Accident victim characteristics and identification of key parameters for compensation in Indian context

ABSTRACT

Road accidents generate substantial personal and national losses. Road accidents reduce victims' and their families' productivity, which slows the nation's economic growth. The victim or victim's heir must be compensated for the mishap's losses. It is important to understand traffic accident victims' concerns before calculating and rewarding a suitable compensation. This study examines traffic accident victims' issues. In this paper, the interviews were conducted in two phases: a pilot survey of seven experts and 38 victims/relatives of victims, based on their opinions, the questionnaire was revised, and the final interviews were conducted in the second phase, which included 126 road accident victims and 27 expert interviews. This paper summarises the results of the second phase along with its statistical interpretations. This paper also emphasises traffic accident victims' debilitation and daily issues. Government and local authorities often announce compensation, although they rarely consider a victim's socioeconomic situation. The arbitrarily set amount may provide immediate relief, but it ignores traffic accident victims' socio-demographics. This study will highlight those aspects that will enable policymakers quickly and efficiently decide remuneration for each individual.

KEYWORDS:

• Road safety
• Road accident victims
• compensation
• socio-economic background
• AHP

1. Introduction

Road accidents have become a global problem and have been widely addressed in various reports and research. However, the detrimental impacts of road accidents on victims and their families are often overlooked. In developed countries, the fatality rate due to road accidents is fairly low and the mechanism to compensate the victims is comparatively robust, on the other hand, the fatality rate due to road accidents in developing countries is quite rampant and the compensation mechanism is quite abject. In view of the same, this paper is an attempt to look into the various parameters that are/or should be used to provide compensation to road accident victims. In this paper, experts from different domains were selected, which included road safety experts medical experts, rehabilitation center pundits, economists, sociologists, Insurance experts, and trauma center experts, Lawyers, and judges were also interviewed during the surveys. These experts were interviewed so that the parameters could be identified that should be used for deciding the compensation amount and weighing the parameters of road accident compensation. To evaluate the data and determine the components’ ranking, Analytic Hierarchy Process (AHP) has been used in this research paper. Based on the findings of this paper it can be said that the most important factor that needs to be taken into account is victims’ related costs while the least important factor as per the findings of the paper is cost related to property damage.

According to World Health Organization (WHO) statistics, 1.3 million people die globally each year as a result of traffic accidents. According to estimates, 20 million to 50 million individuals worldwide suffer non-fatal injuries, the majority of whom end up incapacitated (Global status report on road safety, 2018). In India, the WHO, in 2019 estimated 1,54,732 fatalities 4,37,396 injuries (Global status report on road safety, 2018). The Ministry of Road Transport and Highways (MoRTH) reports that in India in 2018, there were 1,50,785 fatalities and 4,94,624 injuries (Road accidents in India Delhi, 2019). India has the greatest rate of deaths from traffic accidents (Shami, 2005), according to (Sundar & Ghate, 2013), which also supports the findings of (Shami, 2005). The same research paper points out that India is the only country that reports the highest number of deaths due to road accidents. Keeping in view the previous reports it can be said that more than 30% or every 1 in 3 accidents prove fatal in India. While in the remaining cases (about 62%), the victim becomes seriously impaired which greatly affects productivity. It has been found that road accidents not only affects the road accident victims, but it also impacts the whole family (financially and mentally). The repercussions are even more severe when the road accident victim is the sole earning member. The whole dynamics of the family gets changed because of a road accident. Looking into the post-accident scenarios, in order to make up for the damages, compensation needs to be provided. But In India and other developing countries, the compensation mechanism is not at all robust, and eventually, it takes a lot of time to decide the compensation amount. Even if the victim or the dependent family members receive the compensation, most of the time the amount is derisory. People settle for a pitiful amount of compensation simply because they are tired of the stressful and drawn-out compensation procedure, which frequently involves questioning the victim or their family members and re-recording the accident chronology. It further worsens the condition of the diseased person and also the dependent family members (Elbers et al., 2013; Giummarra et al., 2020b; Littleton, Hughes, et al., 2014; Mayou August, 1995) as it causes mental torment and is a form of secondary victimization. Thus, in order to calculate ‘Fair Compensation’, we need to find out the parameters on the basis of which compensation needs to be calculated. In order to find out the parameters, various research papers were studied (Giles, 2003; Giummarra et al., 2020a; Hauer, 1994; Hills & Jones-Lee, 1981b; Jou, 2014; Miller, 2000; Risbey, 2008; TRL, 2004b; World bank group, 2021). Apart from these parameters, the authors also tried to explore other factors that can be incorporated into calculating the compensation amount. In view of the same accident victims/their dependent family members were also interviewed in order to understand their perspectives and expectations from the compensation models.

