Traumatic and Nontraumatic Driving Accidents Due to Dry Spells in Northern Iran: a Time Series Analysis

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Traumatic and Nontraumatic Driving Accidents Due to Dry Spells in Northern Iran: A Time Series Analysis

a ENAYATOLLAH HOMAIE RAD, SHAHROKH YOUSEFZADEH-CHABOK,ZAHRA MOHTASHAM-AMIRI,NAEIMA KHODADADI-HASANKIADEH,ALI DAVOUDI-KIAKALAYEH, LEILA KOUCHAKINEZHAD-ERAMSADATI, AND ANITA REIHANIAN Guilan Road Trauma Research Center, Guilan University of Medical Sciences, Rasht, Iran

(Manuscript received 2 October 2017, in ﬁnal form 11 July 2018)

ABSTRACT

Driving in rain is very dangerous, and drivers seem not to drive properly whenever it rains. In such situations, the risk of driving increases on rainy days, especially after a prolonged dry period. This would be a problem for drivers steering on slippery roads. In this study, the effect of dry spells on road trafﬁc accidents and resulting mortality in Rasht, Iran, located in the southern margin of the Caspian Sea, in a 3-yr period from 21 March 2014 to 19 March 2017 was examined using time series patterns. The results of the study showed that the ﬁrst day after a dry spell had the greatest impact on road accidents and resulting injuries and deaths. It was also found that with increased length of a dry spell, the risk of accidents and related deaths and injuries rises.

1. Introduction 2004; Yousefzadeh et al. 2008). Traumatic accidents are those accidents that lead to injury and mortality, Road accidents are among the most significant causes contrary to nontraumatic accidents. Traumatic accidents of mortality and severe physical and financial damage. have many social, economic, and health consequences, Studies have shown that driving accidents are estimated some of which cannot be compensated at all (Mayou and to be the ninth-leading cause of death in the world, and Bryant 2003; Peden et al. 2004). As a result, identifying all they are expected to climb to third place by 2020 (WHO effective factors has a significant role in the reduction of 2016). Changes and phenomena such as snow, blizzards, traumatic accidents. In recent years, the role of weather fog, avalanches, frost, and slippery surfaces play a sig- conditions in road accidents has attracted the attention of nificant role in road accidents. In recent years, in- many researchers. Although weather may not be the main vestigating weather changes and their induced impacts cause of road accidents, it is undoubtedly one of the major has become very important in designing roads (Keay environmental factors. People often assume that the and Simmonds 2005). The study of weather variables weather conditions cannot be considered as a barrier is of particular importance since the risks of slipping to driving unless traveling is impossible due to adverse on the road’s surface can be examined and controlled. weather and road status. Despite this assumption, var- Therefore, accurate data on road and weather conditions ious studies have been done on the relationship between can be provided to repair and maintenance crews and atmospheric conditions and transportation (Carson and other authorities in the field of transportation safety in Mannering 2001; Eisenberg 2004; Eriksson and Lindqvist order to gather accurate information about road safety 2002; Keay and Simmonds 2006). (Golob and Recker 2003; Homaie Rad et al. 2017). Rainfall is considered the most significant weather Road trauma occurs due to a variety of environmen- phenomenon regarding increased numbers of road ac- tal, behavioral, and technological factors (Eisenberg cidents (Jaroszweski and McNamara 2014). Rain causes losses through a combination of several physical effects a Current affiliation: Social Determinants of Health Research in a driving environment. These negative effects include Center, Guilan University of Medical Sciences, Rasht, Iran. the loss of friction between tires and road and the reduced visibility caused by rain on the windshield and Corresponding author: Anita Reihanian, anita_reihanian@ rainwater spraying from other vehicles’ tires (Brijs et al. yahoo.com 2008). Studies have shown that the occurrence of rainfall

