Indexing crash worthiness, crash aggressivity and total secondary safety for major car brands: A case study of Iran Ali Tavakoli Kashani1, Hessam Arefkhani2 1. (Corresponding author) Academic position: Assistant Professor of civil engineering Last Degree earned: Ph.D. Affiliation institute: School of civil engineering, Iran University of Science & Technology, Tehran, Iran, Email:
[email protected] Fax: (09821) - 77240310 Telephone: (09821)- 77803100 2. Academic position: MSc. student in transportation engineering Last Degree earned: BSc Affiliation institute: School of civil engineering, Iran University of Science & Technology, Tehran, Iran, Email:
[email protected];
[email protected],
[email protected]
No funding Information available Acknowledgements The authors gratefully thank Mohammad Mehdi Besharati and Hasan Mohamdzadeh for their valuable comments and useful assistances on this study.
Indexing crash worthiness, crash aggressivity and total secondary safety for major car brands: A case study of Iran
Highlights
In this study crash worthiness, crash aggressivity, and total secondary safety of the 20 most prevalently used passenger car brands of Iranian fleet were indexed. Kia and Suzuki showed the best performance. Sepand, Pride, and Peykan were recognized as the three most dangerous brands.
Abstract A growing body of research is being conducted all over the world to evaluate and compare the safety impacts of different car brands. This issue has also received a considerable attention among the safety experts in Iran, where the number of road fatalities is around 16500 lives per year. This study aims at indexing crash worthiness, crash aggressivity and total secondary safety of the 20 most prevalently used passenger car brands of Iranian fleet. For this purpose, the data pertaining to 167,759 crashes and 335,518 drivers involved in those crashes that occurred in Iran from 2009 to 2012 were used. Binomial Logistic Regression model was applied in order to define the above-mentioned indices based on driver’s injury severity level. The results showed that most of the domestic brands have a poorer performance than the foreign ones in all three indices. Furthermore, it was revealed that Kia and Suzuki have a better performance and Sepand and Pride have a poorer performance compared to the other brands. Our findings might also be integrated with the findings of other similar studies around the world. This could be helpful for car manufacturers both in Iran and across the world in order to benchmark the best performing car brand in vehicle safety domain and improve the designing of their own car brands. Key words: Crash worthiness, crash aggressivity, total secondary safety, car brand, Iran, driver’s injury severity
1
Introduction and literature review
Safety impacts of motor vehicles and motorcycles have always been a main concern for car designers, traffic safety experts as well as road users. This topic has recently attracted a considerable attention from the governments and people in developing countries such as Iran. This unanimous demand for improving car safety raises the need for performing a scientific analysis of crash safety among the most commonly used products of different car brands in order to identify their real performance in traffic safety. Broughton in 1996 studied the theoretical basis for comparing the crash record of car models. He focused on secondary –not primary- safety or crashworthiness provided by each model (car model) and the driver injury severity. On the one hand, primary safety refers to the features and systems that help avoid crashes, such as good brakes, good steering, etc. On the other hand, secondary safety refers to such features as seat belts, vehicle crash worthiness level, and so on, which help reduce the severity of a crash. He investigated two methods and their indices; The Folksam method and its index R which defines as the ratio of Driver casualties in model 1 cars to Driver casualties in other cars. The DoT method and its main index D which defines as number of drivers who are injured in some kind of crashes to a number of drivers who are involved in those special kind of crashes. The author proposed an alternative method based on the cost of crash casualties [1]. In a study on the effect of vehicle model and driver’s behavior on crash risk, Wenzel and Ross (2005), calculated the so-called “combined risk” for each vehicle model. The combined risk was defined as the sum of risk-to-driver and risk-to-other-driver. More specifically, the risk-to-driver was defined as the risk of colliding vehicle, while the risk-to-other-driver is the risk of counterpart vehicle. In their paper, the word risk as a technical term was defined as driver deaths per year per million registered vehicles. The authors focused on driver in order to eliminate the variations in the number of passengers among vehicle types and models which could affect the results. These types of risks are called unconditional risks. On the other hand, conditional risk is calculated by dividing the number of driver deaths to police-reported crashes in a certain category of vehicle. One should notice that conditional risk addresses the “crash worthiness” of a vehicle, not its crash avoidance characteristics. This is one of the limitations of conditional risk. The authors also tried to discover the effects of driver behavior on vehicle safety by grouping the drivers into several levels according to their age, place of driving and illegal driving experience. Wenzel and Ross argued that the driver behavior might not have a significant effect on vehicle safety [2]. Stigson et al. (2006), studied the injury risk of different types of vehicles in single and two vehicle crashes. This was done through the measurement of the variation of velocity and acceleration in the crash phase. Using the concept of conservation of momentum, they then determined the injury risk of occupants. The results showed that injury risk depends on the types of vehicles involving in the crash and some other external factors such as road characteristics and angle of crash [3]. Newstead et al. (2007), introduced an index called “Total Secondary Safety” for light passenger vehicles of Australia. In that study, multiplication of individual injury risk to the estimated injury severity was considered as the total secondary safety index. They obtained injury risk and injury severity based on descriptive statistics from the database and through a Logistic Regression Model. Driver’s age, driver’s gender, collision characteristics and speed limit were the four variables considered in the study. They also calculated the total secondary safety of different vehicle models and market groups [4]. In another study, the possible correlation between crash worthiness and crash aggressivity (from a previous study) with total secondary safety was investigated. The results showed a correlation between crash worthiness and total secondary safety. Furthermore, no significant correlation was found between crash aggressivity and total secondary safety [5].
