From the all variables in Table 1, those affecting the incidence of fatal traffic accidents were considered as explanatory variables. Given the highly invalid data with respect to distance from the nearest police station, crash longitude coordinate, crash latitude coordinate, road shoulder width, road length, road width, this study does not consider these variables in further analyses. There were also some identifying variables in Table 1, namely: crash seri, crash serial, officer code, road name, police station, road beginning, road end, vehicle system, vehicle system ID, vehicle company, vehicle parent company, vehicle plaque number, vehicle plaque serial, driver first name, driver last name, driver national ID, driving license ID, passenger first name, passenger last name, passenger national ID, pedestrian first name, pedestrian last name, pedestrian national ID, which were used either for producing unique linkage ID in order to combining different databases or to finding out whether the databases had been correctly combined. In addition scene status, driver reaction and driver injury type were removed from simple and multiple analyses because these variables are kind of outcome. Table 2 offers explanatory variables summary. From a total 208,828 crashes recorded in crash database, 2,237 (1.07%) were fatal crashes. The details about all other explanatory variables is presented in table 2. In the case of defining modified levels for a variable, which already have been provided in Table 1, the statistics are described based on the modified levels.
[Table 2 here]
Analysis of the General Model
Estimating the adjusted odds ratio change for each explanatory variable, Table3 shows each factor’s estimated effect through the final dataset. The results are provided based initial model and final model, in which non-significant variables have been removed from multiple analysis.
[Table 3 here]
Passenger and pedestrian involving in a crash
In Table 3 review, crashes with passenger were 4.95 times and crashes with pedestrian were 2.60 times more prone to fatal crashes. Presence of passengers may reduce attention to the driving task and exert direct or indirect psychological pressure to drive in a less safe way. In the same vein, it can be assumed that presence of a passenger may lead to increased stress and thus reduced driving performance (19, 20). In addition, pedestrian are highly likely to be more vulnerable as compared to other road users because they are actually less protected comparing to the occupants within closed vehicles. The relatively high vulnerability of pedestrians to traffic accidents in metropolitan areas is consistent with the results of international research (21-23).
Crash-level variables
The odds ratios of day factor (limited to the weekend and weekday categories), zone type view obstacles, crash position, road surface, road geometric design, vehicle factor and road repairing status were not significant in resulting fatal crashes at a 95% confidence interval.
Night time followed by twilight/ dawn time was riskier than day time (the odds of fatal crashes being at least 1.48 times greater). This may be held supported by the fact that there is high traffic volume during daytime, which prevents drivers from driving at high speed. On the other hand, driving during a daytime provides a better visual perception, more time to distinguish barriers and react consequently. These conditions leads the drivers to be more cautious and better prepared to take necessary measures to reduce the risk of severe crash. This findings are reported with a number of previous researches (24-26).
Comparing to clear/cloudy weather, the odds of fatal crashes increased by 1.32 times during rainy weather. Meanwhile, snowy weather was 65% less prone to a fetal crash. Foggy/stormy/dusty and clear/cloudy weather conditions were equally likely to lead to fatal crashes. Although the number of road collisions on snowy and rainy days is inevitably higher than on clear and cloudy days, and driving on these days is more dangerous due to limited visibility and tire adhesion, drivers drive more carefully and at lower speeds. In addition, most people avoid unnecessary travels or postpone it to another time. For these reasons, the available documents suggest that less severe traffic accidents (property damage or injury) increase on snowy days and more severe ones (fatality) increase on these days (27-29).
As in previous researches (30, 31), the results indicate that roads without specific traffic control is a serious road features with high odds of resulting in fatality given a collision occurs. Absence of intersection control could lead to higher possibility of fatal crashes (1.40 time more). Intersection control can enforce drivers to compliance with traffic control. As a salient example, after detecting a vehicle proceed inside the intersection with not yielding the right of way, the officer can stop and issue a citation for the noncompliant driver. Such targeted enforcement increase legitimacy not only among offenders, but also among others who observe or hear about these activities.
