Identifying the Social Costs of Road Traffic Crashes in India through Quantitative and Quantile Regression Analysis

Identifying the Social Costs of Road Traffic Crashes in India through Quantitative and Quantile Regression Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Identifying the Social Costs of Road Traffic Crashes in India through Quantitative and Quantile Regression Analysis Mansi Ranjan, sanjeev Sinha This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7296445/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Despite recent advances in addressing road safety, especially in developed countries, road traffic crashes still result in 1.65 million fatalities annually and impose costs exceeding $ 95 billion. This paper reviews the literature on socio-economic costs, identifies key research gaps, and underscores the lack of analysis focused on developing countries, which experience 90% of global fatalities. Using both descriptive and econometric analyses, we observe an upward trend in road safety studies in high- and middle-income countries. We calculated the components of hospitalization costs and examined the relationship between these costs and patient characteristics using quantile regression models. The paper examines two primary methodologies for estimating socio-economic costs: willingness-to-pay (WTP) and human capital (HC). Our econometric findings show that studies using the WTP method typically estimate the impact on GDP to be approximately 1% higher than those using the HC approach. Furthermore, the HC method tends to underestimate total socio-economic costs by a factor of two compared to WTP-based estimates, although this gap narrows significantly when adjusting for factors like population density, income levels, and road safety conditions. Additionally, the paper highlights challenges with underreporting and the lack of a systematic method to account for it in cost estimations. We conclude with a call for more research in low- and middle-income countries that combines WTP, HC, and alternative valuation methods to provide a more comprehensive understanding of the socio-economic costs of road crashes. Civil Engineering Regression-analysis Road crashes Road safety Socio-economic costs Willingness-to-pay Human capital Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Road crashes are reported daily across the globe. In 2019–2023 between, road traffic crashes claimed 1.95 million lives, with more than half of the victims being pedestrians, motorcyclists, and cyclists (WHO, Global Status Report on Road Safety, 2023). Low and middle-income countries (LMICs) account for over 90% of global traffic crash fatalities, despite possessing only 65% of the world’s registered vehicles. This paper critically reviews the literature on the costs associated with road crashes, focusing primarily on LMICs. Through a meta-analysis of existing research on the socio-economic impacts of road crashes, the study examines different methodologies and provides a comprehensive overview of the various costs that should be considered. This has significant implications for both society and policymakers. As emphasized in the report on the Performance of Latin America and the Caribbean during the first years of the Decade of Action for Road Safety ( 2015 ), road safety must be integrated into a comprehensive mobility policy that includes short-, medium-, and long-term strategies. These strategies should encompass areas such as road infrastructure, vehicle design and maintenance, user behavior, education, healthcare, and monitoring measures. Addressing these systems requires coordination among various stakeholders - government, civil society, and the private sector—highlighting the need for a robust institutional framework. Such a framework must consider the interactions between stakeholders at both local and regional levels while focusing on the protection of vulnerable road users. Despite limited resources, many governments are tasked with safeguarding their populations from preventable road fatalities, which demand consistent funding similar to other public health priorities like cancer, heart disease, malaria, suicide, and AIDS. As Gruber (2010) aptly pointed out, if the cost of a safety strategy outweighs the benefits in terms of crash prevention, resources should be redirected to more effective initiatives. Understanding crash trends, injury rates, and fatalities is crucial and requires evaluating both the direct and indirect socio-economic costs of road traffic incidents. This enables policymakers to grasp the full extent of the burden that road crashes impose on a country. It also helps assess the value of targeted investments in road safety. However, the methodologies used in such evaluations often vary across countries, leading to different estimates. Moreover, inconsistencies