Huang et al. (2011), studied crash incompatibility between vehicles by indexing crash worthiness and crash aggressivity of different vehicle types. They did so by a Bayesian hierarchical ordered logistic model. The results showed that vehicle size and mass is not necessarily correlated with vehicle overall safety [6]. In another study which was conducted by Huang et al., crash worthiness and crash aggressivity of major car brands of Florida, USA, were indexed and 23 car brands were ranked [7] based on driver’s injury severity level [8,7] using a Bayesian hierarchical ordered logistic model. As another part of the research, Huang et al. put each brand as reference and compared other brands with the reference brand. They concluded that Volvo, Cadillac and Infiniti have better performances than the others [9]. Recently, Huang et al. (2016), have investigated the correlation between vehicle type (different brands), occupant injury and vehicle damage. For this purpose, they used a Bayesian bivariate hierarchical ordered logistic model. They compared different car brands by defining two indices: Occupant Protectiveness (OP) and Vehicle Protectiveness (VP). The results showed that Cadillac, Volvo and Lexus are the best and Kia and Saturn are the worst brands of Florida (USA) regarding to OP and VP [10]. The literature review reveals that safety impacts of car brands and models is still an ongoing topic; few
studies have been conducted on the role of vehicle type in traffic safety and fewer about vehicle brand in the world. In addition, during the last three decades, Iran government have adopted restriction policies on car importation, aiming to support domestic car manufacturing industries. Consequently, almost 88% of car brands in Iran are domestic brands or brands which are manufactured inside the country. Therefore, there has been a long controversy among Iranian experts over the safety impacts of different car brands, mainly comparing domestic car models with foreign ones. Some experts believe that foreign brands have better primary and secondary safety than domestic brands and argue that the car importation limitation policy is responsible for a considerable proportion of crashes and fatalities in Iran; while the others state that there are no meaningful differences between safety impacts of domestic and foreign car brands in Iran. In this regard, the current study tries to investigate the safety impacts of different car brands available in Iranian fleet. This is done through defining three indices: Crash Worthiness Index (CWI), Crash Aggressivity Index (CAI) and Total Secondary Safety Index (TSSI). Then all the frequently used car brands are compared with a reference brand by the three indices to see whether or not there are any significant differences among their secondary safety impacts. One of the main merits of this study is the utilization of local data. The findings can be used in macroeconomic policies about car importation and domestic car manufacturing industries in Iran. Moreover, there are few studies in which safety impacts of a vehicle type/brand on both the traffic flow (CAI) and the ability of the vehicle to protect its occupants (CWI) have been investigated simultaneously (TSSI). Rest of the paper is organized as follows: in section 2 data preparation procedure and the analysis methodology are described. Analysis results and discussion are provided in section 3. Finally, the fourth section presents the conclusion of the paper.
2
Methodology
2.1 Crash data In this research the data pertaining to traffic crashes occurred in Iran from 2009 to 2012 were used. The above-mentioned crash database which is maintained by Iran traffic police, contains 1,360,415 crash records and 2,407,196 driver records. After cleaning the database, the final dataset was prepared with the following considerations:
Only passenger car- passenger car (PC-PC) and passenger car- motorcycle (PC-M) crashes were included in the study dataset. Two-vehicle crashes can provide an obvious identification of CWI, CAI and TSSI of different brands compared to multivehicle crashes. Furthermore, in PC-PC crashes the circumstances for both drivers are approximately the same and thus the bias is minimized in the results. The passenger car- motorcycle (PC-M) crashes data were used to verify the model. Head-on, rear end and angle collisions are the most prevalent types of collisions that occur in twovehicle crashes. The database was exactly reviewed and other kind of collision types were excluded for the final dataset. Injury severity is significantly dependent on incompatibility among several vehicle types such as heavy vehicles and passenger cars [6]. In addition, given the limited crash data, it seems infeasible to simultaneously analyze the effects of both vehicle type and brand on the crash severity [7]; Therefore, only passenger cars were considered in the current study. The focus is on injury severity of vehicle driver as the seating position of driver is fixed in comparison to other passengers. Variable of area type (i.e., rural or urban) was considered as a surrogate factor to account for differences in mean speed of vehicles in rural and urban roads. Again this is one of the four control variables. It is worth mentioning that variation in mean speed of vehicles along a road due to inconsistent geometric design can cause major safety issues which leads to a higher crash rate [11]. Since there are significant differences in injury severity of males and females in traffic crashes [12], driver’s gender was included in the model as a control variable.
Table 1 presents the most frequently used passenger car brands in Iran which were involved in crashes and considered in the current study. Also their raw frequencies as well as percentages of each car brand have been provided in Table 1. According to this table Peugeot 405, Pride and Peykan constitute 75.1% of car models of the study database. It should be mentioned that Pride (one of the most famous productions of SAIPA that has recently raised a lot of complaints from safety experts in Iran) was set as the reference group for car brands because it has the most exposure. In summary, for indexing CW, CA, and TSS of 20 major passenger car brands of Iranian fleet, after a tedious cleaning process, only two-vehicle crashes, passenger cars, collision types of head-on, rear end and angle, from the crashes occurred between 2009 and 2012 were extracted from the original database. Moreover, the focus is on driver’s Injury Severity (IS) rather than passenger’s injury severity. The data filtering yields to a dataset of 167,759 crashes and 335,518 driver records.
