Abstract
Background
Air pollution and weather are just two of many environmental factors contributing to traffic accidents (RTA).
Objectives
This study assessed the effects of these factors on traffic accidents and related mortalities in Ahvaz, Iran.
Methods
In this ecological study, data about RTA, traffic-related mortalities, air pollution (including NO, CO, NO2, NOx PM10, SO2, and O3 rates) and climate data from March 2008 until March 2015 was acquired from the Khuzestan State Police Force, the Environmental Protection Agency and the State Meteorological Department. Statistical analysis was performed with STATA 12 through both crude and adjusted negative binomial regression methods.
Results
There was a significant positive correlation between increase in the monthly average temperature, the number of rainy days, and the number of frost days with the number of RTA (P < 0.05). Increased monthly average relative humidity, evaporation, and number of sunny days were negatively correlated with the frequency of RTA (P < 0.05). We also observed an inverse significant correlation between monthly average relative humidity, evaporation, and wind speed with traffic accident mortality (P < 0.05). Some air pollutants were negatively associated with the incidence rate of RTA.
Conclusions
It appears that some weather variables were significantly associated with increased RTA. However, increased levels of air pollutants were not associated with increased rates of RTA and/or related mortalities. Additional studies are recommended to explore this topic in more detail.
Keywords: Air Pollution, Weather, Traffic Accidents, Mortality
1. Background
Traffic accidents (RTA) impose serious medical, economical, and human costs on society (1). Injuries and deaths caused by car accidents are a major public health issue (2). As reported by the world health organization (WHO) in 2013, about 1.24 million deaths occur on the world’s roads each year (3). In addition, it is predicted that RTA are going to be the second or third leading cause of death in high- and middle-income countries, and by 2020, car accident mortality could reach up to two million people in the world, and developing countries will suffer a considerable share of these mortalities (2, 4, 5). Iran is reported as one of the countries with the highest number of deaths caused by RTA (5). Annually, an average of about 30 per 100,000 people die because of RTA, which is much higher than the world average (22.6 per 100,000), and also higher than the rate of the Eastern Mediterranean Region (13.9 per 100,000) (5, 6). Traffic-related death is considered to be the second-leading cause of death in Iran, and comprises 10.3% of all reported deaths, which is almost five times the global average (2.1%) (6).
Therefore, it is necessary to understand the various factors that may cause RTA. Although a considerable number of studies have been published on this topic, more information is still needed about the various factors causing traffic crashes in order to provide a better method of managing them (1). Fortunately, over the past few decades, several studies about the various factors influencing RTA have been conducted in other parts of the world (7).
The various factors that can affect the incidence rate of traffic accidents may be inevitable, but many are preventable. For instance, Older and Spicer believe that a traffic accident is the result of three combined influences: human, environmental, and road (8). Some of the environmental factors influencing safe and sustainable urban transport include air pollution and weather variables such as storms, frost, snow, rain, fog, and general temperature fluctuations. A literature review showd that only a few studies have considered the effects of weather variables on RTA mortality (9-11). Weather parameters have a well-established relationship to RTA, involving such conditions as rain, snow, temperature, and wind speed (1). However, it seems as though the impact of some other factors, such as ambient air pollutants, on RTA and mortality has not been adequately studied. Furthermore, most of the previous studies are limited to accidents on rural expressways and highways, whereas urban and suburban accidents have yet not been studied (12). The reason may be that considering the effects of weather variables and air pollution on RTA is a relatively new concern. This is illustrated by the fact that for the first time in 1960, when planning a transit road between Liverpool and Hall, local weather was considered as a relevant factor, and later, this led to the more general study of weather variables before planning new roads in England (13). In recent years, determining the effects of various weather factors on the risk, severity, and frequency of RTA has attracted a lot of attention (14). There is no doubt that weather variables (e.g., frost, fog, rain, and dust), either directly or indirectly along with other environmental components, play an important role in RTA (1).
One study conducted about the relationship between climate factors and RTA was developed by Edwards, who studied the relationship between weather variables and car accidents in Wales, England. The results showed a significant increase in the severity and frequency of RTA in rain and fog conditions as compared to those with clear weather variables (14). Along similar lines, Usman et al. studied some of the other relevant factors, including rainfall intensity, visibility, temperature, and wind speed. They reported that low visibility, wet roads, high-speed winds, and low temperatures could substantially increase RTA frequency (10). Another study by Mahmoudi in Iran (on Sanandaj-Hamedan intercity roads) found that high-speed winds, frost, and snowfall play major roles in the incidence rates of RTA (15). However, a study in Ardebil showed that most accidents occurred on clear and sunny days, and that crashes increased with rising temperature, and that foggy days had the lowest number of RTA (16).