DOI: 10.1175/WCAS-D-17-0102.1 Ó 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses). Unauthenticated | Downloaded 09/28/21 02:33 AM UTC 724 WEATHER, CLIMATE, AND SOCIETY VOLUME 10 increases the intensity and frequency of road accidents occupancy and standard deviation of volume detected (Híjar et al. 2000; Lankarani et al. 2014; Töro˝ et al. at the downstream station and the 5-min coefficient of 2009). In addition to these factors, foggy, dusty, and variation in speed at the station closest to the crash, all polluted air also reduces a driver’s visibility and in- during the 5–10 min prior to the crash occurrence, along creases the risk of death and injury due to driving acci- with the rain index, were found to affect the crash event dents (Lankarani et al. 2014). In 2012, the effects of most meaningfully (Abdel-Aty and Pemmanaboina 2006). the interactions among crash occurrence, mountainous Combining rainwater with dust and road surface con- freeway geometry, real-time weather, and traffic data tamination can also be dangerous. Despite investigations were investigated using the Bayesian logistic regression about the impacts of rainfall on injuries and deaths due to technique. The outcomes suggested that the inclusion of driving accidents, and evidences about the high volume of roadway geometrics and real-time weather with records accidents in the first rainfall in the world’s mass media from an automatic vehicle identification (AVI) system in (Jokela et al. 2009), the effect of the first day of rainfall on the context of active traffic management systems was vital, traffic accidents was not examined clearly in quantitative specifically for roadway sections characterized by moun- studies. In this research, the effect of a dry spell on road tainous terrain and adverse weather (Ahmed et al. 2012). accidents and related mortality in Rasht was studied Cheng et al. (2017) examined the impact of weather using time series patterns. conditions on motorcycle crash injuries and found that the models with serial and severity variations of 2. Methods and materials parameters had superior fit as well as the ability for accurate crash prediction. The conclusions from the pa- This was a descriptive and analytical cohort study rameter estimates from the five models were as follows: conducted as a retrospective time series. In time series 1) an increase in the air temperature reduced the possi- studies, data of mean or total frequency were sorted bility of a fatal crash but had a reverse impact on crashes by day. The study aimed to investigate the effects of of other severity levels; 2) humidity in the air was not ob- precipitation, the first day of rainfall following a dry served to have an expected or strong impact on crashes; spell, and the mean horizontal visibility on mortality and 3) the occurrence of rainfall decreased the possibility and injury, as well as total road accidents. In this study, of crashes for all severity levels (Cheng et al. 2017). the threshold of rainfall was precipitation of 0.1 mm The relationship between traffic flow features and (Anagnostopoulou et al. 2003; Ines and Hansen 2006). It crash risk under adverse weather conditions on a high- was recorded by meteorological stations as rainfall, and way has been identified. This study developed single less than that was registered as nonoccurrence of rainfall. crash risk prediction models for different kinds of weather conditions (Xu et al. 2013). 3. Weather status of the studied area The model estimation results indicated that the traffic flow characteristics contributing to crash risk were dif- Guilan Province, the wettest area of Iran, has generally ferent across different weather conditions. In the clear- temperate and humid weather. Except for a small area in weather condition, the upstream occupancy, the speed the southern part of Guilan, the mean annual precipitation variance at downstream detector station, and the speed is 1337.5 mm (from 1956 to 2010), which is much higher difference between upstream and downstream stations than that of the whole country (255 mm; 1960–2010). were related to the real-time crash risk. And the in- The distribution of rainfall in different parts of the fluences of the upstream occupancy on crash risk were province is not the same. Anzali station has the highest found to be different in sparse and heavy traffic. The amount of precipitation during the statistical period, crash risk during rainy weather was influenced by the with 1830.5 mm (from 1951 to 2010). The precipitation rainfall intensity, the upstream occupancy, and the speed decreases when moving from Anzali toward the west difference between upstream and downstream stations. and east, as well as from northern to southern parts of The visibility, the standard deviation of occupancy at the the province. downstream station, and the speed difference between Seasonal distribution of rainfall in the province dem- upstream and downstream stations contributed to the onstrates that autumn, with a total of 42.1% of annual crash risk in reduced-visibility weather. precipitation, and spring, with 14.42%, have the highest Abdel-Aty and Pemmanaboina (2006) calibrated a and lowest amounts of rainfall, respectively. Summer crash-likelihood prediction model using real-time traffic rainfall occurs significantly in Guilan Province, com- flow variable and rain data potentially related to crash pared to other weather zones; 24.21% of annual rainfall occurrence via archive loop detector and rain records takes place in this season, due to warming ground sur- and old crash data. In this study, the 5-min average face and entering high moisture from the sea to land.

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TABLE 1. Deﬁnitions, units, and data sources of variables added in the study.