Table 1- Descriptive statistics of car brands in the study dataset [Table 1 in here] 2.2 Model development Driver’s injury severity1 was considered as the dichotomous dependent variable (code 0= Not injured and code 1=Dead/injured) and logistic regression models were developed. Because one of the main assumptions of ordinal logistic regression model (OLRM), which is proportional odds [13], was not satisfied for the dependent variable in the current study, this method was not employed. As a second step, a multinomial 1
In the available database, there are only three levels for driver’s injury severity: the dead, the injured, and the uninjured.
logistic regression model (MLRM) was applied. However, this method was again rejected because of few sample size in some levels. Finally, a binomial logistic regression model (BLRM) was developed. Since the number of dead drivers for some brands were too low, we decided to combine death and injury levels and consider them as a single level. A BLRM predicts the probability of an observation that falls into one of the two possible categories of a dichotomous dependent variable with one or more independent variable(s). The independent variables can be either continuous or categorical [14,15]. As a brief explanation for the model, let’s start from logistic function. Logistic function is
f ( z)
1 1 e z
If z 1 X1 2 X 2 ... k X k (X: independent variable, k: index of independent variable) then
f ( z)
1 1 e
( i X i )
Forasmuch as the output of a logistic function is between 0 and 1, one can write
P(result 1| X 1 , X 2 ,..., X k )
1 1 e
( i X i )
Or
P( X )
1 1 e
( i X i )
Where X is a shortcut notation for the collection of variables X 1 through X k . Logit transformation, denoted as logit P( X ) , is
P( X ) ] 1 P( X )
logit P ( X ) ln[ and by using some algebra, we would have
logit P( X ) i X i where P ( X )
1 1 e
( i X i )
.
P( X ) odds of X (the probability of happening over the probability of not 1 P( X ) happening) and thus logit P( X ) is the log odds of X. In a logistic regression model is background log It should be noted that
odds and i represents the change in log odds. Five independent variables including Passenger car brands (the intended independent variable), driver gender, collision type, area type, and driver age were considered in this study. If one sets the reference
group of each of the independent variable as 0, then each i represents the change of log odds ratio for that special independent variable (e.g. driver gender) compared to the reference group. Since the reference group has been set 0, then the ratio above is purely the log odds of our considered independent variable compared to the reference category (e.g. male drivers compared to female drivers). For more information please refer to [14]. 2.2.1 Control variables Clearly driver’s injury severity depends not only on vehicle incompatibility but also on other external factors such as driver age, gender, area type and so on. Therefore, to better identify the effects of vehicle features on driver’s IS, one has to control the other external factors. Control variables considered in the current study are summarized in Table 2.
Table 2- Description of control variables [Table 2 in here] In summary, driver’s IS was considered as the dependent variable, passenger car brands as the independent variable, and the other four factors as the control variables. 2.2.2 Crash worthiness index To index crash worthiness, driver’s IS of the colliding car due to collision with its counterpart car is considered. So the dependent variable is set 0 if the colliding car driver is not injured or killed, and 1 if the colliding car driver is injured or killed. 2.2.3 Crash aggressivity index For indexing crash aggressivity, driver’s IS of the counterpart car in the crash is considered. Thus, the dependent variable is set 0 if the driver of the counterpart car is not killed or injured and 1 if the driver of the counterpart car is injured or killed. 2.2.4 Total secondary safety index In order to index total secondary safety for a car brand, driver’s IS for both the colliding car and its counterpart is considered. If both drivers are injured or killed, then the dependent variable is set equal to 1; and 0, otherwise.
3
Results and discussion
Three different models were run to investigate the Crash worthiness, Crash aggressivity as well as Total secondary safety, separately. The results of each model are presented in the following subsections. 3.1 Crash worthiness The results of the BLRM for indexing crash worthiness of different car brands are shown in Table 3Error! Reference source not found..
Table 3- crash worthiness by car brands [Table 3 in here] As can be seen from Table 3, all car brands except MVM are statistically significant at 90% level of confidence. Based on the odds ratios, the best ranked brand is Kia (South Korea) in comparison to Pride (Iran) by odds of 0.117. It means that the odds of being injured or killed for a Kia’s driver is approximately 82% lower than the odds of a Pride’s driver. Based on this table, Sepand (Iran) is the only model that
performed worse than Pride (odds ratio=1.22) according to this index. In addition, Southeast Asian brands seem to have a great performance in crash worthiness than Iranian brands. Control variables regarding to crash worthiness are summarized in Table 4.