The line marking showed a significant effect. To elaborate, compared to crash locations with no line marks, broken lines were 1.36 times more likely to induce fatal crashes. Subsequently, single solid lines and double solid lines were even more critical than a broken line: they were 1.54 and 2.21 times more likely to lead to fatal crashes, respectively. This could be related to the fact that double solid lines mark the boundaries of each way on two-way roads where the risk of a head-on collision and consequently death is much higher on these roads (32).
In terms of road material, asphalt roads are ~ 2 times more likely to result to fatal crashes when compared to sand/clay roads. Drivers are less cautious and alert to their performance especially regarding speed controlling when they are driving on asphalt roads since these road types possess more proper situations relative to other road types. This finding is in line with exiting literature (33).
Consistent with existing studies (34, 35), findings from present study indicate that crashes happening in non-residential areas are found to exacerbate the crash outcome more than other areas (being 2.15 times more fatal). While driving in non-residential areas, drivers are more likely to engage in risky driving behaviors since they usually do not perceive a critical situation in non-residential areas.
Crash severity analysis based mechanisms revealed that involving vulnerable road user crashes are associated to more severe ones. Since they are directly exposed to impact, that leads them to succumb to death and increase the fatality chance (36, 37).
The results also revealed that, it was 13% more likely to die in presence of human factor in causation of a road traffic crash. A comprehensive study conducted by Treat et al. (38) showed that the human factor (namely: hasty driving, ignoring the traffic regulations, fatigue and drowsiness, and etc.) was the lone cause of 57% of accidents and the secondary cause of more than 90%. Consistent finding was obtained in other studies (39, 40).
When dealing with judiciary causation factors:
In terms of first cause, except need for more training, simultaneity of need for more training combined with irresponsibility and other multiple factors played a critical role in increasing the odds of a fatal crash as compared to irresponsibility solely. Failure of organs was almost same as irresponsibility in resulting in fatal crashes. Policy makers have applied numerous measures to ameliorate the severity of traffic crashes, the very epitome of which could be speed cameras and police surveillance (41). Having said that, pedagogical approaches planned for drivers are another way to cultivate more safe drivers by letting them know about traffic safety and improving their driving skills (42). Other studies have assessed the effect of driver’s training on driver’s performance (43, 44).
Considering prior cause showed that other factors namely: fatigue and drowsiness, lack of skill in diagnosing traffic situation, slippery or tarred road surface and etc. could increase the odds of dyeing in a road traffic crash by 50%. To elaborate, based on the result of available studies, it is believed that, after drunken driving, drowsiness is the second most prominent cause for 20-30% vehicle accidents (45, 46). However, many experts believe that this is only a conservative estimate and the true contribution of fatigue and sleepiness to vehicle accidents may be higher (47). Sleepiness is a component of sleep in the circadian rhythm of sleep and wakefulness. Drowsiness leads to driving automobile accidents because it can impair performance and ultimately lead to the inability to deal with falling asleep behind the wheel. Although sleeping is the most effective way to reduce drowsiness, sometimes it is unavoidable particularly for professional drivers to continue driving for some reasons like shift work (48). Accidents caused by fatigue and drowsiness are often severe and have a significant financial burden in addition to catastrophic personal consequences. Therefore, researchers have proposed effective solutions to reduce this problem, including educational activities, changes in behavior, and changes in the environment (47).