Variables Definition (unit) Data source Type of variable Rain after 6 days Occurrence of the first rainfall after a 6-day IRIMOa Explanatory independent dry spell (dummy variable) Precipitation Daily amount of rain (mm) IRIMO Explanatory independent Speed average The daily average speed of vehicles passed Speed cameras Explanatory independent 2 each road (km h 1) Small trucks Daily traffic of small trucks contained (total Speed cameras Explanatory independent number) Medium trucks Trucks less than 10 m in length, buses and Speed cameras Explanatory independent vans with less than 20-person capacity (total number) Buses Buses with more than 20-person capacity Speed cameras Explanatory independent (total number) Heavy trucks Trucks more than 10 m in length (total Speed cameras Explanatory independent number) Deaths and injuries Daily number of deaths until 20 days after Road Police data Dependent variable (count data) accidents and injuries related to the accidents (total number) Road traffic accidents Daily number of accidents reported to the Road Police data Dependent variable (count data) police (total number) a Islamic Republic of Iran Meteorology Organization.

4. Collecting data 3) Other variables. In addition to weather and crashes, several other control variables, including the mean The data required for this study were collected from daily road traffic collected by the Iran Road Main- three sources. tenance and Transportation Organization, total daily 1) Data on meteorological variables. These data include traffic, daily traffic of small trucks, medium trucks, the mean daily precipitation, mean daily horizontal heavy trucks, and buses, and the mean daily speed of visibility, and mean daily precipitation provided at the cars, were entered into the model. A table of vari- Rasht Sardar Jangal International Airport meteoro- ables is shown above (Table 1). logical station within the 3-yr period from 21 March a. Model 2014 to 20 March 2017. The station has appropriate and updated technical equipment for measuring and The model used in this study was the time series recording data. accident and mortality model, designed by Tegnér and 2) Data on the number of road accidents. Data related to Loncar-Lucassi (1997). Similar models were used and the crashes, the number of deaths due to road accidents, developed in many studies (Dadashova et al. 2014; Hoek and the number of injuries. Given that the entrances of et al. 2002; Hughes et al. 2014; Keay and Simmonds Rasht include roads of Rasht-Kuchesfahan, Rasht- 2005; Quddus 2008). Fouman, Rasht-Anzali, Rasht-Jirdeh, and Rasht-Sangar, b. Determination of the first day of rainfall after data on these roads were collected in coordination dry spell with the Iranian Traffic Police (RAHVAR) and entered into Excel software. Using the pivot table com- The occurrence of the first rainfall after a 6-day dry spell mand, the number of crashes, deaths, and injuries was was considered as the first day of rainfall in this study. determined and sorted for each day of the year. The Thus, the first day of rainfall occurred if the precipitation maximum distance between each crash location and was 0 mm for a period of at least 6 days before starting to the Rasht Sardar Jangal International Airport mete- rain. The region’s and asphalted roads have enough time orological station was 10 km. The reason for choosing to get dry and absorb environmental pollution, dust, and roads in this 10-km radius was that the weather exhaust fumes during this 6-day period. In other words, conditions of different regions may vary over a day the combination of rainfall and pollutions makes cars with the increase in the number of cities and the susceptible to slipping on asphalted roads and strongly geographic extent. However, the weather condition in increases the likelihood of accidents. Therefore, a dummy the selected geographical area is relatively similar to variable was determined, and the first day of rainfall was that of Rasht Sardar Jangal International Airport. assigned as one and the other days of the year as zero.

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FIG. 1. Precipitation of rain at Rasht International Airport from March 2014 to March 2017. The vertical axis is rain in mm, and the horizontal axis is the months from 2014 to 2017. c. Statistical analysis between 21 March 2014 and 20 March 2017. As shown in Fig. 1, the rainfall was highest from October to March. Because of having a count-type-dependent variable, Figure 2 shows the mortality and injuries due to driving Poisson regression was used to estimate the models. accidents over the study period. As shown in the fig- Poisson regression model is the best estimator to estimate ure, mortality and injuries in May, June, August, and regression models with count-form-dependent variables September were higher than those in other months. (Verbeek 2008). The P values and standard deviations Table 2 shows the study results on the regression (SD) were used to find the relationship between in- model of the study time series in relation to factors af- dependent and dependent variables, and the R2 statistic fecting the deaths and injuries caused by driving acci- was applied for examining goodness of fit. The signifi- dents. The left column of the table contains the name of cance level in this study was 95%. the variables, then the coefficients, standard deviation, P value, and lower and upper limits. As shown in the 5. Sensitivity analysis table, the precipitation variable coefficient was 0.308 The sensitivity analysis aims to show how a change in after a 6-day dry spell, which was statistically significant. the number of days of a dry spell affects driving accidents The precipitation had no significant relationship with and related injuries and deaths. In this study, the effect of mortality and injuries due to road accidents. The pre- the first rainfall after 3-, 4-, 5-, 7-, 8-, 9-, and 10-day periods cipitation coefficient (mm) was 20.05, which was not in the crashes, injuries, and mortality was also analyzed, statistically significant. The study showed that the increase and the adjusted regression coefficients were compared. in mean daily speed raised mortality and injuries due to In addition, the effect of the second day of rainfall after a traffic accidents (coefficient 5 0.004). The number of 6-day dry spell on mortality, injuries, and road accidents deaths and injuries caused by accidents was higher when was studied, and the results were analyzed. larger trucks traveled more frequently (the coefficient for ordinary and very large trucks was 0.0002 and 0.002, respectively). However, the number of crashes in buses 6. Results and small trucks was lower than that in other motor Figure 1 shows the recorded precipitation at the Rasht vehicles (the coefficient for small trucks and buses Sardar Jangal International Airport meteorological station was 20.000 34 and 20.001, respectively).