2. Literature studies

Automobile accidents were the eighth leading cause of mortality worldwide in 2016, according to WHO 2018 (Global status report on road safety, 2018). Eighth in deaths. Ashenafi et al. estimate 5 million road accident deaths and 50 million injuries per year (Habte Woyessa et al., 2020). It is estimated that automobile crashes could kill 2.4 million people per year by 2030 if appropriate steps are not taken on time. Automobile accidents cause personal and national economic losses (Masniak, 2008). Besides safety, it causes health, social, and economic issues for the victim and their family (Kazmia & Zubair, 2014; Sahu, 2012). Due to their dependency on others and limited mobility, many accident victims’ lives get destroyed. In case of a fatal accident, the family must endure trauma and mental anguish. Road accidents cost nations a lot of money, and even some losses are intangible. India is the only country that reports 15 traffic fatalities and 53 injuries each hour. According to P.K. Sahu, the situation in India is worsening (Sahu, 2012). Seventy percent of road accident victims are from developing nations, and pedestrians make up 65% of them. Minors make up 35% of highway fatalities (Sahu, 2012).

2.1. Costing methods for road accidents

The values of economic losses come out to be different, as different studies have taken different parameters under consideration along with the different methods of calculating the road accident cost. Some of the most common methods which are used across the globe are as follows: -

1. Human capital approach (HC) (Widely used in Asian and other developing and under developed countries)

2. The Life insurance approach (Common in Developed and developing countries)

3. The Court award approach (Used in almost every country)

4. The Implicit public sector valuation approach (European and American Countries)

5. The values of the risk-change approach or the willingness-to-pay approach (WTP) (Commonly used in Australia, European and American countires)

6. Hybrid Method (Limited studies across the globe, mainly from Asian countries)

(Hills & Jones-Lee, 1981a) suggest that only two road traffic accident costing methods i.e. the Human Capital (HC) method, and the Willingness to Pay (WTP) method, appear to be directly relevant among these methods. When examining a country’s wealth, the HC approach (Gross Output method) is appropriate. If social welfare is a concern then Willingness to pay method is more suited. The Gross Output technique is recommended by TRL (Transport Research Laboratory, UK) to cost road accidents in developing nations like India. Professionals working in this field, such as Hauer, believe that putting a monetary value on human life is unethical (Hauer, 1994). Miller (Miller, 2000) divided the losses resulting from traffic accidents into the following four major categories.

• Medical costs (Hospital, ambulance, rehabilitation, etc.)

• Cost associated with other resources (police, fire, legal/court, and victim services).

• Work Loss Costs (Loss of productivity, loss in wages, etc.)

• Life satisfaction value of pain, suffering, and quality of life lost to victims and their families. It calculates the cost of a traffic accident as the total cost of all real resources.

2.2. Compensation Mechanism in India

In India most of the time the compensation is decided based on the following, Primarily it’s the insurance company that shells out the money to its client, depending upon the premium (amount paid periodically to the insurer by the insured for covering his risk.) paid. Also, there are a few guidelines and the Indian government has changed the way road accident victims are compensated. This money is given to people who have been hurt in crashes or to their families if the person has died. Now, if a person dies, his or her family will get Rs. 5,000,000($6289). If the disability was permanent, the compensation would be between Rs. 50,000($629) and Rs. 5,000,000($6289), based on what kind of disability it was and how severe it was. Earlier i.e. before 2019, the government gave Rs. 50000($629) if someone died and Rs. 25,000($312) if they were disabled for life. The victim didn’t have to show that the driver was at fault in order to get this money. But people didn’t take this small amount of money very well. People thought they should have been paid more. So, the Motor Accident Claims Tribunal (MACT) was where most of the road accident compensation claims went to court. In order to win the case in court, the victim would have to show that the driver was at fault. If the fault can be shown, MACT may give a bigger amount of compensation. But again, visiting courts and getting a verdict in one’s favor is a very long and debilitating process and people are forced to accept the derisory amount. When the government looked at the compensations that MACT had given out, they found that the average amount was between Rs. 3,000,000($3750) and Rs. 5,00,000($6289). This is why the government chose to change the way road accident victims are paid. The goal is to cut down on court cases in the MACT. But even though the government has increased the payout by ten times, the victims or their family members can still go to MACT and ask for more money. From 1 April 2022, the immediate family members of victims of ‘hit and run’ crimes are entitled to get Rs 2,00,000($2500) for death. This was an eight-fold increase from the previous cap of Rs 25000($312). Anyone who is grievously injured in an accident will get Rs 50000($629) in compensation, which was about Rs 12,500($156) earlier.

As per our primary survey it has been found that Out of all the claimants who knocked the doors of court for justice, just 33% received the verdicts in their favor, the other 34% got a verdict against them, and the remaining 33% had cases still pending in courts, the case duration can range anywhere between 1 year to 9 years. Sometimes the cases may even stretch for more than 25 years. As per our data, the mean time for the court’s verdict came out to be around 3.6 years. [MACT]Sometimes in cases where there has been a major road accident that grabs the attention of the media and political parties they often announce the compensation of either INR 2,00,000/- or INR 5,00,000/-. In order to ease the situation of road accident victims, these compensation schemes can be good for short-term relief (if provided on time), but it completely ignores the socio-economic characteristics of the victims. And that is exactly where this research paper wants to intercede

*INR(The Indian Rupee), The conversion rate to the dollar has been taken from Aug 2022, i.e., 1$ = 80 INR