Talking about direct causes, it can be inferred that delay in sighting was the only cause that significantly increased (1.35 time) the odds of a fatal crash as compared to irregulation. Delayed vision could be due to whether drivers’ mental health disorders, particularly adult attention-deficit/hyperactivity disorder (ADHD) or their visual impairment. Symptoms of ADHD namely lack of focus, hyperfocus and disorganization have been proven to be related with crash severity (49, 50). Studies have proposed that impulsiveness and visual inattentiveness are the main contributions to the severity of car accidents in patients with ADHD (51). In addition, therapies that mitigate ADHD symptoms translate into more safe driving behavior and accordingly decreased rates of serious crash severity (52, 53). Supplementary to this, it is prove that drivers with poor visual acuity are more prone to road traffic crashes (54). A large study on drivers’ visual field in the USA revealed that those suffered from binocular visual field loss reported two times more crash rates as compared to those without such loss (55). Estimated number of crashes contributed to visual field defect was reported to be 36% higher. With regard to protanopic color vision defect, it is not allowed for people with theses defect to obtain a commercial license since they cannot diagnosis red traffic lights (56, 57). In the light of above-mentioned descriptions, mental stability and visual functioning of drivers seems to have inevitable results in road traffic crashes and would be fundamental issue that needs to be taken under more consideration.
Compared to a side-swipe collision, within a head-on collision followed by a fixed object collision the odds of fatal crashes significantly increased by 3.35 times and 2.36 times, respectively. Meanwhile rear-end and T-bone collisions were almost same as side-swipe collision with respect to crash severity. Consequences of head-on collision could hurt the driver directly in numerous ways, which would exacerbate the crash outcome and even lead to fatal crashes. Head-on collisions are known to be the type of crashes with utmost level of severity and often lead to injuries and fatalities (58). Furthermore, it is reported that these types of crashes were contributing factor for approximately 25% of fatal crash in rural roads of OECD countries (59). Consistent results with regard to the fact that impacts with solid objects often result to severe crash outcome were obtained in similar studies (60-62).
As compared to Tehran, the capital city of Iran, Fras, Khuzestan and then Isfahan were accounted for the most risky provinces in Iran where about 62% of all fatal crashes had been happened in these provinces. Isfahan as Iran’s top tourist destination provides classic tourist stop on a travel itinerary from northern cities of Iran to southern tourist city of Shiraz on Fars province. In addition, these two province are attractive tourist destination for outbound visitors. Accordingly, these two provinces inasmuch as high traffic volume and different driving characteristics (risky driving behaviors, drowsy driving, high speed and etc.) exhibit higher rates with regard to crash severity and even fatality. On the other hand, in khuzestan as a Border city drivers tends to use foreign cars which lead them to drive in more speed. As it is has been shown in other studies high speed is a crucial factor in causing serious crashes. Furthermore, a greater fatalities rate in these three provinces could be contributed to the following issues: (1) emergency medical services performance. In this regard, the number of at-scene death, the number of on-transfer death and the number of in-hospital death had better to be taken under consideration, (2) unsafe roads and (3) higher rates of heavy vehicle, pedestrian and motorcycle crashes.
The results also showed that commuting area contributes to crash severity. Going in to details revealed that suburban areas were at least 3 times more likely to result in fatal crashes when compared to urban areas. It has been reported previously that crashes occurring on rural roads produce lucid trend patterns toward more severe and even fatal crashes (63). It is believed that the features of rural road such as rural drivers’ typical behaviors (less likely to wear seat belt (64) greater driving speeds (63) or stop at stop-sign intersections (65), etc.) their characteristics (more older drivers (66)) or the adversity of reaching in time medical assistance in the time of crash (67)) are leading factors to more frequent fatal crashes on rural roads.
Comparing to the main street, a crash was more likely to involve fatality when it had been occurred in expressway, main road, side road, freeway and rural road, respectively. These road types are commercial and suburban areas. Alongside line marking in afore-mentioned areas is double solid line. And, as it has already been shown in the previous results of this study, crash outcome is more severe in suburban areas and roads with double solid line.
In addition, considering shoulder condition and design of the roads, roads with unpaved shoulders and separated two-way roads contributed to higher risk. Road shoulder provides a necessary stopping lane and serves recovery for errant vehicles beforehand a potential crash occurs (68). Its omission could hence leads to more severe collisions (69, 70). Furthermore, unseparated two-way roads the same as road with double solid lines are more likely to head-on collisions that are more prone to fatality.
