Transport Research Arena 2014, Paris

Characterization of the sites of traffic accidents involving vulnerable

road users (VRU) in São Paulo City

Claudia A. Soares Machadoa, Mariana A. Giannotti

a, Eduardo Jun Shinohara

b, Henrique

Nishisakia, José Alberto Quintanilha

a, 1

aEscola Politécnica da Universidade de São Paulo, São Paulo, Brazil bCompanhia de Tecnologia de Saneamento Ambiental do Estado de São Paulo, São Paulo, Brazil

Abstract

In this paper is discussed the spatial characteristics of the sites with the highest density in terms of number of accidents involving vulnerable road users (VRU - pedestrians, cyclists and motorcycle riders), and their relationship with socioeconomic aspects and the attributes of the transportation system. The database consists of the description of the road traffic accidents involving VRU during the time span 2007 - 2009 in São Paulo City, Brazil. Also, data from an Origin/Destination Home-interview Survey held in 2007 is used in order to analyse the transportation system, mainly in the areas where the amount of accidents is high. It is performed a density analysis (Kernel estimator) by using traffic accidents data, identifying the areas (traffic zones in São Paulo) where the number of traffic accidents involving VRU is higher, and to establish a relationship between VRU traffic accidents and the characteristics of the transportation system. Besides, the socioeconomic aspects of the traffic zones are taken into account in order to characterize the sites with the highest occurrences of accidents. Keywords: Traffic accidents; vulnerable road users; spatial analysis.

Résumé

Dans cet article, les auteurs discutent les caractéristiques spatiales des sites ayant la plus haute densité en termes

de nombre d’accidents des utilisateurs vulnérables de la route (VRU – piétons, cyclistes et motocyclistes), ainsi

que leur relation avec les aspects socio-économiques et les attributs du système de transport. La base de données

comprend la description des « accidents VRU », de 2007 jusqu’à 2009, à São Paulo, Brésil. En outre, les

données d’une enquête à domicile (Origine-Destination, 2007) sont utilisées pour analyser le système de

transport, surtout dans les régions où le nombre d’accidents est élevé. Afin d’identifier les zones où le nombre

des « accidents VRU » est élevé, un estimateur de la densité (noyaux) est employé. Enfin, les aspects socio-

économiques des zones de circulation sont considérés pour caractériser les sites ayant les plus hauts taux

d’accidents.

Mots-clé: Accidents de la circulation; vulnérables usagers de la route, l'analyse spatiale

1 Escola Politécnica da Universidade de São Paulo. Avenida Professor Almeida Prado travessa 2, nº 83, Cep: 05508-070, Butantã, São Paulo, Brasil. Tel.: +55 11 3091.5173. E-mail address: claudia.s.machado@uol.com.br

Machado et al./ Transport Research Arena 2014, Paris 2

1. Introduction

A modern transportation system should have three key elements: sustainability, smartness, and safety.

Sustainability aims economic efficiency, environmental justice, and social equity. In order to get the

sustainability it is needed policies for integrating land use and transportation planning, ensuring suitable

transport supply (both in terms of infrastructure and services), managing travel demand efficiently, and

incorporating environment-friendly strategies. Smartness incorporates a set of technologies to enhance the

transportation system, in order to facilitate the control, the monitoring, and the management of the information

available to the users. Regarding to safety initiatives the target is to minimize the amount of accidents and reduce

the severity of injuries for all users, including motorists and passengers of particular vehicles, public transport

commuters, cyclist, and pedestrians (HAQUE et al., 2013). The safety is the topic addressed in this paper

through the analysis of traffic accidents data.

It is well-known that one of the most troubling problems of the transportation system is the respect of accidents.

Traffic accidents impose serious problems on society, and their costs to individuals, property and society have

been quite significant (WANG et al., 2013).

World Health Organization – WHO (2004) affirms that road traffic injuries are a major but neglected global

public health problem, requiring actions for effective and sustainable prevention. According to WHO (2013) in

2010 1.24 million people were killed due to traffic accidents worldwide, and middle-income countries, which are

motorizing rapidly, are the hardest hit. The cost of dealing with the consequences of these traffic accidents runs

to billions of dollars (WHO, 2013), a large sum that could be applied to actions in the transportation system in

order to prevent traffic accidents. Half of all road traffic deaths are among pedestrians, cyclists and motorcyclists

(WHO, 2013), then is logical to affirm that they are the most vulnerable users of the transportation system. In

Brazil, in 2010, 4% of the deaths in traffic accidents were cyclists, 23% pedestrians, and 25% riders’ motorized

2- or 3- wheelers (MINISTRY OF HEALTHY, 2013).

