# Fatal Traffic Accidents in São Paulo State, Brazil: Social Characteristics and Social Distance Effects

Sivanilza Teixeira Machado, João Reis

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Sivanilza Teixeira Machado, João Reis. Fatal Traffic Accidents in São Paulo State, Brazil: Social Characteristics and Social Distance Effects. IFIP International Conference on Advances in Production Management Systems (APMS), Sep 2022, Gyeongju, South Korea. pp.574-581, 10.1007/978-3-031-16407-1_67. hal-05386137

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# Fatal Traffic Accidents in São Paulo State,
Brazil: Social Characteristics and Social
Distance Effects

Sivanilza Teixeira Machado¹,²✉ and João Gilberto Mendes dos Reis¹,³✉

¹ BS in Civil Engineering (in Progress) - Universidade Paulista - UNIP, 1212 Doutor Bacelar Street, São Paulo, SP 04026002, Brazil

sivanilzamachado@ifsp.edu.br, napole.ifsp@gmail.com, joao.reis@docente.unip.br

² NAPOLE - Research Group, Federal Institute of São Paulo – IFSP, 1501 Mogi das Cruzes Avenue, Suzano, SP 08673010, Brazil

³ RESUP – Supply Chain Research Group, Postgraduate Studies in Production Engineering/Postgraduate Studies in Business Administration, Universidade Paulista - UNIP, 1212 Doutor. Bacelar Street, São Paulo, SP 04026002, Brazil

**Abstract.** Every day someone become a victims of road traffic accidents. A traffic accident is a social, economic and health public problem that affects all society. However, the comprehension of its main causes is still a work in process. The influence of traffic volume can be suggested based on the impact of COVID-19 crisis, since that the fatal traffic accident in São Paulo city in 2020 dropped almost 1/3 in relation to 2019. Thus, the objective of this paper is to analyze the number of fatal traffic victims [FTV] in São Paulo State among January 2015 and September 2021. To do so, we used a São Paulo State database over fatal traffic accidents and applied the statistical analysis to provide graphical and cross tabulation results. Our results indicate that São Paulo Metropolitan Region (RMSP) is responsible for 1/3 of the FTV occurred in São Paulo State, and there was a decrease in the number of fatal traffic victims due to public policies applications and social distance caused by COVID-19. Furthermore, it identifies social characteristics that contribute to unsafety traffic such as gender (male), life stage (adult), vehicle (motorcycle), period of the day (night) and days of the week (Saturday and Sunday). We conclude that this research provides information necessary to auxiliary decision-makers to develop urban transportation plans to reduce fatal traffic accidents.

**Keywords:** Driver behaviour · Risk factors · Road accidents · Urban mobility

## 1 Introduction

Every year around 1.3 million people die worldwide victims of road traffic accidents which represent 3% of Countries' GDP [1]. Due to this problem, many governments start to implement urban mobility plans to reduce the mortality accident index. In Brazil is not different, the traffic national code established that drivers caught conducting a vehicle
under alcohol influence is judged as a criminal with the clear intention of committing murder. However, we can find other more lenient initiatives inside the country. São Paulo city government, for instance, implemented a reduction of speedy limit in roads and highways, improved traffic signalling, and created bicycle lanes [2].

A traffic accident is a social, economic and health public problem that affects all society. However, the comprehension of its main causes is still a work in process. Some authors suggest that human bad behaviour, lack of road infrastructure, lack of traffic control and monitorization are among the main causes that contribute to fatal traffic accidents [1, 3, 4, 5]. Souza and Oliveira Neto showed a strong correlation between road congestion caused by traffic accidents and the response time in the release of the road (highway) after the traffic accident [4]. In 2010, 43.8 thousand Brazilian died in traffic accidents (22.5 per 100 thousand inhabitants) [2].

The influence of traffic volume can be suggested based on the impact of COVID19 crisis. In 2020, the fatal traffic accident in São Paulo city dropped 29.5% in relation to 2019 [6]. These statistics represent a benefit of the social isolation and restriction law adopted by the local government. However, this association was not tested, and the fact is thousands of people lose their lives every year in traffic accidents.