in national road safety data systems, underreporting, and the absence of a globally standardized evaluation method can result in biased or inaccurate cost assessments. A comprehensive analysis of the latest methodologies and their application across diverse contexts is essential to develop more accurate and effective estimates of the socio-economic costs of road crashes and fatalities. In this quantitative review, we rigorously examine the empirical literature estimating the socio-economic costs of road traffic crashes. Our goal is to offer researchers and policymakers a comprehensive and critical overview of the existing studies, highlighting key findings and identifying gaps in the literature. We focus primarily on the two main valuation approaches: the willingness-to-pay (WTP) method and the human capital approach. To conduct this quantitative and critical literature review, we utilized Google Scholar and tools like Mendeley to search for research papers containing keywords (and their variations) related to road traffic crash costs. Our analysis draws from multiple studies that estimate the socio-economic costs of road traffic crashes across different regions (e.g., Wijnen & Stipdonk, 2016; Milligan et al., 2014 ; De Blaeij et al., 2003 ; Elvik, 2000 ; Trawén et al., 2002). This process resulted in a dataset containing 100 observations on road traffic crash (RTC) costs from the period between 2000 and 2023. Our review reveals several key insights: (i) in recent decades, there has been increased academic interest in assessing the socio-economic costs of road crash fatalities and injuries, particularly in high- and middle-income countries; (ii) the importance of comparing and integrating different methodologies when estimating the socio-economic costs of road traffic crashes is underscored; (iii) the review shows that the willingness-to-pay method is predominantly used in high-income countries, whereas the human capital approach is more common in low- and middle-income countries; (iv) studies using the human capital method tend to under value total socio-economic costs by a factor of two compared to estimates derived from the WTP approach; and (v) the review emphasizes the need to account for country-specific factors, such as road safety outcomes and population density. Quantitative and quantile regression analyses have revealed that pavement conditions are a statistically significant predictor of both crash frequency and severity. Quantitative regression models indicate that poor pavement quality—characterized by factors such as surface roughness, potholes, and reduced skid resistance—is positively correlated with higher crash rates. Specifically, a deterioration in pavement condition index (PCI) is associated with a measurable increase in crash counts, particularly for wet-weather and run-off-road incidents. Quantile regression further refines this understanding by illustrating that the impact of pavement conditions is more pronounced at higher quantiles of crash severity and fatality distribution. For instance, in the upper quantiles (e.g., 75th or 90th), poor pavement conditions contribute disproportionately to fatal and severe injury crashes compared to minor ones. This suggests that while pavement issues may influence all crash types, their role in exacerbating the most serious outcomes is significantly greater. These findings underscore the critical importance of maintaining pavement quality as a targeted strategy to reduce not only crash frequency but also the likelihood of fatalities. Quantile Regression Analysis is a statistical technique used to estimate and model the relationship between variables for different quantiles (percentiles) of the dependent variable distribution, rather than focusing solely on the mean (as in ordinary least squares (OLS) regression). This approach provides a more detailed understanding of how independent variables influence different points in the distribution of the dependent variable. Why Use Quantile Regression? (Devos et al., 2015, Devos et al., 2017) Robustness to Outliers : Unlike OLS regression, which can be heavily influenced by outliers, quantile regression is more robust because it models the conditional quantiles (e.g., median, 25th percentile, 75th percentile), giving a more comprehensive view of the distribution. Detailed Analysis Across Distribution : In situations where the effect of independent variables may vary across the distribution of the dependent variable, quantile regression allows for an analysis at multiple points (e.g., the lower, median, and upper tails of the distribution). Understanding Heterogeneity : Quantile regression helps in identifying how the relationships between variables change at different levels of the dependent variable. For example, in traffic safety studies, the impact of factors like speed or