The oil and gas, and the industrial effects of the petrochemical and steel industry have caused Ahvaz to become more vulnerable to natural and artificial air pollution (17). The most common and the most toxic pollutants in Ahvaz are CO and PM10; even though CO levels have dropped by about 25% compared to 2008, PM10 levels are still a concern due to micro-dust from western borders.
2. Objectives
Since pollution and weather variables can affect RTA incidence and mortality rates, we aimed to study the effects of these factors on traffic crashes and related mortalities in Ahvaz, Iran. Interestingly, despite being one of the most polluted cities in the world (18), there have not been any studies addressing the effects of air pollutants and weather variables on traffic crashes in Ahvaz.
3. Methods
This ecological study was based on recorded data from March 2008 until March 2015 in Ahvaz, Iran. Monthly data about urban RTA were obtained from the applied research office of the police force of Khuzestan province, categorized in terms of age, gender (of the guilty driver), and time of the accident. Data was obtained from the Khuzestan province’s forensic medicine office.
Ambient air pollution data was acquired from the Khuzestan province environmental protection agency for seven major pollutants: (1) particulate matter less than 10 μm (PM10), (2) nitrogen monoxide (NO), (3) nitrogen dioxide (NO2), (4) nitrogen oxides (NOx), (5) carbon monoxide (CO), (6) sulfur dioxide (SO2), and (7) ozone (O3). PM10 was the only type of particulate matter which has been recorded at the air quality monitoring stations over the years.
Data on means; max and min temperatures; average relative humidity; number of sunny, rainy and frost days; total evaporation; wind speed; and wind direction were collected from the meteorological organization of the Khuzestan province. Monthly traffic crash incidents and traffic-related mortalities were matched with monthly averages of air pollution and weather data.
Quantitative descriptive analysis was used to describe the damage, injury, and death rates caused by urban traffic crashes, while accounting for the pollutant levels and weather data. Initially, the Poisson regression assumptions were checked. According to a goodness-of-fit test, the distributions did not follow the Poisson distribution (P < 0.001). The relation between traffic crash incidents, traffic-related mortality, and monthly average air pollution and weather factors was analyzed using a negative binomial regression. All statistical analysis was performed using STATA 12, and a value of P < 0.05 was considered significant.
4. Results
A total of 76,006 traffic crashes were recorded by the police force of Khuzestan province between March 2008 to March 2015, which can be divided into three groups of property damage (71.6%), injury (27.7%), and death (0.7%). Most guilty drivers were men (96.6%) in the age range of 25 - 35 years (39.5%). Most traffic crashes had occurred between 6 - 12 a.m. (40.7%) and in the spring (27.8%). Moreover, as reported by forensic medicine, a total of 1,013 deaths were caused by urban traffic crashes in the previous seven years, with an average of 12.05 ± 4.2, and a minimum of three and maximum of 24 deaths per month (see Table 1).
Table 1. Characteristics of Traffic Crashes in Ahvaz from March 2008 to March 2015.
| Variable | No. (%) |
|---|---|
| Type of traffic crash | |
| Damage | 54438 (71.6) |
| Injury | 21047 (27.7) |
| Death | 521 (0.7) |
| Total | 76006 (100) |
| Age groups (year) | |
| Under 18 years | 380 (0.5) |
| 18 - 25 | 7601 (10) |
| 26 - 30 | 15581 (20.5) |
| 31 - 35 | 14441 (19) |
| 36 - 40 | 11781 (15.5) |
| 41 - 45 | 8361 (11) |
| 46 - 50 | 7221 (9.5) |
| 51 - 60 | 8361 (11) |
| 61 - 70 | 1900 (2.5) |
| 71 - 80 | 380 (0.5) |
| Total | 76006 (100) |
| Sex | |
| Male | 73464 (96.6) |
| Female | 2542 (3.4) |
| Total | 76006 (100) |
| Time of day | |
| 24 - 6 (Morning) | 3530 (4.6) |
| 6 - 12 (Morning) | 30959 (40.7) |
| 12 - 18 (Afternoon) | 20844 (27.4) |
| 18 - 24 (Night) | 20673 (27.2) |
| Total | 76006 (100) |
| Season | |
| Spring | 21141 (27.8) |
| Summer | 19906 (26.2) |
| Fall | 18246 (24) |
| Winter | 16713 (22) |
| Total | 76006 (100) |
A summary of air pollution data has been provided in Table 2. Clearly, PM10 is the major air pollutant with varying values for each season. The average value of this pollutant has been over 200 μg/m3. During the study interval, PM10 rates were in the good range (0 - 50 μg/m3) for 2.1%, in the moderate range (51 - 100 μg/m3) for 15.7%, in the unhealthy range for sensitive groups (50 - 101 μg/m3) at 26.5%, unhealthy for everyone (151 - 200 μg/m3) at 20.8%, very unhealthy (201 - 300 μg/m3) for 17.1%, and in the hazardous range (301 - 500 μg/m3) for 10.7% of the days. Also, in 7.2% of the days, it has been in the 501 - 4498 μg/m3 range.