With these ideas in mind, we highlighted two research questions to be verified in this study: RQ1 – Is the Pandemic Crisis of COVID19 the only one responsible for accidents reduction in São Paulo State, Brazil? RQ2 – Is it possible to find out the main social characteristics that influence the number of fatal traffic accidents in São Paulo State, Brazil?

São Paulo state concentrates more than 40 million people – around 20% of the Brazilian population – where its capital of the same name São Paulo has approximately 12 million inhabitants and add the 39 cities of its metropolitan area have around 50% of the state total population. Therefore, this study is not important only for Brazil but can be a comparison model for many countries around the world that have an equal population or big metropolitan areas.

To answer the research questions the objective of this paper is to analyze the number of fatal traffic victims [FTV] in São Paulo State among January 2015 and September 2021 (last data available) using statistical tools. To do so, we used a São Paulo State database over fatal traffic accidents and Microsoft Excel®, R software and QGIS to organize them and provide the statistical and graphical results.

This paper was structured in four sections: the first part contains the introduction, where the problematic and aims were presented; second, we informed what methodology and analysis data were performed; third, we showed the finds and discussion about FTV; and finally, we present our conclusion of the study.

## 2 Methodology

As mentioned previously, we analyzed the number of fatal victims' occurrences in transit of the São Paulo State from 2015 up to September 2021. Data was obtained from São Paulo State Government, and it was composed of 31,663 fatal victim occurrences. In the database, we dropped the occurrences without geographic points resulting in a final number of 29,781.
The input data is the year, month, geographic points, gender, age, day of the week, times of the day, city, accident site, model of vehicle, class of victims, death site and kind of the accident, Table 1.

<table><caption>**Table 1.** Description of the variables</caption><thead><tr><th>Variables</th><th>Category</th><th>Description/Terms definition</th></tr></thead><tbody><tr><td>Year</td><td>2015–2021</td><td></td></tr><tr><td>Month</td><td>Jan–dec</td><td></td></tr><tr><td>Geographic points</td><td>Latitude; longitude</td><td></td></tr><tr><td>Gender</td><td>Male; female</td><td></td></tr><tr><td>Age</td><td>Baby; child; adolescent; young adult, adult, and elderly</td><td>Baby = 0 ~ 2 years old;<br>Child = 3 ~ 11 years old;<br>Adolescent = 12 ~ 18 years old;<br>Young Adult = 19 ~ 24 years old<br>Adult = 25 ~ 60 years old<br>Elderly = above 60 years old</td></tr><tr><td>Day of the week</td><td>Monday ~ Sunday</td><td>7 days of the week</td></tr><tr><td>Times of the day</td><td>Morning ~ midnight</td><td>Morning = 06:00 ~ 11:59 a.m<br>Afternoon = 12:00 ~ 05:59 p.m<br>Evening = 06:00 ~ 11:59 p.m<br>Dawn = 00:00 ~ 5:59 a.m</td></tr><tr><td>City</td><td>Adamantina ~ Zacarias</td><td>645 cities that composed São Paulo State</td></tr><tr><td>Accident site</td><td>Municipal roads; highways</td><td></td></tr><tr><td>Model of vehicle</td><td>Bicycle; bus; car; motorcycle; on foot; other; truck</td><td></td></tr><tr><td>Class of victims</td><td>Driver; passenger; pedestrian</td><td></td></tr><tr><td>Death site</td><td>Hospital; accident site; other</td><td>Hospital = health establishment;<br>accident site = in municipal roads/highways;</td></tr><tr><td>Kind of the accident</td><td>Hit; collision; crash; other</td><td>Collision = two or more vehicles in the movement were involved<br>Crash = one vehicle in movement and other/object was stopped</td></tr></tbody></table>

Source: Based on the database from São Paulo State Government

## 2.1 Data Organization and Analysis

Firstly, we organized the data using a spreadsheet in Microsoft Excel. Secondly, we carried out descriptive statistics to analyse the numeric and categorical variables to explore the frequency distribution, proportionality (R 4.0.5 Software), and the density
mapping of the FTV occurrence in São Paulo State based on the geographic point (QGIS v 3.24 Software). After, to answer the RQ1, the graphic was applied to determine the decrease value of FTV through simple regression analysis using of R 4.0.5 Software.