Table 4- control variables of crash worthiness model [Table 4 in here] According to Table 4, all the control variables are significant at 90%. It is worth noting that all the control variables in this subsection and in the next subsections are about the characteristics of the colliding car driver, not the counterpart driver. As an example, the odds of a male driver to be injured or killed in a crash is 30% lower than a female driver. This might be attributed to a combination of behavioral and physiological differences of men compared to women [12,16]. By skimming driver’s age in Table 4, it reveals that under 19 years old age group has the highest odds of injury or death in traffic crashes. Like most of the world countries, it is illegal to drive with the age of under 19 in Iran. This can explain why this age group was recognized as the most dangerous age group; because they do not have any driving license. Other odds are logical too. 3.2 Crash aggressivity Results of logistic regression of Crash aggressivity for different car brands are summarized in Table 5. Unfortunately, 9 brands out of 20 are not significant at a 90% level of confidence (maybe due to few samples of these brands in the crash aggressivity model). The best brand is again Kia (South Korea). Among Iranian brands, the best ones are Rio and Peugeot 206, respectively and the worst one is again Sepand which ranked as 19th. Nissan (Japan) placed at the bottom of the ranking table. Similar to crash worthiness, the Southeast Asian brands also received the top ranks in Crash aggressivity index. Furthermore, unlike crash worthiness, there are 8 brands with odds greater than 1 for the crash aggressivity. It means that all of these 8 brands have a poorer performance than Pride. For instance, the odds of a counterpart driver to be killed or injured in a crash with Nissan is almost 1.4 times than the same driver (or car brand) colliding with a Pride.
Table 5-crash aggressivity by car brands [Table 5 in here] In addition, control variables used in the crash aggressivity model are shown in Table 6. As one can see from Table 6, if the colliding car driver is a male, then the odds of its counterpart driver to be killed or injured would rise to 1.47 times of when the colliding car driver is a female. This might be attributed to the fact that male drivers drive more risky and faster than female drivers; which in turn could result in more severe injuries for the counterpart driver. However, this argument needs further research. [Table 6 in here]
Table 6- control variables of crash aggressivity model Again under 19 years old age group has been recognized as the most dangerous age group (odds=2.12). Rural crashes are much more dangerous than urban crashes. Moreover, head-on collisions are more dangerous than rear end and angle collisions.
3.3 Total secondary safety In Table 7, the results of logistic regression for total secondary safety of car brands are presented. According to Table 7, the best TSSIs are for Kia (South Korea) and Suzuki (Japan), respectively and the worst is for Sepand (Iran). Like before, Southeast Asian brands are at the top. All the brands except Sepand performed better than Pride in TSSI.
Table 7-total secondary safety by car brands [Table 7 in here] TSSI control variables are provided in Table 8. As can be seen, the odds of both the colliding car driver and its counterpart driver being killed or injured in a crash, when the gender of the colliding car driver is male, is 1.27 times than the driver of the colliding car be a female. As expected, rural and head-on crashes are much more dangerous. Like the CW and CA indices, the most dangerous age of a driver is under 19.
Table 8- control variables of total secondary safety model [Table 8 in here] 3.4 All together As for previous sections, Kia and Suzuki have always been among the top 5 brands while Sepand has been among the lowest. This fact along with an over view, are summarized in Figure 1Error! Reference source not found.. Note that for a special brand the lower the odds ratio, the better that brand. As seen from Figure 1 a logical comparison between three indices of each brand can be given. A probable case for a brand is that its TSSI be somewhere between its CWI and CAI but there is no need to be so, as can be seen in the figure. For instance, Sepand and MVM have a higher TSSI than their CAI and CWI whereas Proton has a lower TSSI than its CAI and CWI. It is also worthwhile to mention that for a single brand, TSSI is somewhat the resultant of its CWI and CAI but the absolute values of these indices for each brand calculated in comparison to Pride. In other words, TSSI, CWI, and CAI are ratios. These ratios depend not only on the brand itself but also on the reference, Pride; so, TSSI can be more or less than CAI and CWI. It is obvious from Figure 1 that for most brands TSSI is between CAI and CWI. [Figure 1 will be inserted here]
Figure 1- comparing CWI, CAI and TSSI by the values of odds ratios In Figure 2 three indices have been brought to see whether there is a correlation among them or not. After conducting a Pearson correlation test significant correlation between TSSI and CWI was found (Pearson correlation=0.726 significant at 0.01 level or CI=99%). Also, no significant correlation was observed between TSSI and CAI (Pearson correlation=0.406 significant at 0.08 level or CI=92%). These results are compatible with those of Newstead et al. and Huang et al. [9,4].
[Figure 2 will be inserted here]
Figure 2- CWI, CAI, and TSSI correlations
All brands were ranked according to the three previously defined indices and presented in Table 9. As stated previously, Kia is the best and Sepand is the worst brand in comparison to Pride.
Table 9- ranking table [Table 9 in here]
Using once again the data of Table 9, Figure 3 is drawn. In this figure the area divided into four parts; Bottom left are brands which their CWI and CAI are among the top 10 brands, top right are brands which their CWI and CAI are among the last 10 brands. Top left and bottom right are heterogeneous brands which have either good performance in crash aggressivity or in crash worthiness.
