|Year : 2019 | Volume
| Issue : 1 | Page : 33-40
Environmental factors affecting the frequency of traffic accidents leading to death in 22 districts of Tehran during 2014–2016
Amir Kavousi1, Ali Moradi2, Hamid Soori3, Khaled Rahmani4, Salahdin Zeini5, Hossein Bonakchi6
1 Safety Promotion and Injury Prevention Research Center, Department of Epidemiology, School of Public Health and Safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran
2 Occupational Health and Safety Research Center, Hamadan University of Medical Sciences, Hamadan, Iran
3 Safety Promotion and Injury Prevention Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
4 Social Determinants of Health Research Center, Research Institute for Health Development, Kurdistan University of Medical Sciences, Sanandaj, Iran
5 Accident Department, Traffic Police of Tehran, Iran
6 Department of Biostatistics, Faculty of Paramedical Science, Shahid Beheshti University of Medical Sciences, Tehran, Iran
|Date of Submission||29-Dec-2018|
|Date of Acceptance||06-Jan-2019|
|Date of Web Publication||2-Aug-2019|
Dr. Ali Moradi
Occupational Health and Safety Research Center, Hamadan University of Medical Sciences, Hamadan
Source of Support: None, Conflict of Interest: None
Background: In many developed and developing countries, a significant proportion of deaths from road traffic accidents occur in the passages within cities. Considering the importance of this issue, this study was conducted to determine the environmental factors affecting the frequency of deaths in 22 areas of Tehran. Materials and Methods: In this study, the statistical population consisted of traumatic traffic incidents during the period of 2014–2016, all of which were studied. The data necessary for conducting the study were extracted from the traffic police databases, Tehran Municipality, and the Statistics Center of Iran. In order to analyze the role of regional and environmental factors in the frequency of traffic accidents related to pedestrians in geographic units (22 districts of Tehran), the Poisson regression and geographically weighted regression models were used. The likelihood ratio test was used to compare the models. The goodness-of-fit of models was evaluated using R2, Bayesian information criterion, and Akaike's information criterion statistics. Results: In this study, 519 incidents were studied, 175 of which (33.7%) were related to motorcyclists, 174 (33.5%) related to automobile drivers, and 170 (32.8%) pedestrians. The frequency distribution of the incidents studied varied in Tehran's 22 districts. The incidence of accidents in the central regions of Tehran was lower, while marginal areas located in the north, south, east, and west of Tehran had the highest frequency of fatal accidents. The both final Bayesian Poisson and GWR models showed that the relationship between the length of highways and educational land used with dependent variable was statistically significant. Conclusion: Various demographic and environmental variables play a role in determining the distribution pattern of these types of events. Through regional planning, proper traffic management, controlling environmental risk factors, and training people the pedestrian safety in Tehran can be improved.
Keywords: Environmental factors, geographic information system, statistical models, traffic accidents
|How to cite this article:|
Kavousi A, Moradi A, Soori H, Rahmani K, Zeini S, Bonakchi H. Environmental factors affecting the frequency of traffic accidents leading to death in 22 districts of Tehran during 2014–2016. Arch Trauma Res 2019;8:33-40
|How to cite this URL:|
Kavousi A, Moradi A, Soori H, Rahmani K, Zeini S, Bonakchi H. Environmental factors affecting the frequency of traffic accidents leading to death in 22 districts of Tehran during 2014–2016. Arch Trauma Res [serial online] 2019 [cited 2020 Jul 5];8:33-40. Available from: http://www.archtrauma.com/text.asp?2019/8/1/33/263967
| Introduction|| |
The incidence of traffic accidents has increased dramatically over the past decade. In other words, population growth, urbanization, and machine life have all increased the use of vehicles to move cargo and passengers. This phenomenon, in particular in developing countries, has led to an increase in deaths and injuries from driving accidents, because as the number of vehicles increases, the infrastructures necessary to improve the safety of their commute have not been established.,,
Traffic accidents are one of the main causes of death and disability, causing annual death of 1.24 million and injuries to tens of millions of people worldwide. Most deaths and injuries caused by accidents occur among young people at the productive and active ages. According to the World Health Organization, in the middle-income and lower-middle-income countries, the highest proportion of deaths from accidents is associated with young people (15–30 years old). Studies have shown that most traffic accidents, especially accidents involving pedestrians and particularly in high-income countries, occur in the passages within cities. For example, in the EU countries, 70% of these events occur in cities.
