a study and suggested that cities should invest in ridesharing systems to improve the population's quality of life and reduce carbon emissions. To the ITDP (Institute for Transportation and Development Policy) [27] encourage the use of bicycles and also improve the ease of walking around the city. In addition to the environmental benefits, it increases the access of poor people to economic activities.

In second place is Metropolitan Train (23.5%) and third one Subway (23%). In São Paulo the Subway and Metropolitan Train are integrated in a network, what differs is the interval (90 s to the subway and 6 to 10 min to train) and the distance between the stations (1 km to subway and 2.5 km to train, in average). The train is faster than subway and its network is bigger.

Moraes [28] says that Subway and Metropolitan Train are important interventions that reduce congestion by increasing the average speed of travel, which, despite having a high cost of implementation, brings social benefits to the population.

Buses with 10.4% also produce social benefits. A bus network can greatly improve with the implementation of corridors and BRTs and a lower implementation cost than the Subway and Metropolitan Train [28]. Diesel-powered buses, even emitting polluting gases, are responsible for only 13% of greenhouse gas emissions in São Paulo [29].

Finally, Ridesharing (7.99%) can not be considered a solution in displacements because it has a low passenger capacity and is polluting in addition to the average traffic speed in São Paulo being 18.84 km/h. Martins et al. [30] claim that Ridesharing affects urban transport, on the one hand, it helps people mobility at night and in low-demand region. However, for Silva and Balassiano [31], ridesharing also competes with the public transport system but has the potential to remove cars from circulation, especially in places with low transport supply and difficulty in parking private cars.

## 4 Conclusions

The importance of the social benefits of collective public transport is relevant for the city. In addition to bicycles that are sustainable, they can insert more people into economic activities and improve their quality of life. The high-capacity transport systems occupying the following areas indicate their importance Metro
and Train, as they are non-polluting and faster, can make public transport more attractive than individual transport. Even buses pollute less than individual transport.

However, the metro-railway transport modes have a relatively lower expansion capacity due to their complexity, so transport by bus, which can preferably be inserted into priority systems such as exclusive lanes, corridors and BRTs, the latter still very incipient at MASP is also important. to enhance and encourage public transport modes.

Ridesharing has its good side of removing private vehicles from circulation and its bad side of impacting and removing passengers from public transport and the researchers still do not have a consensus on the effect of this.

In this way, it is suggested that in works the impacts of carpooling in public transport and how to facilitate the integration of the bicycle in transport are carried out.

**Acknowledgments.** This study was financed in part by the Coordenação de Apresentação de Pessoal de Nível Superior - Brasil (CAPES). Finance Code 001.

## References

1. METRÔ: Pesquisa Origem e Destino 2017 50 Anos - A mobilidade da Região Metropolitana de São Paulo em detalhes (2019)

2. EMPLASA: Plano de Desenvolvimento Urbano Integrado - RMSP. Tech. rep., São Paulo (2019). http://multimidia.pdui.sp.gov.br/rbsp/docs.pdui/rbsp docs.pdui_0018 diagnostico final.pdf

3. Amicci, A.G.N., et al.: Guia TPC: orientações para seleção de tecnologias e implementação de projetos de transporte público coletivo (2018)

4. Schmidt, M., Schöbel, A.: The complexity of integrating routing decisions in public transportation models (2010)

5. Scott, R.A., George, B.T., Prybutok, V.R.: A public transportation decision-making model within a metropolitan area. Decis. Sci. **46**, 1048–1072 (2016)

6. Cruz, F.J.A.d.O.: A prática da mobilidade ativa na perspectiva do estudante universitário (2019)

7. Costa, T. B.; Siqueira, M.F.L.R.C.L.A.: Integração bicicleta-transporte público no contexto de grandes cidades brasileiras (2019)

8. Quadros, C.E.P.d., Adamatti, D.F., Longaray, A.A.: O Processo de Análise Hierárquica (AHP) e a teoria das Inteligências Múltiplas (MI): uma revisão de literatura com meta-síntese sobre a relação entre o método e a teoria 8 (2021)