2.3. Critical review of the methodologies used in an accident costing

Table 1 shows the Strengths and weaknesses of the different methodologies which are most commonly used as a tool for calculating the compensation amount. Researchers think that when people use the WTP method, they may say a value that they are not actually willing to pay. This gives the HC method the edge. The WTP method is complicated and has been debated a lot in the past. Even though it is complicated, the WTP method is known to be the most theoretically sound method to calculate the costs related to intangible parameters like value of pain and sufferings. WTP is used less often in low- and middle-income countries, but it has been used successfully in India and Iran just recently (ADB report 2021) (Asian Development Bank, 2018). Apart from these methods, which are used often, there is a fairly new method called the ‘Hybrid method’. Some international studies have used it and found it to be useful. This method, as the name suggests is a mix of two or three other methods, The HC approach is used to figure out the cost of restitution, and the Willingness to Pay approach is used to figure out the value of a life lost, pain, or grief, etc. Kazakhstan and Azerbaijan have recently used this method (June 2021) (World bank group, 2021). Each method has a unique approach, so the estimates of costs are very different. The output (HC) method gives a fairly moderate estimate of how much an accident will cost in an industrialized country, but the life insurance and court award methods give slightly higher estimates. The Implicit public sector method gives a slightly lower estimate of how much an accident will cost, while the value of risk change (WTP) gives a higher estimate. In developing countries, the HC method is most often used, and WTP is avoided because people’s perceptions of risk and lack of data, make WTP difficult to use. However, a few Studies [done in India using WTP methods found that estimating the value of a life based on willingness to pay for risk reductions increased estimates of human capital by a factor of ten (Viscusi, 2000). It’s hard to agree on which method is best (Risbey, 2008; Viscusi, 2000). Estimating the costs of death, disability, and quality of life is the hardest and most controversial (Hauer, 1994). Economic theory backs the willingness-to-pay approach over the human capital approach. The WTP method is the best way to figure out how much the lives saved are worth (Giles, 2003; Wijnen, 2021). Experts often say that the Human Capital method doesn’t take into account the costs of pain and suffering (Chen et al., 2019; Osterhaudt, 2002). Even if these costs are calculated, they are taken as an arbitrary percentage, and the ‘cost of life’ is also hard to measure. Several studies have shown that the WTP technique is the best way to value things like pain, suffering, loss of quality of life, and death (Osterhaudt, 2002). The current studies don’t do a good job of showing how much loss, grief, pain, and suffering people go through after a car accident (Balakrishnan & Karuppanagounder, 2020; Nachimuthu & Partheeban, 2013; Viscusi, 2000). Apart from differences in how costs are estimated, there have also been some major problems related to data collection. For example, traffic accidents are often under-reported, which means that the real number of accidents could be 20–30 times higher than what is reported (Melhuish, 2005; TRL, 2004a).

Table 1. Methodologies for road accident costing.

Method	Advantage(s)	Disadvantage(s)
Human Capital approach	Easiest and most popular technique to employ. More appropriate when national welfare is prioritized and macroeconomics is taken into account.	Not an effective method to measure sorrow, loss, and suffering. Measures pain and suffering at an arbitrary value. Widely criticized by economists for not adhering to the concepts of (Cost-Benefit Analysis) CBA after the 1970s.
Life insurance approach	The victim receives compensation based on the insurer’s and insurance company’s appraisal. This is usually the quickest way to be compensated	Not everyone gets insurance for themselves. Most of the victims are from the poor part of society, but they are not covered. that is, around 80% (Rajasekaran et al., 2020).
Court award approach	Uses recommendations rather than set rules or formulas, which may be in the victim’s favor. Uses previous judgments to estimate the cost of a traffic accident.	Cases are usually settled too slowly. Because the cases take so long, the victims or their lawyers lose hope and decide not to go to court.
Implicit valuation approach	Focuses on looking into the implicitly placed values on accident prevention.	In the valuation of the cost of a road accident, it has a limited use.
Willing- ness to pay approach	The best way to figure out how much things like pain and sadness are worth. It is the most logical way to figure out how much the human cost is.	Method to be used is complicated. People can say a price that they are not ready to pay.
Hybrid Method approach	Combines two methods to get the best of both worlds. Eliminates the disadvantages of HC and WTP methods.	No major disadvantages have been found as of now. Very limited studies have been done as it is a fairly new method (2020).

2.4. Limitations and gaps in the previous studies

A new version of the ‘Evaluation of Road Accident Costs’ study that TCS did in 1999 is often used to decide how much pay to give. Few problems can be found with this study, so the figures won’t be right. There were some differences in things like life expectancy, medical costs, and the value of the loss of livability, so the figures are not valid. According to K. Nachimuthu (2013) (Risbey, 2008), ‘very few studies have been done in India, and they are not accurate or cover a wide enough area’ (Graya & Colliea, 2022). The court award method takes a long time and says that valuation should be done with the diseased’s ‘future prospects’ in mind. But most of the time, the victim or the victim’s family does not agree with the value because they think it is too low. When the decision is given, sometimes half of the people involved in the case are already dead. Few attempts have been made to explain why different people in society make different amounts of money. Some other social issues, like the loss of opportunities, that happen in Indian families have not been taken into account, and they still need to be added to the compensation models. There is a debate about which way is best for India out of many different ones. Pain and sadness are hard to put a price on, and this topic is rarely talked about. So far, none of the ways has used science to figure out how much the ‘lost opportunity costs’ are. When giving out the final pay, family members who depend on the person are often forgotten.