Considering the importance of this issue in recent years in various parts of the world, different methods of spatial analysis and various statistical models have been used to study the role of various environmental factors in the spatial distribution and severity of traffic accidents in urban areas. Noland and Oh in the state of Illinois studied the impact of road construction on road accidents and their rate of death. Hijar et al. evaluated the environmental risk factors of traffic accidents in highways in Mexico. Rivara and Barber of Tennessee University studied pediatric injuries in Memphis in terms of environmental and demographic factors. Ayati and Abbasi studied the efficiency of Poisson models, negative binomials, accumulated Poisson, and accumulated negative binomials for modeling traffic accidents in interstellar highways. Anastasopoulos and Mannering studied the efficiency of regression models based on count data, including Poisson regression and its derivatives, which consisted of negative binomials and zero-inflated models in the study of the frequency of traffic accidents. Sasidharan and Menéndez who used the partial proportional odds model as an intermediate model for ranked and nonranked models studied factors related to the severity of traffic accidents in pedestrians, and compared partial proportional odds, ordered logit, and multinomial logit for predicting the severity of traffic accidents.
In Iran, in recent years, due to the increase in the production of automobiles, the traffic volume has increased dramatically in the passages within and out of cities. Based on the statistics published by the forensic organization, 15,932 deaths and 33,307 injuries occurred due to traffic accidents during 2016. Because in Iran, a large proportion of traffic accidents are caused by environmental factors, especially the quality of roads and streets, and on the other hand, the capacity of the passageways in terms of their capacity to bear traffic load and also the mixing of various vehicle types has exacerbated the role of other factors in driving accidents.,
Investigating the intra-urban axes, especially heavy-traffic and populated cities such as Tehran in terms of environmental factors which influence driving accidents can be effective in identifying these factors more precisely and determining the contribution of each of them to the occurrence of accidents. Using the results of such studies, better planning can be done to reduce these risk factors. Therefore, this study aimed to provide models for determining the effective factors in the frequency of deaths in Tehran during the period of 2014–2016.
Tehran, the capital city of Iran, with 612 km2 area, is located in the north of the country. The population of Tehran, according to 2005 census was 7,803,883, whereas the number of stationary population is estimated to be 8,089,766 now. Tehran, which is the center of business and industry in Iran, is divided into 22 districts, 134 counties including Tajrish and Rey, and 560 traffic zones. Daily, there are 15–19 million Interurban travels which hamper traffic control strategies. Construction of highways, develop public transportation systems, and different traffic legislations are part of such strategies. Development of subway roads was another strategy which was responsible for moving 129 million passengers on 2011. Tehran is ranked as the 25th populated cities worldwide and is estimated to become more dense in 2030.
| Materials and Methods|| |
The present study was cross-sectional research in which the statistical population consisted of traffic incidents during the years of 2014-2016 in Tehran, all of which were studied. The data necessary for conducting the study were extracted from the traffic police databases of Tehran, Tehran Municipality and the Statistics Center of Iran. The definition of independent variables is provided in [Table 1].
Statistical analyses were performed in two descriptive and analytic parts, using SPSS (version 20; SPSS Inc., Chicago, Ill., USA) to descriptive analysis, using Arc-GIS (version 10.2; ESRI Inc, Redlands, CA, USA) to draw descriptive maps, using SAS (version 9.4; SAS Institute Inc., Cary, NC, USA) to fit Bayesian Poisson regression models and GWR (version 4; Department of Geography, Ritsumeikan University, Japan) to fit GWR model. To describe the quantitative data, the absolute and relative frequencies, mean, standard deviation, minimum, and maximum were used. The zoning maps were used to show the distribution of incidents based on geographic units.