In addition, to answer the RQ2, we investigated the association among categorical variables to identify the social characteristics on FTV applying the Chi-squared (χ²), Eq. 1 [7].

$$ \chi^2 = \sum_{i=1}^{I} \sum_{j=1}^{J} \frac{(O_{ij} - E_{ij})^2}{E_{ij}} \qquad (1) $$

where, I = number of variable categories of X; J = number of variable categories of Y; Oij = number of observations (i = categories of variable X; j = categories of variable Y); Eij = expected frequency of observations (i = categories of variable X; j = categories of variable Y). In this analysis, we applied the 95% confidence interval.

# 3 Results and Discussion

## 3.1 Fatal Traffic Accident Reduction and Social Distance

Our results indicated that one third of fatal traffic accidents in the State of São Paulo occurred in the São Paulo Metropolitan Region (RMSP), area highlighted in Fig. 1. The RMSP is comprised of 39 municipalities, where live approximately 22 million inhabitants [8]. Traffic accident on the world represent the 1.35 million deaths per year, and the Africa and South-East Asia region presented higher deaths rate than global [1].

<figure><img src="image_3.png" /><figcaption>**Fig. 1.** Distribution and geographical visualization of the FTV in São Paulo State Source: Research results from QGIS Software</figcaption></figure>

Fatal traffic victims' rates in the State of São Paulo have been decreasing since 2015. They dropped more than 10% in 2020 suggesting a relation between traffic volume
and fatal accidents, Table 2. The rate of social isolation in São Paulo city, for instance, reached 48%, which represented almost of the 6 million inhabitants out of traffic [9]. Therefore, our study indicates that Pandemic Crisis influenced the drop numbers rather than security measures. This result requires further studies after the end of the emergency of COVID19 to be conclusive.

<table><caption>**Table 2.** Description statistical of number of deaths in transit by year</caption><thead><tr><td>Year</td><td>n</td><td>VAR (%)</td><td>Average</td><td>SD</td><td>Maximum</td><td>Minimum</td></tr></thead><tbody><tr><td>2015</td><td>5.054</td><td>—</td><td>421.17</td><td>87.52</td><td>508</td><td>162</td></tr><tr><td>2016</td><td>5.032</td><td>-0.44</td><td>419.33</td><td>34.89</td><td>481</td><td>364</td></tr><tr><td>2017</td><td>4.612</td><td>-8.35</td><td>384.33</td><td>48.45</td><td>490</td><td>314</td></tr><tr><td>2018</td><td>4.256</td><td>-7.72</td><td>354.67</td><td>33.71</td><td>404</td><td>290</td></tr><tr><td>2019</td><td>4.462</td><td>4.84</td><td>371.83</td><td>41.62</td><td>428</td><td>281</td></tr><tr><td>2020</td><td>3.840</td><td>-13.94</td><td>320.00</td><td>28.95</td><td>362</td><td>264</td></tr><tr><td>2021*</td><td>2.525</td><td>-34.24</td><td>280.56</td><td>36.74</td><td>342</td><td>230</td></tr></tbody></table>

n = number of de FTV per year; VAR % = variation of FTV per year (in percentage); SD = standard deviation; *up to September/2021.

The decrease value of FTV per year during period analysis can be better visualized in Fig. 2, which presents the statistically significant linear model (F-statistics = 62.03; p < 0.001), and the parameters α and β (p-value < 0.001).