2.5. Compensation

Once a road accident has happened, it’s important to look into the compensation issues so that the victim can recuperate at the earliest. Especially, If the accident is grievous or fatal, a ‘fair compensation’ needs to be figured out so that the family members can convalesce. As discussed previously, road accidents can cause serious mental problems and emotional stress, and they often cause serious problems in everyday life.

In India, and countries with similar family dynamics the problem is even worse because a road accident affects the whole family. This is especially true if the person who died was the sole bread earner. Most of the time, compensation is a sum of money that only covers the direct losses, and loss of quality of life (Mayou, 1995). It often ignores the aspiration and ‘Lost opportunities’. Compensation is often a long and frustrating process, and people settle for a derisory amount because they are surfeited by the process. Often, the victim or their family members have to be questioned and re-record the accident, which makes them feel even worse and is often termed as ‘Secondary Victimization’. Which often deteriorates the health of the sick person and the people who depend on them (Elbers et al., 2013; Mayou, 1995). It’s not easy to put a monetary amount on life, and it’s hard to capture the changing nature of costs and compensation with a single statistical or mathematical model. A compensation expert namely Kenneth R. Feinberg says and emphasizes that putting a price on life is hard and debilitating (Feinberg, 2006). It was based on his experience as the Special Master, who, along with his team, calculated the compensation amount for each 9/11 death in the United States. He stated that the value of a life is equal to what a person earns over their working life, plus the money they lose because they can’t work, plus the value of pain and suffering. The formula looks simple on paper, but it isn’t when you use it. When it comes to evaluation and paying compensation, there isn’t a ‘one size fits all’ solution (Feinberg, 2012; Graya & Colliea, 2022). India needs to come up with its own model that has the right parameters and gives victims and their families the money they deserve, and that too on time. It has also been found that it is equally important to acknowledge the pain and suffering of the victim or their family members. The pay shouldn’t be so low that it can’t take care of the family, and it shouldn’t be so high that people treat it like a lottery. It has been found that when compensation is too high, people often fake their cases to get that amount of compensation (Thierauf et al., 2009). Not only in the case of road accidents people tend to fake cases everywhere. A very recent and unfortunate rail collision accident happened in Odisha on 2 June 2023, which killed at least 288 people, at least 1200 got injured. In the same series when the government announced a compensation, there were a number of fake cases were cracked. It is to be noted that the state announced a compensation of INR 5,00,000/- for death and INR 1,00,000/- for those who suffered injuries, The railway ministry announced a compensation of INR 10,00,000/-, In case of death, INR 2,00,000 for grievous injury and INR50,000/- for minor injuries and the Prime minister office announced a compensation of INR 2,00,000 each in case of fatal accident and INR 50,000/- for the injured. Similar amounts were announced by the Chief ministers of various states. The amount seems to be decent for instant relief, and should provide the victims with some stability; however, these train accidents are rare and as these catch up International and media attention, the compensation amount and procedure is often fair. But that’s not the case with road accident victims, hundreds, if not thousands of road deaths are recorded in India, but till date we do not have a robust mechanism that can calculate and provide the road accident victims a fair amount of compensation in a quick and efficient manner. So, a strong system for accident compensation needs to be developed, especially for countries like India.

3. Data collection and Methodology

The methodology comprises of three stages, in the first part background study was conducted keeping in view three bases that included a literature review of research papers, Interviews of experts and interviews of Victims. In the pilot stage 18 research papers, 7 experts and 38 road accident victims were interviewed. Out of each background study, certain parameters were identified and some of them also got overlapped and the same has been shown in Figure 1. After finding the parameters from the first round, A second round of interviews were conducted and a total of 23 parameters were identified. Some statistical tests were conducted to identify the key parameters that should be considered for calculating compensation. The tests were conducted on IBM SPSS + AMOS V23.0. After the analysis out of 23, 5 factors were rejected, and the remaining 18 factors were further grouped in four categories. And these 4 categories and their parameters were subjected to Analytical Hierarchy Process (AHP), Based on the results of AHP the ranking of the parameters were done.

Figure 1. Methodology adopted for the research paper.