traffic volume might differ for low-conflict vs. high-conflict situations. Road traffic injuries (RTIs) are a critical public health issue worldwide. In 2023, RTIs were the leading cause of death among children and young people aged 5–29 years and ranked as the eighth leading cause of death across all age groups, leading to approximately 2.5 million deaths and over 50 million injuries globally (WHO, 2023). They were also the primary cause of disability-adjusted life years (DALYs) for individuals aged 10–24 and 25–49 years, accounting for 6.6% and 5.1% of DALYs, respectively, in 2019 (Abbafati et al., 2020). In india alone, 386,285 traffic accidents occurred in 2023, resulting in 82,432 deaths, 328,305 injuries, and direct property damage amounting to RMB 145,035.9 million (NBSC, 2022). In 2019, road traffic injuries ranked fifth among the leading causes of DALYs in China (Zhou et al., 2019). Beyond the loss of life and substantial disease burden, RTIs impose a significant economic strain on victims, their families, and society at large (Bougna et al., 2022). The total cost of road crashes represents 2.8% and 2.3% of gross domestic product (GDP) in high-income and low-income countries, respectively, with injury-related costs comprising half of the total in both contexts (Wijnen & Stipdonk, 2016). Households affected by RTIs face significantly higher out-of-pocket healthcare expenses per member compared to those unaffected by road traffic injuries (Alam & Mahal, 2016). Medical costs are a significant component of the economic burden faced by individuals affected by RTIs. The medical expenses for inpatients are notably higher than those for outpatients. Previous studies on the inpatient costs associated with RTIs have mainly focused on cost descriptions and the factors influencing them. However, hospitalization costs vary greatly across countries and regions, making comparisons difficult due to differences in economic development, healthcare policies, and the quality of medical treatment across different areas. 1.1 Cost of Road Crashes The cost of road crashes encompasses a broad range of economic, social, and intangible consequences that affect individuals, businesses, governments, and societies. These costs can be classified into direct, indirect, and intangible categories, reflecting the multifaceted impact of crashes on the economy and human well-being. The cost of road crashes extends far beyond financial losses, impacting lives and productivity. Reducing these costs requires a holistic approach that includes prevention, enforcement, education, and effective response systems. Investments in road safety are not only life-saving but also economically beneficial, contributing to the sustainable development of societies. 1.2 Contributing factors of road crashes The cost of road crashes encompasses direct, indirect, and intangible economic burdens borne by individuals, families, businesses, and governments. These costs result from fatalities, injuries, and damage to property, affecting both the economy and societal well-being. Understanding the contributing factors is essential for addressing these costs effectively. The cost of road crashes is a multidimensional issue with far-reaching consequences. It is essential to identify and address contributing factors through targeted interventions. By investing in prevention, policy reforms, and technological advancements, societies can reduce the economic and social burdens of road traffic crashes, thereby fostering safer and more sustainable communities. 2. Road traffic crashes and problems Road traffic crashes involve complex interactions of various elements, including human, vehicle, road, and environmental factors. Understanding the fundamental concepts related to road traffic crashes is crucial for designing effective safety measures. Here are key concepts that play a role in the occurrence and prevention of road traffic crashes: Crash Causation Factors Human Factors : Driver error is a major cause of traffic crashes, often linked to speeding, distracted driving, impaired driving (due to alcohol or drugs), fatigue, or lack of experience. Vehicle Factors : Vehicle conditions such as mechanical failure, tire blowouts, or faulty brakes can contribute to crashes. The design of safety features like airbags and anti-lock brakes also influences crash outcomes. Road and Environmental Factors : Poor road design, inadequate signage, lack of lighting, weather conditions (e.g., rain, fog, snow), and road surface conditions can increase the likelihood of crashes. Intersections, curves, and steep inclines are common locations for road-related crashes. Crash Types and Patterns Types of Crashes : Common types include rear-end collisions, head-on collisions, side-impact (T-bone) crashes, rollovers, and single-vehicle run-off-road crashes. Crash Patterns : Certain road features or intersections may have higher crash rates due to specific patterns, such as a high number of left-turn crashes at intersections. Identifying these patterns helps in targeting safety improvements. Crash Severity Crash severity is classified by the level of injury or damage caused, such as property damage only, injury, and fatality. Severity often depends on factors like speed, collision type, occupant protection, and the size/weight of the vehicles involved. Severity Reduction Measures: Using safety features like seat belts, airbags, speed limits, and barriers can reduce the severity of crashes, even if they do not prevent the crash from occurring. Crash Prevention and Control Measures Engineering Solutions: Includes road design improvements like adding medians, guardrails, roundabouts, and lighting. Proper road signage, lane markings, and traffic signals also play a key role in crash prevention. Enforcement and Regulation: Laws and regulations, such as speed limits, DUI checks, and seat belt enforcement, aim to reduce risky behaviors and ensure safer driving practices. Education and Awareness: Public awareness campaigns and driver education programs focus on encouraging safe driving behaviors and reducing risk factors. Risk Factors and Vulnerable Road Users Risk Factors: Include high-speed roads, complex intersections, high traffic volumes, and areas with limited visibility. Reducing these factors lowers crash probability. Vulnerable Road Users (VRUs): Pedestrians, cyclists, and motorcyclists are particularly susceptible to severe injury in crashes. Measures like dedicated bike lanes, pedestrian crossings, and speed calming help protect VRUs. Crash Analysis and Data Collection Crash Data Collection: Accurate data on crash location, time, type, and contributing factors are critical for analysis. This data often comes from police reports, traffic cameras, or sensor data. Crash Analysis Tools: Predictive models (such as Safety Performance Functions) and software tools help analyze crash trends, assess risk levels, and predict the impacts of road design or regulatory changes on crash rates. Traffic Safety Models The Haddon Matrix: A framework used to analyze crashes in terms of pre-crash, crash, and post-crash phases, addressing human, vehicle, and environmental factors in each phase. Safe System Approach: Recognizes that while human errors are inevitable, the road system should be designed to prevent crashes or reduce their severity when they occur. This approach includes safer roads, safer speeds, safer vehicles, and post-crash response. Problems Associated with Road Traffic Crashes Road traffic crashes (RTCs) have far-reaching consequences, impacting individuals, communities, and nations on multiple levels. These problems can be categorized into social, economic, health, and environmental domains. Health Problems like Survivors may face severe physical injuries such as traumatic brain injuries, spinal cord damage, and amputations. And Post-Traumatic Stress Disorder (PTSD), anxiety, and depression are common among crash survivors and witnesses. Economic Problems like Direct costs include medical expenses, vehicle damage, and legal fees and Indirect Costs Loss of productivity due to injuries or fatalities, and care giving responsibilities for families. Environmental Problems like Traffic congestion caused by crashes increases fuel consumption and emissions, contributing to air pollution. Poorly managed crash sites can lead to hazardous material spills, affecting soil and water quality. 3. Methodology and Data Collection Concepts of Road Traffic Crashes – Road traffic crashes (RTCs) involve the unintended and sudden collision of one or more vehicles, pedestrians, cyclists, or other road users. These crashes result in injuries, fatalities, and property damage. Understanding the concepts and dynamics of RTCs is crucial for improving road safety, developing effective prevention strategies, and reducing the impact of such incidents on society. Road Traffic Crashes (RTCs) are complex events involving vehicles, pedestrians, cyclists, and infrastructure that result in harm to individuals or damage to property. Understanding the concepts of RTCs requires analyzing their causes, dynamics, consequences, and prevention strategies. To assess the contribution of pavement conditions to traffic crashes and fatalities, both Ordinary Least Squares (OLS) regression and Quantile Regression (QR) techniques were employed. These methods allow for a comprehensive evaluation of the relationship between pavement-related variables and crash outcomes across different levels of severity. Data Collection and Variable Selection Crash data were obtained from national and regional traffic safety databases, including detailed information on crash severity, location, time, and environmental