[Figure 3 will be inserted here]
Figure 3- overall ranks
As can be seen in Figure 3, Kia, Mazda, Suzuki, Hyundai, Citroen and BMW are good brands in both CWI and CAI. Southeast Asian brands grab 4 of 6 good-performance brands. Sepand, Peykan, Peugeot 405, Renault, and Pride have poor performance regarding to CAI and CWI. Brands that placed around the bisector passing through the origin are homogenous explaining that the more they close to the origin, the better they would be and vice versa. Brands that placed around the perpendicular diameter are heterogeneous explaining that the more they close to the right bottom corner, the better their CA and vice versa. Before plotting Figure 3, what did we expect? Logically we should have expected brands to be placed around the diameter perpendicular to bisector (Figure 4). Because a car which is good at CWI, generally is heavier and more resistant to collision impact. Thus, it should have damaged its counterpart car more; that means poorer CAI performance. But the observed trend does not look like to our expectancy and more surprisingly somehow exactly in an opposite way. We can see that most of the brands placed around the bisector passing through the origin which means the better one brand in CWI, the better that brand in CAI and vice versa. It can be a symptom that “for good brand designers, both the traffic safety and the car safety are of the same importance”. [Figure 4 will be inserted here]
Figure 4- expected transmittal of brands 3.5 A challenge In order to verify the models, we decided to re-run the models this time including motorcycles too. Motorcycle (treated as a brand in this step) is expected to have the worst crash worthiness index and the best crash aggressivity index due to its powerful engine and high speed, the rider’s lack of protective gears, low stability and small vehicle size, respectively [17-19]. Thus, it can be used to examine the reliability of
our models. For this purpose, the relevant dataset was prepared which included 241,538 crash records and 483,076 driver records. The ranking results from the new model are summarized in Table 10.
Table 10- ranking table including motorcycle [Table 10 in here] The results of new models confirm the results obtained from previous models. The change in the new rankings of car brands compared to their previous ranks might be due to their exposures. Obviously, if the exposure of a brand is high, the crash probability of that brand with motorcycle will raise. Since the rider of motorcycle is not safe in case of a crash with a passenger car, then CAI, CWI, and TSSI of the car will change. A change in CAI, CWI, and TSSI means a change in the ranking of that car.
4
Conclusion and summary
Safety impacts of vehicles and motorcycles are a growing concern among the safety experts and road users across the world, especially in developing countries like Iran. Very few previous studies have addressed the safety level of different car brands and this study tried to bridge this gap. In this regard, the safety impacts of 20 most prevalently used car brands in Iran were modeled and compared by estimating the three indices of Crash Worthiness (CW), Crash Aggressivity (CA), and Total Secondary Safety(TSS). Logistic Regression results revealed that most of the car brands have a better performance than Pride. Results also indicated that Sepand (Iran), Pride (Iran), and Peykan (Iran) are the three most dangerous brands which are driven on roads of Iran. In addition, Kia (a South Korean car brand), and Suzuki (a Japanese brand) showed the best performance in two of the three defined indices (CAI, CWI, and TSSI). In order to validate the results, in the next step, separate models were developed by including the motorcycle crashes and the results of the new models confirmed the original results. Findings of this study can be utilized in macroeconomic decision making about car importation and domestic car manufacturing industries in Iran. The results might also be helpful for car manufacturers both in Iran and across the world in order to benchmark the best performing car brand in vehicle safety domain and improve the designing of their own car brands. In addition, the employed methodology (BLRM) is easily applicable for similar safety studies. This study also uncovered the potential of crash databases for analyzing the effects of vehicle brand/type on the safety of vehicle occupants. Finally, because there were only three injury severity levels (i.e. fatality, injury, and no injury) in the study database, the authors were imposed to combine fatality and injury levels and consider them as a single level. It was because of small fatal sample size (sometimes 0) for some brands. This is a limitation of the current study. Obviously if there were more detailed data for injury levels such as minor injury and severe injury, the results would be more precise. References 1. Broughton J (1996) The theoretical basis for comparing the accident record of car models. Accident Analysis & Prevention 28 (1):89-99 2. Wenzel TP, Ross M (2005) The effects of vehicle model and driver behavior on risk. Accident Analysis & Prevention 37 (3):479-494 3. Stigson H, Ydenius A, Kullgren A Variation of crash severity and injury risk depending on collisions with different vehicle types and objects. In: 2006 international IRCOBI conference on the biomechanics of impact, Madrid, Spain, 20-22 September, 2006.