In this paragraph, the parameters and their implications have been discussed. By Income group, we tend to look into the effect of the Victim’s income group on deciding the compensation. How does the number of family members of the victim should affect the compensation has been looked under the parameter of Dependent family members. We also hypothesized that it is not only the victim but also the family members of the victims that are affected by a road accident and therefore we also took into consideration how an accident affects the opportunities lost due to a road accident. Further, we wanted to investigate whether a diet allowance should be included while deciding the compensation. Also, it was investigated whether the time of the accident, gender, and age should also have an impact on the compensation amount. Impacts of medical expenses, and rehab centers costs were also studied. It is often noticed that there is a fixed cost that is given as compensation for the pain, grief and suffering the victims and their relatives go through, also needs to be accounted for and gauged, and the same was taken as one of the parameters. Under impairment-specific costing, we wanted to find whether any additional compensation should be provided If the impairment affects the mode of earning and forces the victim to switch to another job. Similarly, costs associated with Artificial limbs were also taken into account. After an accident lot of costs related to towing of vehicles, costs related to visits to courts, police stations, workshops, etc. were also considered in the analysis. Under the property damages and lost labor output factor, parameters such as Cost related to an emergency vehicle, Vehicle damages, lost labor output, etc were taken into consideration. It was also looked at whether if drivers’ past records should also be included in deciding the amount of compensation. An unsupervised learning approach called the principal component analysis is used in machine learning to reduce dimensionality. With the use of orthogonal transformation, it is a statistical process that transforms the observations of correlated features into a set of linearly uncorrelated data. The Principal Components are these newly altered features. One of the widely used tools for exploratory data analysis and predictive modeling is this one. It is a method for identifying significant patterns in the provided dataset by lowering the variances. Typically, PCA looks for the surface with the lowest dimensionality onto which to project the high-dimensional data. PCA functions by taking into account each attribute’s variance since a high attribute demonstrates a solid split between classes, which lowers the dimensionality. Since it uses a feature extraction technique, it keeps the crucial variables and discards the unimportant ones. Thus, this method is widely used as a tool to check the relative importance of the various parameters, it can also be used to club the parameters into categories and can also be employed to test which factors should be included and which ones are to be rejected.

4. Predicament of road accident victims in India

In India and in many of developing nations the situation of road accident victims in really very pitiful right from the accident phase till the phase of secondary post-accident implications. There have been cases where the victims has to suffer irreparable losses. The same thing has been summarized in the flowchart given in Figure 2. Majority of the accidents take place in the evening time and is often because of reduced visibility. Many a times street lights are not present asn sometime they are not functional, reducing the visibility. After the accident it if often

Figure 2. Flowchart of road accident phases.

Observed that people might rush for help but the actual help i.e either in the form of an Ambulance or some Samaritan comes only after 15 minutes have passed, in the case of grievous accidents these 15 min are very crucial as they form the first quarter of the ‘Golden Hour’. Ambulance share as the mode of access to the hospital is just 19% as in many of the hospitals the staff is underpowered. Majority of the victims are quite young and in the case of males, they are often the main earning member of the family. An accident victim has a direct impact on at least 2 members of the family. Despite being an important thing only 23% of the victims are found to be insured against road accidents. In cases where hospitalization is not required the victim may have to spend about INR 31,000/- for vehicle repair. Sometimes a vehicle may become immobile because of the impact and the owner may have to shell out INR 2400/- for towing of vehicle.

Figure 3 shows that the average distance between the accident site and the nearest hospital is about 3.8 kms and if the peri urban areas are included then the distance increases to 7.5 kms. The mean of the age of the accident victim is about 35 yrs. However, the mode of age comes out to be as 24 yrs and the same has been shown in the boxplot of Figure 4. In India it has been found that people are still reluctant to help the accident victims, there are numerous reasons for the same. Sometimes people are unaware of the first aid that should be provided and they abstain themselves from helping the accident victim, People do have a very notion that helping a person might land them in unnecessary trouble, like police inquiry, court cases, etc. People may help the person by providing them with water, some kind of support etc, but when it comes to helping the patient to reach out the hospital, majority of the population is still very reluctant. And it has been found, as shown in Figure 5 that it takes about 15 minutes before the help arrives. If this time could be reduced then we can significantly improve the chances of survival of any victim. Especially for grievous accident cases.

Figure 3. Average distance to hospital.

Figure 4. Mean age of the accident victims.

Figure 5. Time before the help arrives.

In the surveys, it was evident that it is not only the road accident victim that gets affected by the accident, rather it is the near and dear ones who also get affected by the accident, an accident impacts at least 2 dependent family members, the same has been depicted in the box plot of Figure 6. In case of an accident, where the patient needs to be admitted as an in-patient to the hospital, then, in that case, the victims spend about 4 days. And the amount spent during the primary treatment is about INR 19,000/-. By primary treatment, we mean the time a victim spends as an in-patient till the time he gets discharged. The same information has been depicted in Figures 7 and 8.

Figure 6. Dependent family members.

Figure 7. Money spent on primary treatment.

Figure 8. Time as in-patient.

5. Data Analysis and discussion

As shown in the methodology and briefed in the introduction the analysis is based on the ratings of the parameters on a Likert scale by of 27 experts and 73 randomly chosen road accident victims out of the 126 victims interviewed, It was so, as many of the victims’ didn’t understand the Likert scale and it was necessary to reject some of the ratings (Cornes, 1992). The first test that was conducted was KMO and Bartlett’s test to check the Sampling Adequacy and whether further analysis will yield acceptable results or not. It turned out that the sample was adequate and it was significant as well. The same has been shown in Table 2.

Table 2. KMO and Bartlett’s test results.

Kaiser-Meyer-Olkin Measure of Sampling Adequacy.		0.617
Bartlett’s Test of Sphericity	Approx. Chi-Square	663.186
	df	253
	Sig.	0.000

After KMO test we proceeded with dimension reduction using factor loadings through the Likert scale ratings and for the same data a correlation matrix was generated along with the screen plot and based on the rankings, the results

Figure 9. Structural Model with factors and parameters.