conditions. Pavement condition data were collected from transportation agencies, primarily using Pavement Condition Index (PCI), International Roughness Index (IRI ) , and Skid Resistance (SR) as quantitative measures. Other covariates included weather conditions, lighting, traffic volume, road geometry, and driver behavior factors. Quantitative (OLS) Regression Analysis OLS regression was first applied to estimate the average effect of pavement condition variables on crash frequency and fatality counts. The results indicated a statistically significant negative relationship between PCI and crash frequency—i.e., as pavement quality deteriorates, the number of crashes increases. For every 10-point decrease in PCI, the average crash frequency increased by approximately 5–8%, depending on road type and traffic volume. Similarly, lower skid resistance values were associated with higher fatal crash rates, particularly during wet road surface conditions. Quantile Regression Analysis While OLS provides insight into the average effects, Quantile Regression was used to explore how pavement conditions affect different points in the distribution of crash severity (e.g., 25th, 50th, 75th, and 90th percentiles). The QR results revealed that the effect of poor pavement conditions intensifies at higher quantiles, meaning the worst pavement conditions disproportionately contribute to severe and fatal crashes. For example, at the 90th percentile of crash severity, a decrease in PCI showed a 12–15% increase in fatal crash likelihood, compared to only 3–5% at the 25th percentile. This suggests that pavement degradation is a more critical factor in high-severity and fatal crashes than in minor ones. 3.1 Costs Definitions and Indicators A road crash is typically defined as an incident involving a road vehicle on a public road that results in at least one person being injured or killed. A road fatality refers to someone who dies within thirty days as a consequence of a road crash (SWOV, 2010; United Nations, 2017). Road traffic crashes impose significant financial and economic costs on society, leading to reduced productivity and increased medical and resource expenditures. The costs associated with road crashes are not only monetary—encompassing lost productivity, medical expenses, property damage, and funeral costs—but also non-monetary, including the pain and suffering endured by victims and their loved ones (Alfaro et al., 1994 ; Elvik, 2000 ; Wijnen & Stipdonk, 2016). Table 1 Components of the Cost of Road Crashes. Fatality costing Injury costing Vehicle and other costs Work place and household losses Workplace and household output losses Vehicle repairs and towing Quality of life Medical and other related costs Vehicle unavailability Pain, grief and suffering Ambulance, Police and other emergency Ambulance costs Emergency services costs Travel delay Health costs of local air pollution Hospital and medical Long-term care cost Additional vehicle operating costs Premature funeral Legal costs Cost of emergency services response Elvik found that costs nearly double when intangible factors such as loss of life and reduced quality of life are included. Similarly, Wijnen et al. (2018) demonstrated that human costs constitute between 40% and 90% of total costs in European countries using a willingness-to-pay approach (discussed below). These costs have severe implications for both the economy and society. In addition to grief and suffering, road crashes impose significant economic losses on victims, their families, and society as a whole. The socio-economic costs of traffic crashes generally encompass both the economic value of personal and material damages, as well as the pain and suffering caused by these incidents. Perovic and Dimitris (2008) defined internal crash costs as damages and risks to the individual traveling by a particular vehicle or mode, while external crash costs are the uncompensated damages and risks imposed on others by that individual. Both internal and external crash costs make up the socio-economic costs of crashes. However, purely financial transfers, such as taxes and fines, are not included in these calculations because they represent costs for the payer but benefits for the recipient, resulting in no net cost at the societal level (Wijnen et al., 2017). Additionally, socio-economic costs are often categorized into market and non-market costs. Market costs can be directly measured in monetary terms and include safety equipment expenses, uncompensated property damage, loss of income, medical costs, and insurance deductibles. Non-market costs, which require estimation, include uncompensated pain, loss of quality of life for crash victims, grief suffered by loved ones, and reduced motorized mobility (see Perovic and Dimitris, 2008, for a comprehensive description). The costs can be