Injuries and deaths caused by the traffic violence have traditionally been regarded as “accident”, i. e., as a

random, unpredictable, and unavoidable event. Nowadays, the expression “traffic accident” has been questioned

in order to have a better understanding of the nature of the events, that are, in most cases, largely preventable

situations, being required establish affirmative actions to reduce it by changing the behavior of transportation

system’s users, adopting programs of road safety, and offering a transportation system with high quality

(PEDEN et al., 2002; MALTA et al., 2009).

Koh & Wong (2013) discuss on how land use environment influences VRU’s (pedestrians) needs and behavior.

The authors found that more infrastructural compatibility factors were rated as important in industrial areas

compared to residential areas. The reason for choosing a transport mode (non-motorized or motorized) is

different between the land use types, for instance: number of parked bicycles at transit stations, percentage of

land for commercial use, distance between origin/destinations, and nearest bus stop with services serving the

transit station. These factors are influential variables on the propensity of getting a non-motorized transport

mode. Besides, the most significant concern with non-motorized mode is the risk of traffic accidents, as

pedestrians, cyclists and even motorcycles riders are the most vulnerable road users. Also, the risk varies with

the level and type of local traffic. The authors affirm that risk of traffic accidents is the most important concern

for VRU in urban centres.

2. Objectives

This article is a result of a project sponsored by the European Community which was developed in the period of

2009 - 2011, titled SaferBraIn, whose main goal was to increase the level of safety and security of the road

transportation system and its components in Brazil and India, focusing the attention on vulnerable road users

(VRUs), being an attempt to contribute to the overall scope of reducing the number fatalities and the severity of

injuries caused by road accidents (PERSIA et al., 2011; QUIGLEY et al., 2011; QUIGLEY et al., 2012;

TRIPODI et al., 2012).

Machado et al./ Transport Research Arena 2014, Paris

In this paper is discussed the spatial characteristics and patterns of the sites with the highest density in terms of

number of accidents involving vulnerable road users (VRUs - pedestrians, cyclists and motorcycle riders), and

their relationship with socioeconomic aspects and the attributes of the transportation system (infrastructure and

services).

The main objective is to analyze the spatial distribution of fatal accidents involving VRUs and compare

socioeconomic factors of road infrastructure in the municipality of São Paulo.

3. Study Area

The study area is São Paulo City (figure 1), capital of São Paulo State, located in South-eastern Brazil. São Paulo

has over than 11.3 million inhabitants (IBGE, 2013), and according to the United Nations Organization – UNO

(2012) São Paulo is classified as a megacity, with urbanization rate of 99.10% (in 2010), and geometric growth

of 0.59% per year (2010-2013) (SEADE, 2013).

Fig. 1. Study Area – São Paulo City – Brazil

4. Materials and Methods

4.1. Data Source

The database consists of the description of the road traffic accidents involving VRUs during the time span 2007 -

2009 in São Paulo City, Brazil. These data are from the CET-SP (Companhia de Engenharia de Tráfego de São

Paulo), the operative body of the municipality of São Paulo for the traffic management of the city, and they are

geo-referenced and bring information about: date, location, accident type (collisions and impacts, rollovers,

runoff collision, running over, etc.), number and type of vehicles involved, number and type of victims produced

(pedestrians, cyclists, motorcycle riders, etc.), and so forth. Also, data from an Origin / Destination Home-

interview Survey held in 2007 by the Companhia do Metropolitano de São Paulo (METRÔ - public company

responsible for operating the subway trains) is used in order to analyse the transportation system, mainly in the

areas where the amount of accidents is high.

4.2. Data Processing

The data processing was performed in a Geographic Information System (GIS) environment, using the software

ArcGIS Desktop 10 Service Pack 4 (ESRI Inc.) to develop the spatial analysis.