Table 2. Descriptive Indices of Air Pollutants and Climate Factors in Ahvaz from March 2008 to March 2015.
| Variable (Mean per month) | Mean | Median | Minimum | Maximum | SD |
|---|---|---|---|---|---|
| O 3 (ppm) | 0.062 | 0.021 | 0.007 | 2.064 | 0.280 |
| PM 10 (µg/m 3 ) | 237.15 | 162.50 | 25 | 4498 | 289.81 |
| NO (ppm) | 0.019 | 0.017 | 0.003 | 0.066 | 0.012 |
| NO 2 (ppm) | 0.021 | 0.017 | 0.002 | 0.081 | 0.016 |
| NO x (ppm) | 0.037 | 0.035 | 0.006 | 0.134 | 0.022 |
| CO (ppm) | 1.319 | 1.000 | 0.100 | 9.100 | 1.499 |
| SO 2 (ppm) | 0.021 | 0.016 | 0.001 | 0.096 | 0.019 |
| Temperature (°C) | 26.7 | 27.1 | 11.6 | 39.5 | 9.1 |
| Minimum temperature (°C) | 19.7 | 20 | 18.8 | 20.4 | 0.6 |
| Maximum temperature (°C) | 33.6 | 33.9 | 32.6 | 34.3 | 0.6 |
| Relative humidity (%) | 43.1 | 41.0 | 19 | 77 | 15.8 |
| Total rainfall (mm) | 14.0 | 2.4 | 0 | 113 | 23.4 |
| Total sunshine (hours) | 256.4 | 253.2 | 144 | 374 | 62.6 |
| Total evaporation (mm) | 264.7 | 255.2 | 41 | 541 | 161.3 |
| Wind speed (m/s) | 11.5 | 10.0 | 7 | 44 | 5.3 |
| Wind direction (°) | 227.2 | 270.0 | 42 | 350 | 82.6 |
The results of the negative binomial regression showed that O3, PM10, NO, NO2, and NOX concentrations were inversely associated with the incidence rate of traffic crashes. After adjustments for the effects of confounding factors, O3, NO2, and NOX levels were still significantly associated with the frequency of RTA (Table 3).
Table 3. Results of crude and adjusted negative binomial regression, and the effects of pollutants and weather variables on traffic crashes (ratio of increase in traffic crashes per month to unit of increase in pollutants and weather variables per month on average).