Table 3. Significance test matrix of the different parameters taken into consideration.

	IG	DFM	TOA	DA	ME	LLO	WV	PGS	LO	ISC	REC	VD	PD	TC	PI	CV	AG	GDR	IC	EC	RC	DPR	ALC
IG		0.000	0.055	0.344	0.000	0.000	0.443	.000	0.000	0.270	0.309	0.281	.125	0.312	.076	0.326	0.088	0.234	0.296	0.363	0.181	.052	0.042
DFM	0.000		0.239	0.482	0.000	0.000	0.359	.000	0.000	0.498	0.440	0.428	.218	0.142	.117	0.254	0.171	0.401	0.473	0.356	0.029	.062	0.153
TOA	0.055	0.239		0.419	0.024	0.050	0.197	.493	0.042	0.424	0.297	0.294	.467	0.328	.443	0.016	0.309	0.383	0.468	0.076	0.011	.115	0.161
DA	0.344	0.482	0.419		0.173	0.150	0.004	.317	0.476	0.290	0.009	0.062	.190	0.100	.040	0.272	0.386	0.041	0.162	0.003	0.041	.268	0.085
ME	0.000	0.000	0.024	0.173		0.000	0.234	.000	0.001	0.145	0.081	0.124	.237	0.371	.045	0.348	0.076	0.098	0.143	0.148	0.310	.108	0.008
LLO	0.000	0.000	0.050	0.150	0.000		0.179	.000	0.000	0.127	0.364	0.155	.174	0.486	.206	0.125	0.027	0.108	0.298	0.494	0.183	.341	0.209
WV	0.443	0.359	0.197	0.004	0.234	0.179		.439	0.327	0.000	0.000	0.460	.407	0.379	.434	0.289	0.275	0.479	0.392	0.030	0.410	.386	0.053
PGS	0.000	0.000	0.493	0.317	0.000	0.000	0.439		0.000	0.301	0.281	0.353	.284	0.346	.193	0.330	0.088	0.064	0.297	0.430	0.341	.001	0.017
LO	0.000	0.000	0.042	0.476	0.001	0.000	0.327	.000		0.081	0.451	0.451	.370	0.416	.238	0.116	0.231	0.043	0.316	0.378	0.049	.083	0.380
ISC	0.270	0.498	0.424	0.290	0.145	0.127	0.000	.301	0.081		0.000	0.362	.089	0.217	.339	0.240	0.451	0.388	0.213	0.324	0.023	.251	0.010
REC	0.309	0.440	0.297	0.009	0.081	0.364	0.000	.281	0.451	0.000		0.104	.459	0.217	.253	0.107	0.469	0.320	0.200	0.365	0.010	.496	0.118
VD	0.281	0.428	0.294	0.062	0.124	0.155	0.460	.353	0.451	0.362	0.104		.131	0.457	.000	0.023	0.461	0.163	0.135	0.394	0.005	.105	0.461
PD	0.125	0.218	0.467	0.190	0.237	0.174	0.407	.284	0.370	0.089	0.459	0.131		0.000	.168	0.125	0.307	0.084	0.319	0.458	0.156	.153	0.113
TC	0.312	0.142	0.328	0.100	0.371	0.486	0.379	.346	0.416	0.217	0.217	0.457	.000		.301	0.128	0.494	0.009	0.313	0.362	0.015	.241	0.012
PI	0.076	0.117	0.443	0.040	0.045	0.206	0.434	.193	0.238	0.339	0.253	0.000	.168	0.301		0.348	0.234	0.130	0.115	0.250	0.020	.092	0.492
CV	0.326	0.254	0.016	0.272	0.348	0.125	0.289	.330	0.116	0.240	0.107	0.023	.125	0.128	.348		0.458	0.296	0.340	0.435	0.185	.265	0.334
AG	0.088	0.171	0.309	0.386	0.076	0.027	0.275	.088	0.231	0.451	0.469	0.461	.307	0.494	.234	0.458		0.196	0.206	0.474	0.263	.393	0.011
GDR	0.234	0.401	0.383	0.041	0.098	0.108	0.479	.064	0.043	0.388	0.320	0.163	.084	0.009	.130	0.296	0.196		0.048	0.011	0.215	.464	0.412
IC	0.296	0.473	0.468	0.162	0.143	0.298	0.392	.297	0.316	0.213	0.200	0.135	.319	0.313	.115	0.340	0.206	0.048		0.485	0.039	.221	0.303
EC	0.363	0.356	0.076	0.003	0.148	0.494	0.030	.430	0.378	0.324	0.365	0.394	.458	0.362	.250	0.435	0.474	0.011	0.485		0.121	.076	0.048
RC	0.181	0.029	0.011	0.041	0.310	0.183	0.410	.341	0.049	0.023	0.010	0.005	.156	0.015	.020	0.185	0.263	0.215	0.039	0.121		.182	0.116
DPR	0.052	0.062	0.115	0.268	0.108	0.341	0.386	.001	0.083	0.251	0.496	0.105	.153	0.241	.092	0.265	0.393	0.464	0.221	0.076	0.182		0.271
ALC	0.042	0.153	0.161	0.085	0.008	0.209	0.053	.017	0.380	0.010	0.118	0.461	.113	0.012	.492	0.334	0.011	0.412	0.303	0.048	0.116	.271

so produced help in identifying the relevant parameters and dropping some of the parameters that were initially taken i.e 23. All the parameters taken have been shown in Figure 2 along with acronyms summarized in the table alongside. The structural model of the same has been shown in Figure 9. Then the resultant parameters were clubbed under 4 factors. Similar to the Correlation matrix a matrix for the significance was also generated as shown in Table 3, to find the relevant parameters, it can be seen from the significance matrix table and rotated component matrix Table 5, any parameter with values less than 0.4 needs to be rejected.