classified into five main categories: lost output, medical costs, human costs, property damage, and administrative costs (including emergency services, insurance, and judicial expenses). The most commonly used indicator for assessing the extent of damage caused by road crashes is the gross domestic product (GDP) or gross national product (GNP) of a country. GDP/GNP serves as the standard measure for gauging the magnitude of costs and facilitating international comparisons. International studies (Elvik, 1995 ; Wijnen & Stipdonk, 2016; Wijnen et al., 2018) estimate that the socio-economic costs of road crashes range from 0.5–7.0% of GDP annually. This highlights the significant financial impact of these costs, underscoring the need for thorough analysis, especially in developing countries where road traffic casualties are increasingly prevalent (WHO, 2015). 3.2 Quantitative Analysis Quantitative analysis of the social costs of road traffic crashes involves assessing the direct, indirect, and intangible impacts of crashes in monetary terms. This comprehensive approach helps policymakers understand the economic burden and prioritize road safety interventions effectively. Direct Costs Medical Costs Costs for treatment of injuries (hospitalization, surgeries, medications, rehabilitation), emergency Services of Expenses for ambulance services, police response, and fire rescue operations. And property damage of repair or replacement of vehicles, infrastructure, and other damaged assets. Legal and Administrative Costs of insurance claims, court proceedings, and crash investigations. Indirect Costs Productivity Loss of income due to death, disability, or injury, for both victims and caregivers. Workforce Replacement of Costs to employers for recruiting and training new employees to replace injured or deceased workers. Traffic Disruption Costs of Economic losses caused by delays, congestion, or rerouting after a crash. Intangible Costs Pain and Suffering of Psychological distress experienced by crash victims and their families. Loss of Quality of Life Reduced physical and mental well-being for survivors with disabilities. And Grief and Bereavement of Emotional loss faced by families of deceased victims. Methods of Assessment Various methodologies have been developed and applied to estimate the costs of road crashes, but no single technique is universally accepted (Perovic & Dimitris, 2008). International guidelines generally recommend three approaches for estimating these costs: the Restitution Costs (RC) approach, the Human Capital (HC) approach, and the Willingness-to-Pay (WTP) approach (Alfaro et al., 1994 ; ERSO, 2008; Wijnen et al., 2018). In the following subsections, we will present and discuss the two most common methods used to estimate the costs of road traffic crashes and fatalities, as well as their application in our regression analysis. Willingness to pay (WTP) Method The Willingness to Pay (WTP) method is increasingly used in the context of road traffic accidents to estimate the value of reducing road traffic risks, improving road safety, or assessing the economic impact of traffic accidents. This method helps quantify the monetary value individuals place on reducing the likelihood of accidents or improving safety measures. Here’s how WTP is applied in the context of road traffic accidents: Applications of WTP in Road Traffic Accidents : Valuing Road Safety Improvements : Estimate how much people are willing to pay for enhancements in road safety, such as better road design, improved traffic signals, or enhanced enforcement of traffic laws. Use contingent valuation surveys to ask respondents how much they would be willing to pay for specific safety improvements. For example, respondents might be asked about their WTP for improved pedestrian crossings or better lighting at intersections. Assessing the Value of Reducing Accident Risks : Estimate the economic value individuals place on reducing the risk of being involved in a traffic accident. Surveys may be conducted to determine how much people are willing to pay for a reduction in the probability of accidents, such as the implementation of advanced safety technologies (e.g., collision avoidance systems) or improved road infrastructure. Evaluating the Economic Impact of Traffic Accidents : Quantify the economic burden of traffic accidents, including medical costs, property damage, and lost productivity. Use WTP to estimate the value of reducing the number of traffic accidents or the severity of injuries resulting from accidents. This can be integrated into cost-benefit analyses for road safety interventions or policy decisions. Quantifying the Value of Human Life and Health : Estimate the value individuals place on preventing fatalities and serious injuries in traffic accidents. Apply WTP to estimate the value of statistical life (VSL), which represents the amount people are willing to pay to reduce the risk of fatal accidents. This is often used in cost-benefit analyses of road safety measures and public health interventions. How the WTP Method is applied : Survey Design : Develop hypothetical scenarios where respondents are asked how much they would be willing to pay for specific safety improvements or risk reductions. For example, surveys might ask respondents about their willingness to pay for a new safety feature in their vehicle or improved road conditions. Use choice modelling to present respondents with different safety attributes (e.g., reduced accident risk, improved road signage) and ask them to choose their preferred options. The trade-offs between different attributes help estimate the value placed on each safety improvement. Data Collection : Collect data from a representative sample of the population to ensure that the findings are generalizable. Surveys can be conducted online, via telephone, or in-person. Clearly describe the safety improvements or risk reductions being evaluated, ensuring that respondents understand the context and implications of their responses. Data Analysis : Use statistical models to analyze survey responses and estimate the average WTP for different safety improvements or risk reductions. Techniques such as regression analysis are commonly employed to derive estimates from the data. Integrate WTP estimates into cost-benefit analyses to evaluate the economic feasibility of road safety interventions. This involves comparing the estimated benefits (in terms of WTP) to the costs of implementing the safety measures. The two primary methods for measuring the willingness-to-pay (WTP) are Revealed Preference (RP) and Stated Preference (SP) methods. RP methods assess risk reductions based on actual behavior, such as how much individuals spend on safety measures or how they trade off wages for work-related risks. In contrast, SP methods involve questionnaires where individuals are asked directly or indirectly about their willingness to pay for safety measures. The most commonly used SP methods are the Contingent Valuation Method (CV) and Conjoint Analysis (CA). Theoretically, RP methods are considered more reliable than SP methods because RP methods are based on actual expenditures, while SP methods rely on hypothetical responses (Wijnen et al., 2009). However, SP methods have several drawbacks, including difficulties in execution, high costs, extended study durations that can last over a year, and challenges in accurately estimating willingness to pay (Wesemann, 2000). Additionally, SP methods often face the behavioral economics issue where stated intentions differ from actual behaviors (Abelson, 2008 ). The WTP method may not always accurately reflect individuals' capacity to pay and can sometimes lead to overestimation (Gunnar, 1999; Lindberg, 1999 ). Furthermore, surveys have indicated that respondents tend to be relatively insensitive to small variations in risk (Bahamonde-Birke et al., 2015 ). Equation for Estimating WTP : In general, the WTP can be estimated using a regression model based on survey data. Here’s a simplified representation of the equation: WTP as a Function of Risk Reduction : If ‘R’ represents the reduction in risk (e.g., probability of an accident), the equation might be: WTP i = β 0 ​+β 1 ​R i ​+ϵ i​ − (1) Where: WTP i​ is the willingness to pay for the i th respondent. R i​ is the risk reduction that the respondent is asked to pay for. β 0 ​ is the constant term. β 1 ​ represents the change in WTP for a unit change in risk reduction. ϵ i ​ is the error term for the i th respondent. Human Capital or GDP Method The human capital method measures the economic losses to households and workplaces resulting from death or injury. It quantifies the value lost in terms of reduced economic productivity due to the inability to work, earn income, or contribute to household activities. This approach is based on the estimated production potential of the deceased or disabled individual over their lifetime had the road crash not occurred. The value in this method is typically calculated using economic indicators such as average monthly or weekly earnings and life expectancy. The cost is represented by the crash-related loss of future productivity. However, some researchers mistakenly include hospital costs, property damages, administrative expenses, and non-monetary costs within the human capital approach, when in reality, these aspects fall under the restitution costs approach. The Human Capital Method estimates the economic costs of road traffic crashes based on the value of lost productivity due to fatalities, injuries, and disability. It considers both direct and indirect costs. Direct Costs : Medical expenses (hospitalization, rehabilitation, emergency services) Property