Regression models were used to analyze the role of local and regional factors in the frequency of traffic accidents in geographic units (22 districts of Tehran). Because the dependent variable in these units is the number or density of traffic accidents in geographic units, the Poisson regression model was used. An important feature of the Poisson regression model is the equation of mean and distribution variance, which in practice may not be held, and more dispersion may occur. To estimate the Poisson regression coefficients, the maximum likelihood method is used., Due to the low number of study units that are included in 22 districts of Tehran municipality and not providing the necessary assumptions for using the conventional Poisson model, the univariate and multivariate Bayesian Poisson Regression models were used. The noninformative normal prior was specified for the regression coefficients. Because of that the variables of residential land used, commercial land used, educational land used, and industry land used had large scale, and hence, the logarithmic transformations were used. The significant levels for univariate and multivariate analyses were assigned 0.25 and 0.05, respectively.
Regarding the fact that ordinary regression models do not consider spatial autocorrelation in spatial data and also the role of the nonstationary part in these data is not considered in the relationship between variables in the calculations, for the analysis of environmental factors affecting the distribution of the incidents also GWR model was used. Fotheringham et al. developed this model for studying traffic accidents. This model is a generalization of the regular regression model. In this model, based on the following equation, the parameters in different locations vary according to their geographic coordinates (Vi, Ui) from the point i.
In fact, a regression model based on geographic information weights generates a regression equation for any spatial observation or geographic unit and provides for each point or location R2, β0, βk, and the t-statistic; therefore, this model is based on point equations and predicts the outcome by decreasing the sizes based on the distance between i and j using the following equation and according to the combination of the least squares method and the weight matrix (W (i)). The longer the distance between i and j, the higher weight the relevant factor will have.
The goodness-of-fit and performance of models were controlled using Akaike's information criterion, Bayesian information criterion, Adjusted R-squared (R2), and Percent deviance explained.
| Results|| |
In this study, 519 incidents were studied, 175 of which (33.7%) were related to motorcyclists, 174 (33.5%) related to automobile drivers, and 170 (32.8%) pedestrians. The frequency distribution of the incidents studied varied in Tehran's 22 districts. The incidence of accidents in the central regions of Tehran was lower, while the areas located in the north, south, east, and west of Tehran had the highest frequency of fatal accidents. The most frequent events were in district 4 (57 cases) and the least frequent incidents were in district 10 (6 cases). [Table 2] shows the frequency of crashes and independent variables in the models in regions of Tehran in 2014–2016. Furthermore, [Figure 1] shows the distribution of the density of the incidents studied in Tehran in the years 2014–2016 in terms of 22 districts
|Table 2: Frequency of crashes and independent variables in the models in regions of Tehran 2014-2016|
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In this study, the relationship between different variables and the frequency of traffic accidents in 22 districts of Tehran was investigated. The variables were divided into three general categories, including population and encounter, road networks, and travel and land use. [Table 3] lists the variables entered into the final models of the factors affecting the frequency of traffic accidents in 22 districts of Tehran with their descriptive characteristics, including mean, standard deviation, minimum, and maximum.
The Bayesian Poisson model shows the variables including population density, total number of employees at residence, total number of parks, length of highways, Percapita ownership of private cars, level of development, traffic volume, residential land used, commercial land used, educational land used, and industry land used were statistically significant on rate of crash. Each unit increase in population density led to almost 100% decrease in crash rate. In total number of employees at residence, each unit increase in this variable led to 95% increase in crash rate (percentiles interval: [1.85–2.05]). In total number of parks, each unit increase in this variable led to 1% increase in crash rate (percentiles interval: [1.012–1.015]). In length of highways, each unit increase in this variable led to 3.2% increase in crash rate (percentiles interval: [1.030–1.034]). In Percapita ownership of private cars, each unit increase in this variable led to 59% increase in crash rate (percentiles interval: [1.25–2.01]). In the level of development, each unit increase in this variable led to 0.3% increase in crash rate (percentiles interval: [1.001–1.005]). In traffic volume, each unit increase in this variable led to 2% increase in crash rate (percentiles interval: [1.025–1.030]). In residential land used, each unit increase in logarithm of this variable led to 84% increase in crash rate (percentiles interval: [1.75–1.95]). In commercial land used, each unit increase in logarithm of this variable led to 24% increase in crash rate (percentiles interval: [1.18–1.29]). In educational land used, each unit increase in logarithm of this variable led to 66% increase in crash rate (percentiles interval: [1.50–1.82]). In educational land used, each unit increase in logarithm of this variable led to 66% increase in crash rate (percentiles interval: [1.56–1.75]). In industry land used, each unit increase in logarithm of this variable led to 18% increase in crash rate (percentiles interval: [1.16–1.20]) [Table 4].