After conducting the significance and covariance analysis we also looked into how the software has clubbed up the parameters into four factors. The commonalities and the rotated matrix of the data were also tested and the same has been shown in Tables 4 and 5.

Table 4. Parameters and Commonalities.

Parameters	Initial	Extraction
IG	1.000	0.762
DFM	1.000	0.661
TOA	1.000	0.056
DA	1.000	0.389
ME	1.000	0.489
LLO	1.000	0.693
WV	1.000	0.671
PGS	1.000	0.578
LO	1.000	0.524
ISC	1.000	0.523
RC	1.000	0.593
VD	1.000	0.683
PD	1.000	0.403
TC	1.000	0.439
Pl	1.000	0.639
CV	1.000	0.145
AG	1.000	0.055
GDR	1.000	0.330
IC	1.000	0.121
EC	1.000	0.302
RC	1.000	0.500
DPR	1.000	0.197
ALC	1.000	0.226

Table 5. Parameters with Rotated components matrix.

Parameters	Factor1	Factor2	Factor3	Factor4
IG	0.872	−0.459	0.819	0.614
DFM	0.804
TOA
DA
ME	0.643
LLO	0.822
WV		0.810
PGS	0.743
LO	0.718
ISC		0.688
RC		0.733
VD
PD
TC				0.628
Pl			0.786
CV
AG
GDR				−0.525
IC
EC
RC			0.492	−0.489
DPR
ALC		0.407

Out of the 23 values taken initially, the bottom 5 parameters were rejected and we were left with 18 parameters, these 18 Parameters were clubbed under 4 factors namely, Restitution Cost (Minimum amount granted to victim to compensate personal loses), Victim-related costs (Costs such as medical expenses, rehab costs etc come under this category), administrative cost (Costs related to the amount spend to procure the compensation amount), and Costs related to property damages (Monetization based on the loss to property, both private and public) and lost opportunities (Keeping in view that opportunities get lost because of accidents, like promotion, loss in school/college, etc). After the parameters were extracted an AHP was done for the parameters and an AHP was also done between the 4 major factors.

5.1. Analytic Hierarchy Process

The analytical hierarchy process is a way to measure priority (AHP). It is used to make ratio scales from comparisons between different things that are either continuous or discrete. You can make these comparisons using real measurements or a simple scale that shows how strongly different preferences and emotions are shown (Saaty) (Saaty, 1980).

The next step is to compare the parts of the hierarchy in pairs. The goal is to figure out how much each of them weighs in relation to each of the parts at the next higher level. The letter ‘A’ stands for the pairwise comparison matrix, ‘W’ for the eigenvector, and ‘max’ for the highest eigenvalue. The acquired weight vector is multiplied by the weight coefficients of the items at the higher levels of the hierarchy, up until the top of the hierarchy, if there are any elements at the higher levels. The option with the highest weight coefficient value should be chosen as the best choice. The components [a_ij] will meet the following requirements if n(n-1)/2 comparisons are consistent, where n is the number of criteria: a_ij = w_i/w_j = 1/aji and aii = 1 with I_j,k = 1,2,…,n. In the comparison matrix, the letters a_ij show that ith is more important than jth. Since comparing two qualities is easier than figuring out the whole weight of a criterion, it seems that using pairwise comparisons to figure out the weights of criteria is a more reliable way to get them than getting them directly. AHP also calculates an inconsistency index to show how consistent the decisions made by decision makers during the evaluation stage are (also known as a consistency ratio). The formula CI = (λ_max - n)/2 can be used to find the inconsistency index in pairwise comparison matrices and decision matrices (n − 1). As the inconsistency index gets closer to zero, the consistency goes up. If all of the criteria meet the condition that a_ij - a_ik = a_ik, then the decisions will be consistent. The relevant index must be less than 0.10 for the AHP results to be accepted as consistent. If this is not the case, the person making the decision has to do the comparisons and evaluations again. The comparison matrix must be normalised before any vector of priority calculations can be done. So, the total number of entries in each column must be divided by the number of columns. In this way, a normalised matrix is made, in which the elements of each column vector add up to 1. To figure out how much each criterion matters, you have to figure out the eigenvalues of this matrix.