9. Vieira, G.H.: Análise e Comparação dos Métodos de Decisão Multicritério AHP Clássico e Multiplicativo. Ph.D. thesis, Instituto Tecnológico de Aeronáutica (ITA), São José dos Campos (2006)

10. Wolff, S.C.: O método AHP: revisão conceitual e proposta de simplificação. Ph.D. thesis, Rio de Janeiro, Brazil (2008)

11. Alves, J., Alves, J.: Definição de localidade para instalação industrial com o apoio do método de análise hierárquica (AHP), pp. 13–26 (2015)

12. Peinado, H.: Método AHP para tomada de decisão multicritério. No. 103 in Coleção Fundamentum, EDUEM, Maringá (2016)
13. Lucena, A.F.E., Mori, L.M.D.: Uso do analytic hierarchy process (AHP) para Hierarquização de Métodos de Mensuração do Grau de Aplicação da Construção Enxuta. Revista Gestão Industrial, 14(4) (2018)

14. Carvalho, C.H.: Emissões Relativas de Poluentes do Transporte Motorizado de Passageiros nos Grandes Centros Urbanos Brasileiros (2011)

15. Uber: Baixe e solicite uma viagem com o app da Uber (2021). https://www.uber.com/br/pt-br/ride/

16. SPTrans: Aviso ao publico 001-21. Tech. Rep. 001/21, São Paulo (2021)

17. Bici, T.: Aluguel de bicicleta em São Paulo: praticidade e segurança (2021). https://bikeitau.com.br/sao-paulo/

18. CPTM: a-companhia—CPTM (2021). https://www.cptm.sp.gov.br/a-companhia/Pages/a-companhia.aspx

19. Metrô: Relatório Integrado 2020. Tech. rep., São Paulo (2021)

20. SPTrans: Relatório de Administração 2018. Tech. rep. (2018)

21. Barbosa, B.: Alívio na lotação de Metrô e CPTM deve ficar para 2017 (2016). https://www.mobilize.org.br/noticias/9120/alivio-na-lotacao-de-metro-e-cptm-deve-ficar-para-2017.html

22. G1: Linha 3-Vermelha do Metrô é a mais lotada de passageiros em SP no período da manhã — São Paulo—G1 (2019). https://g1.globo.com/

23. Voitch, G.: São Paulo tem média de 1,4 ocupante por carro - Jornal O Globo (2011). https://oglobo.globo.com/brasil/sao-paulo-tem-media-de-14-ocupante-por-carro-2695421

24. CET: Mobilidade no Sistema Viário Principal Volumes e Velocidades 2019. Tech. rep. (2019)

25. Sevilha, A.: Entenda como são os níveis de pedal — Lobi Cicloturismo e Aventura (2018). https://www.lobi.com.br/entenda-como-sao-os-niveis-de-pedal/, https://www.lobi.com.br/entenda-como-sao-os-niveis-de-pedal/, section: andar de bicicleta

26. Barriola, X.: The impact of gasoline shortages on public bike-sharing systems. Academia Revista Latinoamericana de Administración 34(4), 561-577 (Oct 2021). https://www.emerald.com/insight/content/doi/10.1108/ARLA-01-2021-0017/full/html

27. ITDP: Active Mobility. https://www.itdp.org/key-issues/health-safety/active-mobility/

28. Moraes, A.C.d.: Congestionamento urbano: custos sociais (2013)

29. CETESB: Emissões Veiculares no Estado de São Paulo - 2018. Tech. rep., São Paulo (2018)

30. Martins, D.: Análise sobre o impacto dos aplicativos de ridesourcing nas ações dos planos de mobilidade urbana (2019)

31. Silva, M., Balassiano, R.: Uber - Uma análise do serviço oferecido ao usuário na cidade do Rio de Janeiro (2018)