|Table 4: Fatal crash univariable and multivariable Bayesian Poisson model for regions in Tehran|
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Length of highways and educational land used were significant in the multivariable Bayesian Poisson model. an adjusted estimate of variable based on multivariable model demonstrate that each unit increase in the length of highways led to 2.9% increase in crash rate, presuming that the effect of other variables remains unchanged (percentiles interval: [1.014–1.04]). Moreover by assuming that the effect of all other variables are constant, crash rate with each unit increase in logarithm of educational land used 56% was increase (percentiles interval: [1.07–2.31]) [Table 4].
The GWR model shows that the variables of population density, total number of employees in at residence, total number of parks, length of highways, lack of parking space, per capita ownership of car, level of development, traffic volume, residential, commercial, educational, and industrial land use, are in the final model. In this regard, the relationship between all of them except the traffic volume total number of parks, with the dependent variable (number of accidents) in spatial units has been statistically significant. The direction of the variables of length of highways, the number of employees in the place of residence, educational and industrial land use also level of development with dependent variable has been positive and the direction of population density and residential land used, per capita ownership of car and lack of parking space with dependent variable has been negative [Table 5].
|Table 5: Fatal crash final geographically weighted regression model for regions in Tehran|
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| Discussion|| |
The marginal areas of Tehran, especially areas located in the northwest, east, south, and southwest of Tehran are the most vulnerable areas in terms of fatal traffic accidents. In total, the frequency of driving accidents leading to death in different parts of the city of Tehran was different regarding various variables during 2014–2016. In general, these variables could be categorized into three categories, including demographic variables, transport network, and land use.
This study indicated that the frequency distribution of fatal incidents had a significant relationship with demographic variables, including population density and employment in the place of residence, and the direction of this relationship was negative. In other words, the occurrence of these types of events in areas with high population density and higher employment in the place of residence was significantly less than those with a lower population density and less employment in the area. This relation could be because the incidence of accidents leading to deaths was lower in the central areas of Tehran, which had a high population density and more employment in their area of residence. Various studies in the United States, including Cottrill and Thakuriah in Chicago, Sebert Kuhlmann et al. from Denver, Colorado, Siddiqui et al. in Florida, Ukkusuri et al. in New York, and McArthur et al. in Michigan have also showed a significant relationship between population density and frequency of traffic accidents involving pedestrians. However, the direction of the relationship in the mentioned studies has been positive despite the results of the present study. Given that lower density of the fixed population and lower employment in the workplace and at the place of residence will reduce the traffic load of the passages, resulting in increased traffic speed, one of the reasons for this issue is the high traffic speed and increased number and intensity of collision of vehicles with pedestrians in the marginal areas of Tehran, which are often less crowded and have lower employment at work and residence compared to the central regions. Of course, the increase in the density of highways and the low level of pedestrian safety facilities in Tehran compared to the major American cities and cultural differences in terms of following traffic regulations can also lead to these differences.
Frequency of accidents leading to deaths has a significant relationship with the length of highways, which is one of the most important transport variables. The direction if this relationship is positive. In other words, the incidence of these types of incidents is higher in areas where the density of highways is higher. Given the fact that in these areas more vehicles are moving at high speeds, it can be said that the proportion of severe accidents is increased and thus, the incidence of fatal accidents increases as well. Studies in other parts of the world have also shown such a relationship. Examples include Hashimoto in Florida, Cloutier et al. in Canada, and Green et al. in the UK. Therefore, it can be said that one of the factors increasing the incidence of road traffic accidents leading to death in Tehran is associated with unsafe highways.