In completing of the crash-level variables it should be mentioned that in addition to afore-mentioned factors, coincident of multiple road defect such as signs defect, geometric defect and etc. implied to higher risk of fatal crashes (Table 3). Road defects are those where a road design element transfers ambiguous information to drivers, resulting in driver error, or where a change in the road could have reduced the likelihood of a road accident (71). Krug & Sharma (72) state that road environments that encourage risky driver behavior (e.g. by inspiring high traffic speeds) or fail to consider safety in all conditions (e.g. at night or in adverse weather conditions), increase the probability of a road accident and its severity indirectly. Hence, a road that is designed and regularly maintained in accordance with operational and functional requirements is critical in influencing drivers' perceptions and resulting to safer roads for all users (73). It has been shown that the road environment element is in a poor condition in developing countries due to worse road design as well as maintenance (74). In addition, defects of various traffic combinations requiring different infrastructure needs are commonly not observed on roads such as high-speed vehicles, heavy marketable traffic, bicyclists, pedestrians, and motorcycle users (75). However, growing rates of motorized vehicles in developing countries are outstripping the capacity of current transportation infrastructure, leading to increased accident rates and severity levels (76).
Vehicle-level variables
The following rows in Table 3 provides results regarding vehicle factor. It can be observed that vehicle safety equipment and moving direction were not associated with a fatal crash happening. On the other side, the categories highly associated with crashes involving fatality were: heavy vehicle type, vehicles of risky colors with life of fifteen years and more, vehicles with not personal regional plaque, and vehicles with maneuvers such as stopping outside of the road, sudden starting, sudden stopping, and spiral movement.
Despite heavy vehicle crashes are less frequent, these crashes are more severe to such an extent that approximately 18 percent of all fatal crashes in 2019 involve a heavy vehicles (77) . Intense exposure is the leading cause of serious injury or even death in heavy vehicle accidents. It is also noteworthy that these accidents often lead to the death of the users of the other vehicle (78).
The findings with regard to silver color in particular is in a clear contrast with results of a case-control study (79) which concluded that silver vehicles were approximately 50% less prone to serious crashes compared to while colored cars. The results of this study are biased due to not considering number of critical confounding factors such as vehicle type and personality traits of drivers. It is stated that commercial vehicles that are more likely to severe crashes are predominantly white. Secondly, there might be a relationship between driving behavior and color choice. For instance, more careful drivers may prefer silver color. In contrast in a paired case-control study (80), the authors concluded that vehicles with light color were associated with less risky collisions. Although this study tried to account for special driver and vehicle features and consider large number of confounders, it failed to consider unmeasurable or unmeasured confounders. A study by stratified induced exposure design (81) found that relative to white color, black, blue, grey, green, silver and red were associated more serious crashes and this association was even more strongest during daylight times as compared to dark or twilight times.
Consistence results exist with regard to vehicle age. The studies assessing the impact of vehicle age on car collision have found that older vehicles are more prone to be included in severe crashes (82, 83). Another study (84) indicates that for vehicles aged 15 years and older, there was about three times more risk. It is proved that older vehicles, as compared to new ones, are more probable to develop defects in terms of safety likes brake failure and tyre. On the other hand, older vehicles are less likely to have safety features. Safety equipment and its defects not only cause a crash but also may increase its intensity.
The difference between personal and commercial vehicles could be attributed to the fact that commercial vehicle are from heavy type and their drivers suffer from sleepiness and fatigue more than personal vehicles. On the other hand commercial cars are usually in highways, expressways and main roads that found to be more critical for intense crashes. In addition, unusual maneuvers such as stopping outside of the road, sudden starting, sudden stopping, and spiral movement are categorized as risky driving behaviors which have been found to be strongly linked with crash severity (85).
Driver-level variables
Table 3 also presents results about driver-level factors. Driver fault status and gender were not significant in predicting a fatal crash. The categories with the highest odds ratio of fatal crashes were: divers with non-academic education and middle income status, driver old age, no driving license, not using the seat belt, driver unconscious or lack of driving skills or violation of the law and driver misconduct other than spiral movement or over speeding.