| Variables | Crude IRR* and 95% CI | P | Adjusted IRR* and 95% CI | P |
|---|---|---|---|---|
| O 3 (ppm) | 0.99825 (0.99746 - 0.99903) | < 0.001a | 0.99884 (0.99806 - 0.99961) | 0.004a |
| PM 10 (µg/m 3 ) | 1.00124 (1.00032 - 1.00217) | 0.008a | 0.99974 (0.99895 - 1.00052) | 0.519 |
| NO (ppm) | 0.97420 (0.967008 - 0.98145) | < 0.001a | 0.99452 (0.98308 - 1.0060) | 0.352 |
| NO 2 (ppm) | 0.98062 (0.97444 - 0.98683) | < 0.001a | 0.98929 (0.9803171 - 0.9983392) | 0.020a |
| NO x (ppm) | 0.98698 (0.98270 - 0.99128) | < 0.001a | 0.99264 (0.98807 - 0.99722) | 0.002a |
| CO (ppm) | 1.00003 (0.99994 - 1.00011) | 0.483 | 0.99996 (0.99990 - 1.00002) | 0.267 |
| SO 2 (ppm) | 0.99746 (0.9910563 - 1.003902) | 0.439 | 0.99725 (0.99229 - 1.00223) | 0.280 |
| Temperature (°C) | 1.01213 (0.99967 - 1.02475) | 0.040a | 1.02910 (1.01010 - 1.06745) | 0.044a |
| Relative humidity (%) | 0.98939 (0.98283 - 0.99598) | 0.002a | 0.97280(0.95550 - 0.99041) | 0.003a |
| Total evaporation(mm) | 1.00047 (0.99976 - 1.00118) | 0.192 | 0.99759 (0.99545 - 0.99973) | 0.027a |
| Sunny days (number in month) | 0.99961 (0.99765 - 1.00156) | 0.696 | 0.99646 (0.99295 - 0.99998) | 0.040a |
| Rainy days (number in month) | 0.99696 (0.99239 - 1.00155) | 0.195 | 1.00312 (1. 00112 - 1.00799) | 0.043a |
| Frost days (number in month) | 1.17526 (1.12526 - 2.46156) | 0.004a | 1.09876 (1.05877 - 1.92172) | 0.021a |
| Wind speed (m/s) | 1.01331 (0.99186 - 1.03523) | 0.226 | 1.00088 (0.98520 - 1.01680) | 0.913 |
Abbreviation: IRR, incidence rate ratio.
aStatistically significant.
Our results also showed that traffic crashes were positively correlated with monthly average temperature and the number of frost days, while negatively correlated with average relative humidity. When adjusted for the effects of confounding factors, average relative humidity, evaporation, and the number of sunny days were inversely correlated with the results, whereas monthly average temperature and number of rainy and frost days were positively correlated with the frequency of RTA (Table 3).
We observed a significant inverse relation between fatal accidents and NO and NO2 concentrations. Adjusting for the effects of the confounding factors, O3, PM10, NO2, and CO concentrations were inversely correlated with the rates of fatal accidents (Table 4).
Table 4. Results of crude and adjusted negative binomial regression analyses, and the effects of pollutants and weather variables on traffic-related mortality (ratio of increase in traffic-related mortality per month and per unit of increase for pollutants and weather variables based on monthly averages).
| Variables | Crude IRR* and 95% CI | P | Adjusted IRR* and 95% CI | P |
|---|---|---|---|---|
| O 3 (ppm) | 0.99967 (0.99906-1.00028) | 0.296 | 0.99933 (0.99868-0.99997) | 0.043a |
| PM 10 (µ g/m 3 ) | 0.99973 (0.99914-1.00033) | 0.392 | 0.99910 (0.99846-0.99974) | 0.006a |
| NO (ppm) | 0.99238 (0.98574-0.99907) | 0.026a | 1.00813 (0.99838-1.01797) | 0.102 |
| NO 2 (ppm) | 0.99026 (0.98473-0.99583) | 0.001a | 0.98702 (0.97931-0.99480) | 0.001a |
| NO x (ppm) | 0.99821 (0.99432-1.00211) | 0.370 | 0.99852 (0.99438-1.00267) | 0.485 |
| CO (ppm) | 0.99996 (0.99990-1.00001) | 0.193 | 0.99993 (0.99988-0.99998) | 0.011a |
| SO 2 (ppm) | 1.00084 (0.99636-1.00533) | 0.713 | 1.00039 (0.99659-1.00420) | 0.840 |
| Temperature (°C) | 0.99786 (0.98975-1.00605) | 0.609 | 1.01064 (0.98371-1.03831) | 0.442 |
| Relative humidity (%) | 0.99891 (0.99418-1.00366) | 0.653 | 0.97761 (0.96319-0.99225) | 0.003a |
| Total evaporation(mm) | 0.99979 (0.99933-1.00026) | 0.391 | 0.99789 (0.99620-0.99958) | 0.015a |
| Sunny days (number in month) | 0.99949 (0.99829-1.00069) | 0.408 | 0.99982 (0.99724-1.00240) | 0.893 |
| Rainy days (number in month) | 0.99936 (0.99617-1.00256) | 0.695 | 1.00139 (0.99707-1.00572) | 0.528 |
| Frost days (number in month) | 1.20466 (0.76509-1.89675) | 0.421 | 1.00750 (0.67360-1.50691) | 0.971 |
| Wind speed (m/s) | 0.98857 (0.97385-1.00351) | 0.133 | 0.98446 (0.97068-0.99844) | 0.030a |
Abbreviation: IRR, incidence rate ratio.
aStatistically significant.