Frequency distribution of incidents leading to deaths has a significant relationship with land use variables, including educational and industrial use. The incidence of these events is higher in the areas with more industrial and educational density. One of the reasons for this issue can be the traffic of more heavy and semi-heavy vehicles for the transfer of industrial materials and products, which consequently leads to an increase in the proportion of severe accidents with light cars, motorcyclists, and pedestrians.
Moreover, one of the reasons for the increased incidence of fatal traffic accidents in Tehran is the insufficiency of safety for students of schools and universities. It should be noted that studies carried out in other parts of the world have shown that one of the factors contributing to the frequency distribution of traffic accidents in urban areas is the use of land in different parts. Examples include Yao and Loo in Hong Kong, Ukkusuri et al. in New York, and Wier et al.in the United States. Therefore, it is necessary to consider the improvement of the safety of motorcyclists and pedestrians in the development plan of different areas of Tehran, in particular, reconstruction of old textures.
The results indicate that the incidence of fatal accidents decreases with the increase of the developmental level and number of parks in the 22 districts of Tehran. The high incidence of fatal accidents in low developed areas, especially in the south of the city, can be because in these areas, the criteria for improving the safety of the passage of vehicles and pedestrians have not been sufficiently addressed. Moreover, the reason for the reduced frequency of accidents with increasing number of parks can be due to increasing the passengers attraction for leisure and fewer encounters with vehicles. Therefore, by increasing the level of development in different areas of Tehran and increasing the safety of vehicles and pedestrians, the incidence of such events can be reduced.
This study also had some limitations. In Iran, the relation between the deaths of the wounded from traffic accidents and the incident has been defined based on 1 month after the time of death, but in this study, only the data from traffic police have been analyzed according to the method used. Therefore, accidents that have led to deaths within 1 month after the incident have been classified as fatal accidents.
Due to the lack of registration of geographical coordinates of traffic incidents at the time of the accident, their coordinates have been determined approximately during data recording on the computer using auxiliary points and systems for recording spatial coordinates; as a result, the obtained coordinates may have a difference of some meters with the exact coordinates of the accidents.
In a limited number of cases, the auxiliary points recorded by the police were very weak, and it was attempted to determine the exact location of the incident through contact with the people involved in the accident. However, determining the exact location of the occurrence of these cases may be accompanied by some errors.
Occasional environmental events may change over time. To overcome this problem, the latest police data were used to conduct the study while geographic layers and information or estimates of the time of the study or their approximate estimates were used for the analyses.
One of the most important limitations of this study is the low sample size. Because information about independent variables exists for only 22 districts in the city of Tehran. Therefore, using routine counting models such as Poisson, when entering a large number of covariate in models and assuming normalization, may lead to errors in estimates. For this reason, in this study, we use the Bayesian Poisson models that do not require such assumptions, and its results are compared with the GWR model.
One of the strengths of this study is that by interacting and cooperating with traffic police, systematic information of Tehran police databases was used to determine the environmental factors of traffic accidents while the factors influencing the distribution of fatal traffic incidents were also analyzed and compared.
| Conclusion|| |
This study is one of the few studies that investigate the spatial risk factors of incidents and traffic accidents in intra-city passages in Iran using geographic information system and regression models. This study initially identified high-risk areas in terms of fatal traffic accidents in Tehran using the geographic information system. Then, using regression models, factors influencing the frequency distribution of the incidents in geographic units (22 districts) were determined. This study showed that if the appropriate information is available about dependent variables and environmental factors, it is possible to determine the factors affecting the distribution of these types of events within the cities using regression models, while more targeted planning can be also done based on the obtained results in order to reduce them.
All of the friends who participated in this study, especially the commanding officer of the traffic police, Mr. Mehmandar, and the staff of the office of Tehran traffic accidents, particularly Mr. Soroush are appreciated and thanked. The budget of this project has been provided by the Faculty of Health, Safety, and Environment of Shahid Beheshti University of Medical Sciences.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
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[Table 1], [Table 2], [Table 3], [Table 4], [Table 5]