FIG. 2. Number of deaths and injuries due to road trafﬁc accidents on Rasht metropolitan roads from March 2014 to March 2017. The vertical axis is the number of deaths and injuries, and the horizontal axis is the months from 2014 to 2017.

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TABLE 2. Time series analysis of factors affecting the deaths and injuries caused by driving accidents.

Variable Coefﬁcient Standard error P value Lower limit Upper limit Rain after 6 days 0.308 122 0.091 800 5 0.001 0.128 196 3 0.488 048 Precipitation 20.051 33 0.038 103 6 0.178 20.126 007 0.023 356 Speed average 0.004 852 0.000 699 5 0.000 0.003 481 4 0.006 223 Cars: Base Small trucks 20.000 34 0.000 105 3 0.001 20.000 55 20.000 14 Regular trucks 0.000 254 0.000 088 7 0.004 0.000 080 4 0.000 428 Buses 20.001 68 0.000 829 5 0.042 20.003 31 20.000 058 Big trucks 0.002 09 0.000 561 9 0.000 0.000 988 5 0.003 191 Constant variable 0.907 993 0.046 601 1 0.000 0.816 656 5 0.999 33

Table 3 shows time series regression model with the of dry spell with regard to the dependent variable of dependent variable of number of road accidents. As number of road accidents. As shown in the figure, the shown in the table, the occurrence of rainfall after a variable coefficient rises with increasing dry spell length. 6-day dry spell increased the number of traffic accidents The coefficients and lower and upper limits for 3, 4, 5, 7, 8, significantly (coefficient 5 0.178). In addition, the con- 9, 10, and 11 days of dry spell were 0.547 26 (20.572 91 founder variables of the study and the mean daily speed and 0.166 743), 20.099 727 (20.305 81 and 0.230 036), had no relationship with the number of road accidents. 0.126 765 (20.019 222 and 0.272 751), 0.2641 (0.0986 and 0.4296), 0.3483 (0.1702 and 0.5265), 0.3358 (0.1429 and 0.5287), 0.3914 (0.1795 and 0.6033), and 0.4330 (0.2005 7. Sensitivity analysis and 0.6655), respectively. For analyzing sensitivity, the length of the dry spell Also, after entering the control variables, the co- was initially changed, and the mean daily speed, daily efficient of the second day of rainfall following a 6-day precipitation, and type of vehicles traveling in the road dry spell was investigated. The coefficient for the de- axes were analyzed using control variables based on the pendent variable of death and injuries was 20.004 179, regression pattern. which was not statistically significant. (upper limit 5 Figure 3 shows changes in the coefficients for the first 0.2483; lower limit 520.2567; P statistic 5 0.974), and the rainfall after 3–11 days of dry spell and their relationship coefficient of this variable for the dependent variable of with deaths and injuries due to driving accidents. As accidents was 20.045 70, which was also not statistically shown in the table, the variable coefficient increases significant (upper limit 5 0.1685; lower limit 520.2599; when the length of the dry spell until the eighth day, significant 5 0.676). with a maximum level on the eighth day of the dry spell (coefficient 5 0.5213; lower limit 520.317; upper 8. Discussion limit 5 0.724). The coefficient and lower and upper limits for 3, 4, 5, 7, 8, 9, 10, and 11 days of dry spell were This study showed that an increase in road accidents 0.133 (20.005 and 0.273), 0.122 (20.037 and 0.281), and related mortality and injuries occurred on the first 0.207 (0.033 and 0.382), 0.401 (0.207 and 0.594), 0.521 day after at least 4 days of dry spell more than other (0.317 and 0.724), 0.505 (0.285 and 0.724), 0.474 (0.228 rainy days. Therefore, it is important to consider a way and 0.720), and 0.500 (0.230 and 0.769), respectively. to control cars and avoid accidents on the first rainy day Figure 4 shows the variation of coefficients in 3–12 days after a dry spell. When tires hit the roads, their movement

TABLE 3. Time series analysis of factors affecting the number of accidents.