<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Decrease value of FTV per year Source: Adapted from R Software</figcaption></figure>
Despite the data being related to São Paulo State, São Paulo city has a great contri-
bution to this scenario, due to the government plan to reduce the speed limit of roads in
2011. According to Sarno's study, in the second semester of 2016, São Paulo city showed
a drop of 44,5% in the rate of FTV [5]. This argument is supported by World Health
Organization [WHO] which reported that the death risk rises 4.5 times for pedestrians
hit when car velocity increases from 50 to 65 km/h [1]. These observations allow us to
consider that COVID19 is not the only reason to drop numbers. Perhaps it is responsible
for the sharp decay but not for the total reduction.

Public policies to make traffic safer also contributes to the urban mobility plan,
encouraging walking and cycling on urban roads, and reducing the number of vehicles.
Many people report that they do not use the bicycle as a mode of transport, because they
do not feel safe [2].

## 3.2 Social Characteristics that Influence Fatal Traffic Accidents

Our results also showed that most victims were male (81.5%), and females correspond
18.5% indicating an association between gender and fatal traffic accidents. According
to WHO, 73% of road traffic death is related the road user's young male under 25 years
old [1] which allows us to characterize the driver profile involved in road accidents.
Moreover, we obtained a significant association between gender and period of the day
($\chi^2$ = 104.25; p < 0,001), however, surprisingly we did not find the same results between
gender and days of the week ($\chi^2$ = 9,827.0; p = 0.1321).

Analysing the period of the day with the highest incidence of FTV, it was observed
that about 36.5% occur in the evening, followed by 23.5% in the afternoon, 20.5% at
dawn and 19.5% in the morning ($\chi^2$ = 702.8; p < 0,001). In addition, we observed that
most accidents occur on weekends 39.5%, 14.5% on Fridays and the other days of the
week around 11.5% each. Both scenarios indicate an association among youths, leisure
and traffic imprudence. According to Ferreira et al., the young drivers liked to test the car
capacity and velocity, and the speed limit is one the traffic legislative criteria not relevant
to them [3]. This behaviour is against what WHO indicated as seven behavioural risk
factor on traffic that require legislative criteria such as speed, drink-driving, motorcycle
helmets, seat-belts, child restraints, drug-driving and mobile phones [1].

Investigating the existence of an association between the gender of the victims and
the life stage (Fig. 3), we noted that approximate 61% of the cases occur by people in
the adult stage, following elderly (17%) and young adults (15.5%). Also, the class of
victims and life stage ($\chi^2$ = 4,285; p < 0,05), we highlighted that almost 39% were
related drivers in adult life, 12.5% were pedestrian in adult stage too, and 10.6% were
drivers in the young adult stage. WHO reported that the main fatal victim of road traffic
injuries is children and young adults aged 5–29 years [1].

Thus, we can infer the reasons are adults make more travels alone and are more
exposed to distractions in imprudence both as a pedestrian or a driver. Moreover, young
adult drivers usually make mistakes based on their inexperience of conducting a vehicle
or under traffic imprudence (speed limit exceed, alcohol influence, etc.) [3, 5].

Regarding the analysis of the types of vehicles involved in FTV, it was noted that
the majority are motorcycles and cars, respectively 35.8% and 26.16%. Most victims
are represented by drivers, pedestrians and passengers, Table 3. The global results from
<figure><img src="image_5.png" /></figure>

$$ \chi^2 = 673; p < 0,001 $$

<caption>**Fig. 3.** Death in transit by life stage and gender, values in percentage.</caption>

WHO corroborated our results, highlighting that road traffic death is higher for vulnerable users: pedestrians, cyclists and motorcyclists [1].