$\begin{array}{rl}& WijIfxihasmorimportanceascomparedtoxj\\ & \left\{\frac{1}{Wij}ifXjhasmore\right.importancethanXi\\ & 1Ifbothareequallyimportant\end{array}$

Assuming, for example, that if and only if Ij, then for every I j 1,2,…, n, xi dominates xj, the positive reciprocal matrix will look like this:

$W=\left(\begin{array}{c}1W_{12}\dots W_{1n}\\ 1/W_{12}1\dots W_{2n}\\ \dots \dots \dots \dots \\ \dots \dots \dots \dots \\ 1/W_{1n}1/W_{2n}\dots 1\end{array}\right)$

To give each element xi a ‘priority’, the primary eigenvalue max of matrix W and its normalized eigenvector are calculated (or ‘weight’). The w(xi) values make up the parts of this vector (xi). This method has a very interesting property: if the judgements of J meet the cardinal consistency criterion, w_ij.w_jk = w_ik for all I j k, then the resulting w_(xi) meet the constraint that w_ij = w(x_i)/w(x_j) for all I j. But in real life, cardinal constancy isn’t seen very often. So, AHP uses a ‘consistency test’ to stop priorities from being made if there are a lot of differences between them. To find the divergence, you take the average of the consistency index C.I. of matrix W and the consistency index

C.I. of matrix W from ‘consistency’ to get a random index R.I. of order n. C.I. of tens of thousands of randomly generated (of order n) reciprocal matrices forced 1–9 reciprocals on the scale. There are R.I. values for matrices with sizes of 1, 2, and 10. The amount of C.I. to R.I. If the consistency ratio, or C.R., is 0.10 or less, then this is acceptable.

5.2. AHP Results

Summary of the AHP results

Table

Sl no	Parameters	The geometric mean of the priority	Rank
1	Major Components of Compensation
a	Restitution Cost	20	2
b	Victim Related cost	62	1
c	Property Damage	6	4
d	Administrative cost	7	3
2	Victim related
a	Medical cost	30	1
b	Rehabilitation cost	13	3
c	Pain, grief, and suffering	11	4
d	Impairment specific	18	2
e	Artificial limb’s Cost	9	5
3	Restitution Cost
a	Income group	21	2
b	Dependent members	20	3
c	Lost opportunity	27	1
d	Age	21	4
4	Administrative Cost
a	Police investigation	9	3
b	Court visits	29	2
c	Insurance claims	34	1
d	Rituals cost	8	4
e	Workshops visits	5	5
5	Property damages and lost output
a	Towing/emergency vehicle cost	4	4
b	Vehicle/property damages	24	3
c	Drivers’ record	25	2
d	Lost labor	27	1

As can be observed from the above table, out of the four main compensation criteria (factors), the victim-related costs have been given the highest priority, followed by the cost of restitution and then administrative costs, while the property damage factors have received the lowest priority ranking. When we examine each major parameter in depth, we find that the ‘victim-related’ costs are included as sub-parameters. Medical expenses received first priority, followed by the compensation component particular to an impairment. The cost of rehabilitation had the sixth highest priority, while the cost of suffering from pain, grief, and other emotions received the lowest priority. The lost opportunity component received the highest priority in the restitution cost portion, followed by the income group and then the dependent family members. The expenses associated with filing insurance claims and going to court were the two administrative costs criteria that received the highest rankings, followed by the costs associated with conducting police investigations, and the costs associated with performing rituals received the lowest rankings. The cost of lost labour was given the most consideration when determining compensation for lost opportunities and property damage, according to a variety of experts. Next, in importance to be considered when determining compensation were the costs associated with vehicle and property damage, towing expenses, and emergency vehicle expenses.

6. Conclusion

Considering the opinion of the experts, and the road accident victims, it’s clear that motor vehicle accidents have a huge effect on the people who are hurt. It has been found that getting the right amount of compensation often takes a long time or that the amount got is often very small. To calculate and render what is called ‘fair compensation’, it is very important to look at the factors that affect the amount of compensation. In light of this, 4 main parameters and 18 sub-parameters were ranked. The study found that costs related to victims should be given the most importance, followed by costs related to restitution, administrative costs, and then costs related to car and property damage. In the second half of the research paper, the second half of the research talks about the predicament of road accident victims in India. It seems that the road accident and its detrimental impacts are highly undermined. Accident victims, especially those who belong to the unprivileged section of society often face obstacles in every phase of road accidents. This paper was an attempt to highlight the same. It is expected the results of this paper will help in framing better policies for road accident victims and will also help in the calculation of compensation in a quick and efficient manner.

6.1. Limitation and future scope of work

As far as the present study is concerned, there are few limitations to it like the sample size was restricted to 100 (27 experts and 73 Victims), because of the manpower constraint and also, it’s very difficult to trace, interact and extract data from hospitals and road accident victims after conducting their interviews. The whole process is debilitating and time-consuming. Apart from this many times the road accident victims are unable to understand the question and may tend to give answers that are irrelevant to the study, therefore some of the samples had to be eliminated from the total samples collected. We collected about 126 road accident victim samples that had approximately 38 questions. Out of 126 samples, only 93 turned out to be relevant and therefore we rejected the others and selected only 73 samples out of 93 so that we can get a total of 100 samples. The model has done the ranking only, as s future scope it can also put in weightages to the factors that can be useful in deciding the compensation amount. Also a bigger sample size can be taken to ensure more accurate results.

Disclosure statement

No potential conflict of interest was reported by the author(s).