4. Newstead S, Watson L, Cameron M (2007) An index for rating the total secondary safety of vehicles from real world crash data. 51st Annual Proceedings, Association for the Advancement of Automotive Medicine 5. Newstead SV, Keall MD, Watson LM (2011) Rating the overall secondary safety of vehicles from real world crash data: the Australian and New Zealand Total Secondary Safety Index. Accident; analysis and prevention 43 (3):637-645. doi:10.1016/j.aap.2010.10.005 6. Huang H, Siddiqui C, Abdel-Aty M (2011) Indexing crash worthiness and crash aggressivity by vehicle type. Accident; analysis and prevention 43 (4):1364-1370. doi:10.1016/j.aap.2011.02.010 7. Huang H, Hu S, Abdel-Aty M (2014) Indexing crash worthiness and crash aggressivity by major car brands. Safety Science 62:339-347. doi:10.1016/j.ssci.2013.09.002 8. Xie Y, Zhao K, Huynh N (2012) Analysis of driver injury severity in rural single-vehicle crashes. Accident; analysis and prevention 47:36-44. doi:10.1016/j.aap.2011.12.012 9. Huang H, Hu S, Zheng L (2014) Crash-level analysis on passenger cars’ total secondary safety. International Journal of Crashworthiness 19 (6):613-623. doi:10.1080/13588265.2014.931540 10. Huang H, Li C, Zeng Q (2016) Crash protectiveness to occupant injury and vehicle damage: An investigation on major car brands. Accident; analysis and prevention 86:129-136. doi:10.1016/j.aap.2015.10.008 11. Luque R, Castro M (2016) Highway Geometric design consistency: speed models and local or global assessment. International Journal of Civil Engineering 14 (6):347-355 12. Ulfarsson GF, Mannering FL (2004) Differences in male and female injury severities in sport-utility vehicle, minivan, pickup and passenger car accidents. Accident Analysis & Prevention 36 (2):135-147. doi:10.1016/s0001-4575(02)00135-5 13. Gujarati D (2014) Econometrics by example. Palgrave Macmillan, 14. Gail M, Krickeberg K, Samet J, Tsiatis A, Wong W (2007) Statistics for biology and health. Springer, 15. Lund ALM (2016) Binomial logistic regression using spss. https://statistics.laerd.com/spsstutorials/binomial-logistic-regression-using-spss-statistics.php. Accessed 10/26/2106 2016 16. Fredette M, Mambu LS, Chouinard A, Bellavance F (2008) Safety impacts due to the incompatibility of SUVs, minivans, and pickup trucks in two-vehicle collisions. Accident Analysis & Prevention 40 (6):19871995 17. Anvari Mb, Tavakoli Kashani A, Rabieyan R (2017) Identifying the most important factors in the at-fault probability of motorcyclists by data mining, based on classification tree models. International Journal of Civil Engineering. doi:10.1007/s40999-017-0180-0 18. Kashani AT, Rabieyan R, Besharati MM (2014) A data mining approach to investigate the factors influencing the crash severity of motorcycle pillion passengers. Journal of safety research 51:93-98 19. Kashani AT, Rabieyan R, Besharati MM (2016) Modeling the effect of operator and passenger characteristics on the fatality risk of motorcycle crashes. Journal of injury and violence research 8 (1):35
Table 11- Descriptive statistics of car brands in the study dataset
Brand Peugeot 405 Pride (as reference) Peykan Samand Peugeot 206 Renault Hyundai Citroen Toyota Rio Nissan Mazda Daewoo Sepand Mercedes-Benz Kia BMW Suzuki MVM Proton Total
2
No. of dead drivers 105 108 36 17 6 5 0 4 2 2 2 0 1 2 0 0 0 0 1 0 291
No. of injured drivers 2125 4204 1218 365 305 215 44 62 55 64 37 28 58 83 19 6 10 7 28 14 8947
No. of uninjured drivers 107834 102036 34323 23593 14683 7590 5727 5109 4271 3693 3421 3035 2561 1710 1525 1408 1054 1049 897 761 326280
No. of each model in the crash database 110064 106348 35577 23975 14994 7810 5771 5175 4328 3759 3460 3063 2620 1795 1544 1414 1064 1056 926 775 335518
% of each model in the crash database 32.80% 31.70% 10.60% 7.15% 4.47% 2.33% 1.72% 1.54% 1.29% 1.12% 1.03% 0.91% 0.78% 0.53% 0.46% 0.42% 0.32% 0.31% 0.28% 0.23% 100.00%
Manufactured in Iran2 Iran Iran Iran Iran France South Korea France Japan Iran Japan Japan South Korea Iran Germany South Korea Germany Japan China Malaysia
Based on the Iranian culture and industrial atmosphere of Iran’s industries, considering some models of a special international brand as a separate brand is logical. Obviously for other international brands, those models that exist in Iran’s fleet are of a concern.
Table 12- Description of control variables
Control variables Collision type
Area type Driver gender
Driver age
Description Head-on =0 (reference) Rear end=1 Angel=2 Urban=0 (reference) Rural=1 Female=0 (reference) Male=1 65=3
Descriptive statistics Head-on:9.4% Rear end:40.1% Angel:50.5% Urban:80.7% Rural:19.3% Female:10.8% Male:89.2% 65:1.9%
Table 13- crash worthiness by car brands
Number of cars_CW=335,518 Vehicle model (pride (Iran) as reference) Kia (South Korea) Suzuki (Japan) Hyundai (South Korea) Mazda (Japan) BMW (Germany) Nissan (Japan) Citroen (France) Mercedes-Benz (Germany) Toyota (Japan) Samand (Iran) Rio (Iran) Proton (Malaysia) Peugeot 405 (Iran) Peugeot 206 (Iran) Daewoo (South Korea) Renault (France) MVM (China) Peykan (Iran) Pride (Iran) Sepand (Iran)
B
S.E.
Wald
df
Sig.
-2.150 -1.621 -1.585 -1.360 -1.274 -1.264 -1.076 -1.074 -1.048 -.918 -.824 -.748 -.671 -.657 -.549 -.312 -.262 -.173
.410 .380 .152 .191 .319 .162 .125 .232 .135 .054 .126 .271 .027 .060 .133 .071 .191 .033
1159.895 27.516 18.177 108.071 50.728 15.977 60.579 73.763 21.406 60.492 286.446 42.927 7.585 629.603 120.227 16.941 19.418 1.876 26.836
19 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
.000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .006 .000 .000 .000 .000 .171 .000
.195
.114
2.958
1
.085
Exp(B)
.117 .198 .205 .257 .280 .283 .341 .342 .351 .399 .439 .473 .511 .518 .578 .732 .770 .841 1.000 1.216
95% C.I.for EXP(B) Lower Upper .052 .094 .152 .176 .150 .206 .267 .217 .269 .359 .343 .278 .485 .461 .445 .638 .529 .788
.260 .417 .276 .373 .522 .388 .436 .538 .457 .444 .561 .806 .539 .583 .750 .841 1.119 .898
.973
1.519
Rank
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Table 14- control variables of crash worthiness model
Control variables of CW Collision type (head-on collision as reference) rear-end collision angle collision Area type (urban as reference) rural collisions Sex (Female as reference) Male age (between 25 and 45 years old as reference) 65 Constant
B
95% C.I.for EXP(B) Lower Upper
S.E.