It is performed a density analysis (Kernel estimator) by using traffic accidents data, identifying the areas (traffic

zones in São Paulo) where the number of traffic accidents involving VRUs is higher, and to establish a

relationship between VRUs traffic accidents and the characteristics of the transportation system, such as number

of trips generated (attracted or produced), the relationship between public transportation and individual transport,

the relationship between number of inhabitants and the number of private vehicles, the density of trips whose

reason is work, and road network density (divided by the road category as arterials, collectors and locals

Machado et al./ Transport Research Arena 2014, Paris 4

(FHWA, 1989)). Besides, the socioeconomic aspects of the traffic zones - per capita income and number of jobs

normalized by the population - are taken into account in order to characterize the sites with the highest

occurrences of accidents.

Thus, the first step was to apply a density estimator in order to get the areas with the highest amount of accidents

occurrences, using the collection of the georeferenced points that represent the fatal traffic accidents involving

VRUs. This study adopted the Kernel estimator. Kernel calculates the probability density function of a

distribution from which a sample has been observed, by centring a probability density function around each one

of the observed points (in this study, the geographical location of the accidents involving VRUs), and then taking

the average value of all of these (BRUNSDON, 1995). According to Flahaut et al. (2003) the kernel estimator is

a non-parametric algorithm, which uses a density estimation method. This technique allows evaluating the local

probability accident occurrence, and consequently the probable dangerousness of a traffic zone.

The second step was the geocodification of the Origin - Destination home-interview survey that shows the traffic

zones with greater amounts of generated trips. It was generated density vector maps containing the amount of

generated trips (places of origin and destination), the number of jobs normalized by number of inhabitants per

traffic zone, road network, urban trips with work purpose, the relationship between public transportation and

individual transport, the relationship between number of inhabitants and the number of private vehicles; in order

to be represented on the map Moran. The Moran index, called LISA – Local Indicator of Spatial Association is a

measure of spatial dependence, i.e., spatial autocorrelation based on the notion of binary contiguity between

spatial units, in order to study spatial patterns (ANSELIN, 1995), In this study the Moran's index was used to

identify the spatial correlation structure that best describe the data by estimating the autocorrelation between

traffic zones. According to Steenberghen et al. (2004) the Moran index is a good choice to analyse traffic

accidents in a GIS environment, and this has been proven by many studies that demonstrated a more stable result

than other indices. Other advantages are the simplicity, and the flexibility of the conditional distribution. The

Moran index gives good results for the identification of accidents clusters on roads (STEENBERGHEN et al.,

2004).

4.3. Methodological Flowchart

The methodology adopted follows the flowchart presented in figure 2.

Fig. 2. Methodological Flowchart

Machado et al./ Transport Research Arena 2014, Paris

5. Results

5.1. Kernel Estimator

The result of the density analysis of the fatal accidents involving VRUs, represented by georreferenced points, is

the kernel density map, presented in the figure 3. There is a concentration of events in the central area of the

municipality (red area), which corresponds to jobs density spatial distribution (figure 4). These are the black

zones, with high density of accidents occurrences (FLAHAUT et al., 2003; STEENBERGHEN et al., 2004).

Fig. 3. Kernel Density Map – Fatal Traffic Accidents - VRUs

5.2. Maps of the transportation system attributes

In order to perform the Moran’s spatial statistics, were made some maps that describe the behavior of the

transportation system attributes. The figure 4 shows the proportion of jobs normalized by the amount of

inhabitants of each traffic zone, the figure 5 presents the relation between population and the number of

individual vehicles (in each traffic zone), and the figure 6 illustrates the amount of total urban trips in the traffic

zones.

The figure 5, which corresponds the analyses of private vehicle per capita, indicates that in the suburban areas, as

expected, there is less number of vehicles per inhabitants, and less income per capita (figure 12a). On the other

hand, figure 6, does not demonstrate a pattern in the distributions of total urban trips in the traffic zones.

Machado et al./ Transport Research Arena 2014, Paris 6

Fig. 4. Amount of jobs in

traffic zone / population of

the traffic zone

Fig. 5. Population of the

traffic zone / number of

private vehicles

Fig. 7. Density of arterial

roads – São Paulo City

Fig. 8. Density of collector

roads – São Paulo City

Fig. 9. Density of local

roads – São Paulo City

Fig. 6. Total urban trips

The road network is presented in the figures 7, 8, and 9, which respectively show the road density of arterial,

collector and local roads. Arterial and collector road densities are concentrated in the central area in a

complementary distribution of the local road density.