Ultimately, our analysis showed that there was no significant association between climate factors and mortality. However, after adjusting for the effects of confounding factors, average relative humidity, monthly evaporation, and wind speed appeared to be inversely associated with mortality (Table 4).
5. Discussion
RTA are one of the most important problems of modern societies, and include many social, economic, and health-related issues. The present study addressed the effects of weather and air pollution on urban RTA and traffic-related mortality rates in Ahvaz, Iran.
There are four air quality monitoring stations in Ahvaz, including the environmental protection agency station, the Naderi Square station, the university square station, and meteorological organization station. According to the environmental protection agency experts, the air quality monitoring stations’ locations were representative of the ambient air quality of the whole city. Ahvaz, with an area of 8,152 square kilometers and the capital city of Khuzestan province, is located between the 48th degree to the 49th degree east of the Greenwich meridian, and between 31 degrees and 45 minutes north of the equator (19). According to the 2011 census, Ahvaz has 286,032 households and 1,056,589 residents (20).
Our results indicated that most urban traffic crashes among drivers were within the age range of 25 - 35 years, which is roughly consistent with previous studies. It seems like people involved in traffic crashes are mostly young adults. This group is the most active group in society, and therefore their loss imposes high economical costs on the society.
In this study, men were more involved in urban traffic crashes than women. Previous studies have also pointed out that more men are involved in RTA than women (21-25). The reason may be that male drivers are more often engaged in dangerous driving in comparison to females (2, 4, 26).
Our findings showed an inverse and significant association between concentrations of O3, NO2 and NOX with urban traffic crashes. Likewise, we observed an inverse and significant association between concentrations of O3, PM10, NO2 and CO with traffic crash mortality. Furthermore, weather variables such as relative humidity and evaporation were inversely associated with mortality, whereas numbers of sunny and clear days were inversely associated with traffic crashes.
In line with these results, other studies have also observed an inverse association between weather variables and the incidence rate, risk, and severity of traffic crashes (13, 27-32). For instance, in a study in Riyadh, Saudi Arabia, monthly RTA records for seven years were evaluated. It was ultimately determined that RTA were inversely correlated with relative humidity, evaporation, snowfall, and hail (32). It is also worth noting that in a study, by Fridstrøm et al. in 1995 in Denmark, RTA decreased by 1.2% per one day increase in the number of frost and snow days (30).
In Greece, Karlaftis and Yannis showed that increases in rainfall reduced the incidence rates of RTA, traffic-related mortality, pedestrian-involved crashes, and pedestrian mortality. These results are in conflict with many previous studies where rainfall increased RTA. Researchers think that the reason may be based on the safety-offset hypothesis, which suggests that drivers practice more caution and less speedy-driver behavior in conditions which they believe may dangerous, as southern European drivers are not used to driving in rain (13).
Bergel-Hayat et al. showed that on interurban roads (unlike highways outside the city), RTA were inversely related to weather variables such as rain. These researchers argued that reduced traffic volume and hence reduced exposure are the reasons for the lower number of RTA during rainfalls (27). In addition, Aguero-Valverde and Jovanis in Pennsylvania, USA, concluded that although there is a linear relationship between rainfall and an increased number of RTA using negative binomial models, no statistically significant relationship was observed using Bayesian hierarchical models (28). It therefore seems as though the relation between weather variables and RTA is highly dependent upon both the geographical features of the region and the local perceptions of extreme weather and dangerous conditions. Presumably, this is the reason for conflicting reports in the literature (1).
Other studies, such as those by Andrey and Yagar (1993) and Khattak et al. (1998), have also mentioned driver compensation in extreme weather conditions (33, 34).
In this study, we found a positive correlation between average temperature rates and the frequency of RTA. This result is consistent with the results of a study by Bergel-Hayat et al., where average temperature was correlated with the incidence rates of traffic crashes in France, the Netherlands, and Athens (27). These researchers observed that per one degree centigrade increase in average temperature, the incidence rate of monthly traffic crashes increased by 1 - 2% (27). Along similar lines, Karlaftis and Yannis reported that daily temperature increases could result in increased numbers of total RTA, traffic injuries, and pedestrian accidents (13). Nofal and Saeed stated that in Saudi Arabia, RTA are more frequent in the summer between noon and 3 p.m. when there is fierce sunlight, heavy traffic, and an average temperature of 42° C. In a study by Ranandeh Kalankesh et al. conducted in Kerman, Iran, it was reported that traumatic death happens more often in hot seasons, and the strongest association between temperature and traumatic death was seen in those aged 60 and over (35).