Wald
df
Sig.
Exp(B)
-1.649 -.967
.031 .026
2876.80 2794.74 1377.08
2 1 1
0.000 0.000 .000
.192 .380
.181 .361
.204 .400
.690
.023
864.02
1
.000
1.993
1.904
2.087
-.370
.033
.000 .000 .000 .153 .655 .000 0.000
.648
.736
.138 .029 .029 .073 .038
1 4 1 1 1 1 1
.691
.674 .041 .013 .321 -2.043
128.97 43.09 23.77 2.04 0.20 19.40 2838.41
1.962 1.042 1.013 1.378 .130
1.496 .985 0.958 1.195
2.572 1.102 1.071 1.590
Table 15-crash aggressivity by car brands
Number of cars_CA=335,518 Vehicle model (pride (Iran) as reference) Kia (South Korea) MVM (China) Mazda (Japan) Daewoo (South Korea) Suzuki (Japan) Rio (Iran) Hyundai (South Korea) Citroen (France) Peugeot 206 (Iran) BMW (Germany) Mercedes-Benz (Germany) Pride (Iran) Renault (France) Toyota (Japan) Peykan (Iran) Peugeot 405 (Iran) Samand (Iran) Proton (Malaysia) Sepand (Iran) Nissan (Japan)
B
S.E.
Wald
df
Sig.
-.478 -.370 -.298 -.254 -.242 -.201 -.169 -.086 -.083 -.077 -.021
.227 .271 .143 .143 .246 .119 .097 .095 .059 .217 .168
59.419 4.449 1.860 4.335 3.140 .967 2.862 3.056 .813 2.005 .124 .016
19 1 1 1 1 1 1 1 1 1 1 1
.000 .035 .173 .037 .076 .326 .091 .080 .367 .157 .725 .901
.001 .056 .062 .069 .098 .149 .316 .339
.076 .099 .036 .026 .043 .224 .136 .093
.000 .321 2.947 6.830 5.214 .442 5.407 13.173
1 1 1 1 1 1 1 1
.992 .571 .086 .009 .022 .506 .020 .000
Exp(B)
.620 .691 .742 .776 .785 .818 .845 .918 .920 .926 .979 1.000 1.001 1.058 1.064 1.071 1.103 1.160 1.372 1.403
95% C.I.for EXP(B) Rank Lower Upper .397 .406 .561 .586 .484 .648 .699 .761 .821 .605 .704
.967 1.176 .983 1.027 1.272 1.032 1.021 1.106 1.032 1.418 1.362
.863 .871 .991 1.017 1.014 .749 1.051 1.169
1.161 1.283 1.142 1.128 1.200 1.799 1.792 1.685
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Table 16- control variables of crash aggressivity model
Control variables of CA Collision type (head-on collision as reference) rear-end collision angle collision Area type (urban as reference) rural collisions Sex (Female as reference) Male age (between 25 and 45 years old as reference) 65 Constant
B
95% C.I.for EXP(B) Lower Upper
S.E.
Wald
df
Sig.
Exp(B)
-1.683 -.954
.031 .026
2986.00 2923.06 1353.98
2 1 1
0.000 0.000 .000
.186 .385
.175 .366
.198 .405
.658
.023
796.91
1
.000
1.931
1.844
2.021
.384
.043
.000 .000 .000 .000 .010 .161 0.000
1.350
1.596
.130 .028 .029 .076 .048
1 4 1 1 1 1 1
1.468
.753 .136 -.074 .106 -3.111
80.93 72.64 33.73 24.00 6.68 1.96 4158.51
2.123 1.146 0.928 1.112 .045
1.647 1.085 .878 .958
2.738 1.210 0.982 1.291
Table 17-total secondary safety by car brands
Number of cars_TSS=335,518 Vehicle model (pride (Iran) as reference) Proton (Malaysia) Kia (South Korea) Suzuki (Japan) Hyundai (South Korea) Mazda (Japan) Nissan (Japan) Citroen (France) Renault (France) Toyota (Japan) Samand (Iran) Peugeot 206 (Iran) MVM (China) Daewoo (South Korea) Rio (Iran) Peugeot 405 (Iran) BMW (Germany) Mercedes-Benz (Germany) Peykan (Iran) Pride (Iran) Sepand (Iran)
B
S.E.
Wald
df
Sig.