<table><caption>**Table 3.** Percentage of number of FTV by vehicle and class of victim</caption><thead><tr><th>Vehicle</th><th>Driver</th><th>Passenger</th><th>Pedestrian</th><th>na</th></tr></thead><tbody><tr><td>Car</td><td>16.42</td><td>8.30</td><td>—</td><td>1.43</td></tr><tr><td>Bicycle</td><td>5.88</td><td>0.04</td><td>—</td><td>0.09</td></tr><tr><td>Truck</td><td>2.84</td><td>0.60</td><td>—</td><td>0.08</td></tr><tr><td>Motorcycle</td><td>31.62</td><td>3.12</td><td>—</td><td>1.06</td></tr><tr><td>Bus</td><td>0.19</td><td>0.70</td><td>—</td><td>0.03</td></tr><tr><td>Other</td><td>0.36</td><td>0.08</td><td>—</td><td>0.05</td></tr><tr><td>On foot</td><td></td><td></td><td>23.99</td><td></td></tr><tr><td>na</td><td>0.35</td><td>0.04</td><td>—</td><td>2.73</td></tr><tr><td><strong>Total</strong></td><td><strong>57.66</strong></td><td><strong>12.88</strong></td><td><strong>23.99</strong></td><td><strong>5.47</strong></td></tr></tbody></table>

$$ \chi^2 = 45,838; p < 0,001; na = not available. $$

We found a significant statistical association between model of vehicles and kind of accidents ($\chi^2$ = 39,075; $p < 0,001$), collision is 1.6 higher than hit. Analysing each number, vehicles involved in a road accident is led by motorcycle (49.4%), cars (32.8%), bicycle (11%), and others. In relation to foot represent 94.6% road hit. These results reinforce the unsafe condition of São Paulo State roads. We also found a significant association between the class of victims and death site ($\chi^2$ = 1,056.5; $p < 0,001$), and 52% of victims die in hospitals, 43% on roads and the others in different sites as ambulances during transportation from road to the hospital.
# 4 Conclusions

This paper analyzed fatal traffic victims in São Paulo State to explore the reason for drop in values in accident numbers and how the accidents are related to social aspects. Our results indicate that there was a decrease in the number of fatal traffic victims due to public policies applications and social distance caused by COVID-19. Furthermore, it identifies social characteristics that contribute to unsafety traffic such as gender (male), life stage (adult), vehicle (motorcycle), period of the day (night) and days of the week (Saturday and Sunday).

The results of this research provide information necessary to auxiliary decision-makers to develop urban transportation plans to reduce fatal traffic accidents. Moreover, the numbers of São Paulo state may be used as a comparison for other states, regions, and cities worldwide.

The limitation of the paper is highlighted by lack of variables like condition of the road/highway infrastructure and the driver behaviour. However, the paper contributed to study of the traffic accident and to determine the factor risk that could be provided a basis to decision making in traffic public policies.

# References

1. World Health Organization [WHO]. https://www.who.int/publications/i/item/9789241565684

2. Municipal Department of Mobility and Traffic of São Paulo. https://www.prefeitura.sp.gov.br/cidade/secretarias/mobilidade/planmob/index.php?p=189299

3. Ferreira, P.C.P., Santos, M.P.S., Nassi, C.D.: Análise comportamental de motociclistas brasileiros no trânsito urbano. In: XXVIII Congresso de Pesquisa e Ensino em Transporte-Anpet. Curitiba; 2014, pp. 1-11 (2014)

4. Souza, V.N., Oliveira Neto, F.M.: Análise do tempo de duração de acidentes de trânsito em vias arteriais de fortaleza. In: 32 Congresso de Pesquisa e Ensino em Transporte da ANPET. Gramado: ANPET; 2018, pp. 3345-8 (2018)

5. Sarno, C.C.B.: Benefícios imediatos da redução das velocidades máximas permitidas. Nota Técnica 251, Companhia de Engenharia de Tráfego (2016)

6. Companhia de Engenharia de Tráfego. http://www.cetsp.com.br/noticias/2021/05/27/cai-num-ero-total-de-sinistros-de-transito-na-cidade-de-sao-paulo-em-2020.aspx

7. Fávero, L.P., Belfiore, P.: Manual de análise de dados. 1a. LTC, Rio de Janeiro, 2021, p. 1187 (2021)

8. Instituto Brasileiro de Geografia e Estatística [IBGE]. https://cidades.ibge.gov.br/brasil/sp

9. Governo de São Paulo. https://www.saopaulo.sp.gov.br/coronavirus/isolamento/