In this study, rainy and frosty days were associated with more accidents. Several studies have shown that rain and snowfall increase the incidence of RTA (30, 33, 36-40). For example, Andreescu and Frost showed that snow and frost days are the most important weather factors contributing to traffic problems, and snow can greatly increase the incidence of traffic crashes (40). In accordance with our findings, Eisenberg and Warner showed that snow leads to more non-fatal traffic crashes than fatal RTA (36). In the same vein, Andrey and Yagar maintain that the increased risk created by rain, snow, and frost is due to a combination of the slippery road conditions, low friction, low visibility, and light reflection from the road surface during nighttime conditions. Even if the driver adjusts his or her perception and vehicle conditions to the wet road, the risk of crash is still high because of low visibility (33).
Our results also suggested an inverse correlation between wind speed and traffic crash-related mortalities. Our results were in contrary with those of Hermans et al., which indicated that hurricanes and prolonged rainfall are directly related to an increased risk of traffic crashes in the Netherlands (41). Similarly, Baker and Reynolds reported that hurricanes in the UK increased traffic crash rates from 50% to 66% (42). Overall, the effects of wind speed have not been well studied in the literature, and only a few studies have addressed it in relation to traffic crashes (41-44). Furthermore, most studies do not agree that wind speed can increase the incidence and/or mortality rates of traffic crashes (1). Further studies are therefore required to assess the possible effects of wind speed.
5.1. Conclusions
Overall, the findings here suggest that some weather variables such as temperature and rainy and frosty days can substantially increase the risk of traffic crashes.
Acknowledgments
The authors would like to express their gratitude to the staff of the Applied Research Office of the Khuzestan Police Force, the Meteorological Organization Office, and, especially, Mr. Alireza Azarian from the Khuzestan Province Environmental Protection Agency for their sincere help and cooperation.
Footnotes
Authors’ Contribution:All authors have participated in various parts of study.
Financial Disclosure:The authors declare that they have no conflict of interest.
Funding/Support:This study was approved by the Neurology Research Center and funded by Kerman University of Medical Sciences (Grant No. 94/595 and Ethics Committee No: 17090).
References
- 1.Theofilatos A, Yannis G. A review of the effect of traffic and weather characteristics on road safety. Accid Anal Prev. 2014;72:244–56. doi: 10.1016/j.aap.2014.06.017. [DOI] [PubMed] [Google Scholar]
- 2.Bahadorimonfared A, Soori H, Mehrabi Y, Delpisheh A, Esmaili A, Salehi M, et al. Trends of fatal road traffic injuries in Iran (2004-2011). PLoS One. 2013;8(5):65198. doi: 10.1371/journal.pone.0065198. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.World Health Organization, WHO. WHO global status report on road safety 2013: supporting a decade of action. 2013
- 4.Peden M. World report on road traffic injury prevention. Geneva: World Health Organization; 2004. [Google Scholar]
- 5.Shams M, Shojaeizadeh D, Majdzadeh R, Rashidian A, Montazeri A. Taxi drivers' views on risky driving behavior in Tehran: a qualitative study using a social marketing approach. Accid Anal Prev. 2011;43(3):646–51. doi: 10.1016/j.aap.2010.10.007. [DOI] [PubMed] [Google Scholar]
- 6.Bhalla K, Naghavi M, Shahraz S, Bartels D, Murray CJ. Building national estimates of the burden of road traffic injuries in developing countries from all available data sources: Iran. Inj Prev. 2009;15(3):150–6. doi: 10.1136/ip.2008.020826. [DOI] [PubMed] [Google Scholar]