-16.088 -2.135 -1.000 -.825 -.782 -.708 -.548 -.429 -.397 -.342 -.329 -.323 -.269 -.245 -.178 -.127 -.116 -.048
1371.583 1.002 .710 .254 .357 .271 .222 .171 .228 .092 .121 .505 .272 .228 .050 .451 .338 .066
65.33 0.00 4.54 1.98 10.59 4.81 6.81 6.07 6.28 3.04 13.77 7.41 0.41 0.98 1.15 12.49 0.08 0.12 0.54
19 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
.000 .991 .033 .159 .001 .028 .009 .014 .012 .081 .000 .006 .522 .322 .283 .000 .779 .732 .464
.513
.230
4.96
1
.026
Exp(B)
.000 .118 .368 .438 .457 .493 .578 .651 .672 .710 .720 .724 .764 .783 .837 .881 .891 .953 1.000 1.671
95% C.I.for EXP(B) Lower Upper 0.000 0.017 .091 .267 .227 .289 .374 .465 .430 .593 .568 .269 .448 .500 .759 .364 .459 .838
0.842 1.480 .720 .920 .839 .894 .911 1.051 .851 .912 1.948 1.301 1.224 .924 2.134 1.728 1.084
1.063
2.624
Rank
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19
Table 18- control variables of total secondary safety model
Control variables of TSS Collision type (head-on collision as reference) rear-end collision angle collision Area type (urban as reference) rural collisions Sex (Female as reference) Male age (between 25 and 45 years old as reference) 65 Constant
B
95% C.I.for EXP(B) Lower Upper
S.E.
Wald
df
Sig.
Exp(B)
-2.470 -1.510
.064 .045
1933.47 1509.37 1103.49
2 1 1
0.000 0.000 .000
.085 .221
.075 .202
.096 .241
1.253
.043
853.64
1
.000
3.500
3.218
3.807
.241
.086
.005 .020 .001 .659 .782 .604 0.000
1.075
1.505
.256 .057 .055 .153 .092
1 4 1 1 1 1 1
1.272
.852 .025 -.015 .080 -4.026
7.84 11.63 11.08 0.19 0.08 0.27 1914.83
2.344 1.026 0.985 1.083 .018
1.419 .917 .884 .802
3.872 1.147 1.097 1.463
Table 19- ranking table
Brand Kia (South Korea) Suzuki (Japan) Hyundai (South Korea) Mazda (Japan) BMW (Germany) Nissan (Japan) Mercedes-Benz (Germany) Citroen (France) Toyota (Japan) Samand (Iran) Rio (Iran) Proton (Malaysia) Peugeot 405 (Iran) Peugeot 206 (Iran) Daewoo (South Korea) Renault (France) MVM (China) Peykan (Iran) Pride (Iran) Sepand (Iran)
CW Rank 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
CA Rank 1 5 7 3 10 20 11 8 14 17 6 18 16 9 4 13 2 15 12 19
TSS Rank 1 2 3 4 15 5 16 6 8 9 13 14 10 12 7 11 17 18 19
Table 20- ranking table including motorcycle
Brand Kia (South Korea) Suzuki (Japan) Hyundai (South Korea) BMW (Germany) Mazda (Japan) Nissan (Japan) Citroen (France) Mercedes-Benz (Germany) Toyota (Japan) Samand (Iran) Proton (Malaysia) Rio (Iran) Peugeot 206 (Iran) Peugeot 405 (Iran) Daewoo (South Korea) Renault (France) MVM (China) Peykan (Iran) Sepand (Iran) Motorcycle (different brands)
CW Rank 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
CA Rank 2 3 6 7 4 15 10 5 9 17 8 11 13 18 14 16 12 20 19 1
TSS Rank 1 2 4 5 3 7 6 11 10 13 16 9 15 14 8 12 17 18 19
Comparing CWI, CAI and TSSI By Values Peugeot 405 (Iran) Peugeot 206 (Iran)
Peykan (Iran)
Proton (Malaysia)
Samand (Iran)
MVM (China)
Renault (France)
Hyundai (South Korea)
Suzuki (Japan)
BMW (Germany)
Citroen (France)
Kia (South Korea)
Toyota (Japan)
Mercedes-Benz (Germany)
Rio (Iran)
Sepand (Iran) Daewoo (South Korea)
CAI
Nissan (Japan) Mazda (Japan)
CWI
TSSI
Figure 1- comparing CWI, CAI and TSSI by the values of odds ratios
Correlations By values 1.80 1.60 1.40 1.20 1.00 0.80 0.60 0.40 0.20 0.00
CWI
CAI
TSSI
Figure 2- CWI, CAI and TSSI correlations
CAI
Overall Ranks 21 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 0
Nissan (Japan)
Proton (Malaysia)
Sepand (Iran)
Peugeot 405 (Iran)
Samand (Iran)
Mercedes-Benz (Germany)
Peykan (Iran) Toyota (Japan)
Pride (Iran)
Renault (France) BMW (Germany) Peugeot 206 (Iran)
Citroen (France) Hyundai (South Korea)
Suzuki (Japan)
Rio (Iran)
Daewoo (South Korea)
Mazda (Japan) MVM (China) Kia (South Korea) 1
2
3
4
5
6
7
8
9
10
11
12
CWI
Figure 3- overall ranks
13
14
15
16
17
18
19
20
21
CAI
Expected Transmittal of Brands 21 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 0
Nissan (Japan)
Proton (Malaysia)
Samand (Iran) Toyota (Japan)
Peykan (Iran)
Peugeot 405 (Iran)
Mercedes-Benz (Germany)
Sepand (Iran)
Renault (France) Pride (Iran)
BMW (Germany) Hyundai (South Korea)
Peugeot 206 (Iran)
Citroen (France)
Rio (Iran)
Suzuki (Japan) Daewoo (South Korea) Mazda (Japan) MVM (China)
Kia (South Korea) 1
2
3
4
5
6
7
8
9
10
11
12
13
CWI
Figure 4- expected transmittal of brands
14
15
16
17
18
19
20
21