Variable Coefﬁcient Standard error P value Lower limit Upper limit Rain after 6 days 0.178 785 0.077 541 5 0.021 0.026 806 0.330 763 4 Precipitation 0.004 532 0.002 473 6 0.067 20.000 316 0.009 380 1 Speed average 0.000 849 0.000 563 1 0.131 20.000 254 0.001 953 Cars: Base Small trucks 20.000 16 0.000 090 3 0.073 20.000 338 0.000 015 4 Regular trucks 0.000 104 0.000 077 0.175 20.000 046 0.000 255 4 Buses 20.000 42 0.000 719 3 0.563 20.001 826 0.000 993 6 Big trucks 0.000 547 0.000 505 2 0.279 20.000 443 0.001 536 9 Constant variable 1.426 54 0.034 408 6 0.000 1.359 101 1.493 98

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FIG. 3. Changes in the coefficients for the first rainfall after FIG.4.AsinFig. 3, but for the coefficients’ relationship with 3–11 days of dry spell and their relationship with deaths and number of accidents. injuries due to driving accidents. The vertical axis is the coefficient of dry spell, and the horizontal axis is the day after a dry spell. In another study in Australia, researchers found that rainfall with a maximum delay of 5 days would increase driving crashes by 8.9%–9.9%. However, a dry spell along the road creates a frictional force. Vibration on the with more than 5 days has a doubled effect (17.9%– surface of the road creates a friction that helps the tires 19.5%) (Keay and Simmonds 2006). to touch the road through changing the shape of the tire. In a study in 2004, Eisenberg studied the impact of dry When it rains, water on the road will reduce friction. As spells on traffic accidents and related deaths and injuries the tires move on a wet surface, water in the small pits in the United States. As with the present study, it was on the road surface will effectively make the surface found that with increased length of a dry spell, the smooth. As a result, natural heat and friction are reduced, likelihood of road accidents and deaths and injuries in- resulting in a surface that is more slippery than the dry one. creases as well, so the risk of death from road accidents for Meanwhile, if there is much water on the road, including the first rainfall after more than 21 days of dry spell is water in potholes, it may cause even more friction be re- 30 times more than after 1 day of the spell, and it is al- duced. In these cases, the car’s tires can completely lose most twice as likely to cause injuries and traffic accidents. contact with the surface of the road through a thin surface Eisenberg (2004) studied the occurrence of road acci- of water. This condition is called hydroplaning and can dents on the second day of rainfall and showed that the be very dangerous (Black and Jackson 2000; Chan et al. number of road accidents would be significantly reduced 2010). There are also materials on the road that can lead the following day by increasing precipitation. More at- to loss of friction when water is added. For example, tention needs to be paid to the hazards of driving in rainy most dry roads contain a layer of bitumen, rubber, oil, weather after a dry spell. Some of the most important sulfur, and exhaust fumes from cars. When it starts to ways to decrease crashes in this situation are listed here. rain, these materials can combine with water and create a very slippery, fatty layer. This layer is still not cleaned 1) Driving at a safe speed. Driving at a safe speed is from the surface of the road in the early hours after the important because it increases the chance of the driver rain, and the combination of these materials will cause reacting. Rainwater reduces the friction between tires excessive slipping on the roads. Therefore, the number of and the road. If driving is slow, the braking opportu- traffic accidents and related deaths and injuries at these nity increases, and the possibility of overturning, hours is higher than that at other hours and even on other diversion, and crashes is reduced. Therefore, drivers days of rainfall (Eisenberg 2004). Afterward, the amount should be informed about the hazards of the first of slipping is reduced when the road is cleaned, resulting moments of rainfall, and they should be asked to drive in fewer deaths and injuriesduetoroadcrashes at a slower speed and stay farther behind other (Eisenberg 2004). vehicles at those hours (Boyle and Mannering 2004; A study in India showed that in the case of dry roads Edwards 1999; Haglund and Åberg 2000). over a period of 1–5 days, the probability of accidents 2) Avoiding sudden braking. Sudden braking may cause in rainy days would increase by 23.3%, and if the length slipping. When the roads are slippery, excessive of a dry spell exceeds 5 days, the average number of spinning of tires will make them lose all friction, road accidents increases 115.7% (Mondal et al. 2008). and a car crash is more possible (Pisano et al. 2008).

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