- 7.Miles DE, Johnson G. L. Aggressive driving behaviors: are there psychological and attitudinal predictors? Transportation Research Part F. Traffic Psychol Behav. 2003;6(2):147–61. [Google Scholar]
- 8.Older SJ, Spicer BR. Traffic conflicts-a development in accident research. Human Factors. J Human Factors Ergonomics Soci. 1976;18(4):335–50. [Google Scholar]
- 9.Usman T, Fu L, Miranda-Moreno LF. Quantifying safety benefit of winter road maintenance: accident frequency modeling. Accid Anal Prev. 2010;42(6):1878–87. doi: 10.1016/j.aap.2010.05.008. [DOI] [PubMed] [Google Scholar]
- 10.Usman T, Fu L, Miranda-Moreno LF. A disaggregate model for quantifying the safety effects of winter road maintenance activities at an operational level. Accid Anal Prev. 2012;48:368–78. doi: 10.1016/j.aap.2012.02.005. [DOI] [PubMed] [Google Scholar]
- 11.Wang C, Quddus MA, Ison SG. The effect of traffic and road characteristics on road safety: A review and future research direction. Safety Sci. 2013;57:264–75. [Google Scholar]
- 12.Shew C, Pande A, Nuworsoo C. Transferability and robustness of real-time freeway crash risk assessment. J Safety Res. 2013;46:83–90. doi: 10.1016/j.jsr.2013.04.005. [DOI] [PubMed] [Google Scholar]
- 13.Karlaftis MG, Yannis G. Weather Effects on Daily Traffic Accidents and Fatalities: Time Series Count Data Approach. 89 ed. Ann Meeting; 2010. [Google Scholar]
- 14.Edwards JB. The relationship between road accident severity and recorded weather. J Safety Res. 1999;29(4):249–62. [Google Scholar]
- 15.Mahmoudi P. The effect of climatic parameters affecting traffic accident with an emphasis on mountain roads centered in Sanandaj - Hamedan. Climatology Islam Azad Univ Central Tehran; 2005. [Google Scholar]
- 16.Alizadeh S. Analyzing the role of climatic factors on road accidents (Case study: Ardabil-Parsabad road in Ardabil Province, Iran). Scientific J Pure Applied Sci. 2014;3(7):572–84. [Google Scholar]
- 17.Goudarzi G, Geravandi S, Saeidimehr S, Mohammadi M, Vosoughi Niri M, Salmanzadeh S. Estimation of health effects for PM10 exposure using of Air Q model in Ahvaz City during 2009. Iranian J Health Environment. 2015;8(1):117–26. [Google Scholar]
- 18.Tabatabaei S. A., Treur J. Comparative analysis of the efficiency of air source heat pumps in different climatic areas of Iran. Procedia Environment Sci. 2016;34:547–58. [Google Scholar]
- 19.Goudarzi G, Geravandi S, Vosoughi M, javad Mohammadi M, Sadat Taghavirad S. Cardiovascular deaths related to Carbon monoxide Exposure in Ahvaz, Iran. Iranian J Health, Safety Environment. 2014;1(3):126–31. [Google Scholar]
- 20.Yearbook IS. Iran Statistical Yearbook. Tehran, Iran: Statistical Center of Iran; 2013. [Google Scholar]
- 21.Ghorbani A, Rabiei MC. Epidemiology of trauma due to collision in shahid motahari hospital of Gonbad-e-Kavous city. Sci J Forensic Med. 2009;15(53):29–34. [Google Scholar]
- 22.Abdolvand M, Monfared AB, Khodakarim S, Farsar AR, Golmohammadi A, Safaei A. Evaluation of accidents and incidents at injury registered in medical centers affiliated to Shahid Beheshti University of Medical Sciences (2012-2013). Safety Promot Injury Prevent. 2014;2(1):65–72. [Google Scholar]
- 23.Saneei TM, Hemadi H, Sajadinasab M, Sharifi G, Jalali A, Shakiba M. Computed tomography findings in patients with mild head trauma. 2007
- 24.Ganveer GB, Tiwari RR. Injury pattern among non-fatal road traffic accident cases: a cross-sectional study in Central India. Indian J Med Sci. 2005;59(1):9–12. [PubMed] [Google Scholar]
- 25.Laapotti S, Keskinen E. Has the difference in accident patterns between male and female drivers changed between 1984 and 2000? Acc Analy Prev. 2004;36(4):577–84. doi: 10.1016/S0001-4575(03)00064-2. [DOI] [PubMed] [Google Scholar]
- 26.Whissell RW, Bigelow BJ. The speeding attitude scale and the role of sensation seeking in profiling young drivers at risk. Risk Anal. 2003;23(4):811–20. doi: 10.1111/1539-6924.00358. [DOI] [PubMed] [Google Scholar]