5.3. Moran Index

The results of the Moran analysis are presented in the follow figures. The figure 10(a) and 10(b) show,

respectively, the density map of work trips in the traffic zones and its Moran index. The figures 11(a) and 11(b)

show the relationship between public and individual transport modes, and the figures 12(a) and 12(b) the spatial

relation with per capita income.

Machado et al./ Transport Research Arena 2014, Paris

Fig. 10 (a) Work trips density Fig. 10 (b) Moran map work trips density

Fig. 11 (a) Public / Individual transport modes density Fig. 11 (b) Moran map - Public / Individual transport modes density

Machado et al./ Transport Research Arena 2014, Paris 8

According to Moran map (figure 10(b)) it is possible to affirm that there is a significant spatial correlation only

in some zones of high work trip density (figure 10(a)), in central area. The figures 11 ((a) and (b)) represents the

higher dependency of the public transportation in the suburban areas, which are low-income per capita areas

(figures 12 (a) and (b)).

6. Discussions

The spatial distribution of traffic accidents involving VRUs is concentrated in the central area of São Paulo City

– Central Business District (CBD), as can be seen in the figure 3. According to the spatial statistical analysis

performed, the jobs and the trips are concentrated in the CBD (figures 4 and 6). Therefore, the population,

mainly low-income inhabitants, have to move from suburban to central areas in order to take part of the activities

system. They depend highly on the public transport service (figures 5, 11 (a) and (b)) (bus, subway and light

train systems), and perform long displacements, many of them combined with walking and/or cycling (METRÔ,

2008), and is during these trips that the user is more vulnerable.

It is also in the central zone where there is the highest concentration (density) of arterials and collector roads

(figures 7, 8, and 9). These pathways have the greatest flows of vehicles and people, and are essential to the

economy and social development of the city in terms of commuting, freight transportation, and goods

distribution (COM, 1992; CRAINIC et al., 2009).

In Brazil, the increasing of motorcycle fleet is due to federal policies that favor low-cost manufacturing and

facilities to get financial credit to purchase this type of vehicle. In most cases, motorcycles are used for

commuting and the delivery of goods, because of its speed and ability to escape the congestion of urban centers.

This is directly related to the increased amount of fatal traffic accidents involving motorcycle riders (drivers and

passengers) in the last decade, which characterizes a situation of urban epidemic (MARÍN-LEÓN et al., 2012).

In São Paulo, the fleet of motorcycles circulates mainly in the CBD, which corroborates the hypothesis that this

area concentrates the economic activities and because of this generates a huge amount of urban trips, and

therefore is where most accidents involving VRUs occur.

Fig. 12 (a) Per capita income Fig. 12 (b) Moran map -Per capita income

Machado et al./ Transport Research Arena 2014, Paris

7. Conclusions

The results show that the fatal VRUs accidents are concentrated in municipality´s central area (CBD),

configuring the black zones of the city. These central areas also presents high work trip density and high arterial

and collector roads densities. The suburban areas spatial analyses showed a high concentration of public

transportation dependency, low income per capita and a high relationship between inhabitants per private

vehicles. The Moran map and Kernel density map are useful techniques to be used while analyzing spatial

distribution of VRUs accidents, socioeconomic aspects and transportation system attributes.

It is concluded that actions of territorial organization that seek a more balanced distribution of urban activities

(housing, work, education, healthy, etc.), and the development of integrated land use - transport modeling, can

modify trip patterns and reduce the amount of traffic accidents. Moreover, public policies involving

transportation system planning and operation in order to prioritize the most socially efficient transportation

means, such as non-motorized transport, and more stringent law enforcement (to reduce vehicle speed, traffic

violations, and so on) would reduce the number and severity of traffic accidents, mainly involving VRUs.

Acknowledgements

The authors thank the Project SaferBraIn <http://www.saferbrain.eu/>, the Metrô-SP (Companhia do

Metropolitano de São Paulo) and the CET-SP (Companhia de Engenharia de Tráfego de São Paulo) for

providing the database; and the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES and the

Conselho Nacional de Desenvolvimento Científico e Tecnológico - CNPq, for financial support to conduct this

study.

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