Driver education and income both as socioeconomic status (SES), play a crucial role in traffic safety’s breakthrough. To elaborate, cognitive perception which construct the way of interpreting and understanding different situations and whether being obedient to rules or not, is in a close relationship with SES. A driver with high SES level hardly ever would be under too extremes fear and courage sense and they would perform more reasonably in a critical situations (86, 87). Beyond behavioral factors, vehicle-related and contextual features can be attributed to the exacerbated risk among individuals with low SES. These people usually suffer from monetary crisis to such an extent that they would barely remain capable of copping with their day-to-day expenses. An inevitable result of being riddled with such unaffordability would be possessing vehicle with not advanced safety equipment and lower crash-test rating. On the other hand, area properties might also have relevance, as there is a vivid difference in accessibility to hospital trauma centers between low and high-property areas. Limited access to trauma centers and specialists may increase crash severity and following mortality rate (88).
It is undoubtedly true that, nowadays, increased traffic crashes among the elderly have become pervasive among a thorough of nations all around the globe (11). Suffering from musculoskeletal disorders and slowed physical activities, older people experience more serious crash outcomes relative to middle-aged group in the case of traffic collision. Furthermore, chronic diseases the very epitome of which is osteoporosis, increase the rate of bone fracture and consequently extend hospitalization period and mortality rate in these generation. This predicament, impose an undue financial burden to the shoulders of health care system of each society by increasing medical cost. So, in order to stave off this deleterious condition and enhance traffic safety, special policies had better to be the matter of greater emphasis for this age group.
The consistent results with regard to driving license proves that unlicensed operator are more likely to be involved in a sever crash and engaged in illegal behaviors such red light running, speeding, drunken driving and not using of seatbelt. Also these groups are more prone to be at fault as compared to licensed drivers (89). Since unlicensed drivers are on the high rise (90), measures such as increasing petrol enforcement expanding the applied penalties, promoting public knowledge about the dangerous of driving without license and vehicle impoundment need to be taken at this population (91).
Turning to seat belt use, it is evident that seat belt use can considerably decrease non-fatal and fatal injuries both in front and rear seats occupants. In a study authors found that people in metropolitan and urban areas are more likely than people in rural areas to use seat belt. In addition, gross provincial product, educational level and legalization were declared to be related with use of seat belt (92).
Key results and insights
Table 4 summarize the significant factors as well as their level and safest categories contributing to fatal crashes based on general final model. For better figuratively presentation, explored risk factors and corresponding odds ratios is illustrated in figure 1.
Strength and limitation
All registry system variables are presented in Table 1, whether they was used as an explanatory variable or not. Since most of the categories are based on international classifications, they can be considered as a referral document for developing traffic crash registry system in other countries. In this study we considered unknown values as missing and replaced them by missing data management strategies. Although added values such as shoulder width, road width, road length were not included in the study due to a large error in recording information, but variables such as speed limit, road type (one-way, two-way, ...) and road type (expressway, freeway, etc.) were a very good representative of these added values and did not affect the results significantly. The longitude and latitude of crash location had a lot of missing values and could not be taken under consideration for related analysis and detecting more gangrenous segments. Considering some limitation such as defining upper and lower limit in recording the afore-mentioned quantitative variables is suggested. In addition, registry system had better be provided via some advances features such as automatically fulfillment of road length and width or shoulder width by selecting road name and type. If so, researchers would be able to use these critical information in more complicated and specialized analysis. Furthermore it worse noticing that, although comparing factors in overall analysis is good, but subgroup analysis sometimes provides better specific information such as road types with respect to fatality that may lead to decisions for giving areas. So subgroup analysis with regard to all significant identified factors is suggested for further investigation. Beyond this more specific analysis with regard to passenger and pedestrian fatalities is suggested.