- 27.Bergel-Hayat R, Debbarh M, Antoniou C, Yannis G. Explaining the road accident risk: weather effects. Accid Anal Prev. 2013;60:456–65. doi: 10.1016/j.aap.2013.03.006. [DOI] [PubMed] [Google Scholar]
- 28.Aguero-Valverde J, Jovanis PP. Spatial analysis of fatal and injury crashes in Pennsylvania. Accid Anal Prev. 2006;38(3):618–25. doi: 10.1016/j.aap.2005.12.006. [DOI] [PubMed] [Google Scholar]
- 29.Theofilatos A, Graham D, Yannis G. Factors affecting accident severity inside and outside urban areas in Greece. Traffic Inj Prev. 2012;13(5):458–67. doi: 10.1080/15389588.2012.661110. [DOI] [PubMed] [Google Scholar]
- 30.Fridstrom L, Ifver J, Ingebrigtsen S, Kulmala R, Thomsen LK. Measuring the contribution of randomness, exposure, weather, and daylight to the variation in road accident counts. Accid Anal Prev. 1995;27(1):1–20. doi: 10.1016/0001-4575(94)e0023-e. [DOI] [PubMed] [Google Scholar]
- 31.Jones B, Janssen L, Mannering F. Analysis of the frequency and duration of freeway accidents in Seattle. Acci Anal Prev. 1991;23(4):239–5. doi: 10.1016/0001-4575(91)90003-n. [DOI] [PubMed] [Google Scholar]
- 32.Nofal FH, Saeed AA. Seasonal variation and weather effects on road traffic accidents in Riyadh city. Public Health. 1997;111(1):51–5. doi: 10.1038/sj.ph.1900297. [DOI] [PubMed] [Google Scholar]
- 33.Andrey J, Yagar S. A temporal analysis of rain-related crash risk. Accid Anal Prev. 1993;25(4):465–72. doi: 10.1016/0001-4575(93)90076-9. [DOI] [PubMed] [Google Scholar]
- 34.Khattak A, Kantor P, Council F. Role of adverse weather in key crash types on limited-access: roadways implications for advanced weather systems.Transportation Research Record. J Transport Res Board. 1998;1621:10–9. [Google Scholar]
- 35.Ranandeh Kalankesh L, Mansouri F, Khanjani N. Association of Temperature and Humidity with Trauma Deaths. Trauma Mon. 2015;20(4):23403. doi: 10.5812/traumamon.23403. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Eisenberg D. The mixed effects of precipitation on traffic crashes. Accid Anal Prev. 2004;36(4):637–47. doi: 10.1016/S0001-4575(03)00085-X. [DOI] [PubMed] [Google Scholar]
- 37.Caliendo C, Guida M, Parisi A. A crash-prediction model for multilane roads. Accid Anal Prev. 2007;39(4):657–70. doi: 10.1016/j.aap.2006.10.012. [DOI] [PubMed] [Google Scholar]
- 38.Chang LY, Chen WC. Data mining of tree-based models to analyze freeway accident frequency. J Safety Res. 2005;36(4):365–75. doi: 10.1016/j.jsr.2005.06.013. [DOI] [PubMed] [Google Scholar]
- 39.Edwards JB. Weather-related road accidents in England and Wales: a spatial analysis. J Transport Geography. 1996;4(3):201–12. [Google Scholar]
- 40.Andreescu MP, Frost D. B. Weather and traffic accidents in Montreal, Canada. Climate Res. 1998;9(3):225–30. [Google Scholar]
- 41.Hermans E, Brijs T, Stiers T, Offermans C. The impact of weather conditions on road safety investigated on an hourly basis. Washington, DC: Proceedings of the 85thAnnual meeting of the Transportation Research Board; 2006. [Google Scholar]
- 42.Baker CJ, Reynolds S. Wind-induced accidents of road vehicles. Accid Anal Prev. 1992;24(6):559–75. doi: 10.1016/0001-4575(92)90009-8. [DOI] [PubMed] [Google Scholar]
- 43.Levine N, Kim KE, Nitz LH. Daily fluctuations in Honolulu motor vehicle accidents. Accid Analy Preven. 1995;27(6):785–96. doi: 10.1016/0001-4575(95)00038-0. [DOI] [PubMed] [Google Scholar]
- 44.Brijs T, Karlis D, Wets G. Studying the effect of weather conditions on daily crash counts using a discrete time-series model. Accid Anal Prev. 2008;40(3):1180–90. doi: 10.1016/j.aap.2008.01.001. [DOI] [PubMed] [Google Scholar]
- 45.Hu Z, Rao KR. Particulate air pollution and chronic ischemic heart disease in the eastern United States: a county level ecological study using satellite aerosol data. Environ Health. 2009;8:26. doi: 10.1186/1476-069X-8-26. [DOI] [PMC free article] [PubMed] [Google Scholar]