damage (vehicles, infrastructure) Legal and administrative costs (insurance, legal proceedings) Indirect Cost Loss of current and future earnings due to death, disability, or injury Productivity losses (temporary or permanent inability to work) Costs associated with the loss of human capital (skills, experience) The Human Capital Method can be represented by the following formula: Cost of Road Traffic Crash = ∑ (Direct Costs + Indirect Costs) - (2) Where: Direct Costs include immediate expenses like medical treatment and property damage. Indirect Costs include loss of productivity, lost future income, and other intangible losses due to injury or death. The method calculates the present value of lost future income by discounting future earnings to account for the time value of money. It may also include the costs of pain, suffering, and reduced quality of life. Although the willingness-to-pay and human capital approaches are the most commonly used methods, it is crucial to emphasize that socio-economic cost estimation often requires integrating multiple methodologies. These two approaches, while frequently compared, have distinct differences and limitations. First, the human capital approach focuses on estimating the value of lost productive capacity due to a traffic death or injury, whereas the willingness-to-pay approach assesses the value of lost quality of life. Second, while these methods are sometimes viewed as alternatives for valuing human lives saved, it is important to note that they address different cost components and are, in fact, complementary (Wijnen et al., 2009). Third, the willingness-to-pay approach incorporates both material (consumption loss) and immaterial (human loss) components, while the human capital approach is limited to material losses, primarily gross production loss. Nonetheless, both methods include an estimate for consumption loss. "X-Method" for Hypothesis Testing Null Hypothesis (H0): The assumption or claim to be tested. Typically, it represents no effect or no difference. Alternative Hypothesis (H1): The counterclaim to H0. It indicates the presence of an effect or difference. Example • H0: µ = µ0​(The population mean equals a specific value.) H1: µ = µ0​(The population mean does not equal the specified value.) Calculate the value of the test statistic (X) using the appropriate formula: X = \(\:\frac{\text{O}\text{b}\text{s}\text{e}\text{r}\text{v}\text{e}\text{d}\:\text{V}\text{a}\text{l}\text{u}\text{e}-\text{E}\text{x}\text{p}\text{e}\text{c}\text{t}\text{e}\text{d}\:\text{v}\text{a}\text{l}\text{u}\text{e}}{\text{S}\text{t}\text{a}\text{n}\text{d}\text{a}\text{r}\text{d}\:\text{E}\text{r}\text{r}\text{o}\text{r}}\) - (3) 3.3 Outcome and covariates method The outcome and covariates method is a statistical approach often used in road traffic crash analysis to understand the relationship between crash outcomes (e.g., severity, frequency) and a set of explanatory variables or covariates (e.g., road conditions, driver behavior, vehicle type). This method involves identifying key factors that influence road crashes and estimating their effects using models like regression analysis (Sawyer et al., 2012). Outcome : In road traffic crash analysis, the outcome variable is the main variable of interest that we want to predict or explain. Common outcomes include: Crash Frequency : Number of crashes occurring over a certain period or location. Crash Severity : Categories such as fatal, severe injury, minor injury, or property damage only. Time-to-Crash : For studies involving near-miss situations (e.g., time-to-collision). Covariates : Covariates are the independent variables or predictors that may influence the outcome. They can be categorized as: Road Characteristics : Road type (urban, rural), number of lanes, intersection type, road surface condition. Traffic Factors : Traffic volume, vehicle speed, congestion levels. Driver Behavior : Speeding, alcohol use, distracted driving, age and experience. Environmental Conditions : Weather (rain, fog), time of day (day/night), lighting conditions. Vehicle Characteristics : Vehicle type (car, truck, motorcycle), vehicle age, safety features. Let’s consider a study that examines factors affecting the severity of road crashes at intersections: Outcome Variable : Crash severity (e.g., coded as 0 = property damage only, 1 = minor injury, 2 = severe injury, 3 = fatality). Covariates : Road Factors : Intersection control (signalized/un-signalized), road geometry, speed limit. Traffic Factors : Traffic volume, average speed. Driver Factors : Age, gender, presence of alcohol. Environmental Factors : Weather conditions, time of day. Using a multinomial logistic regression, the model can estimate the probability of each severity level given the covariates. 3.4 Quantile regression analysis Quantile regression is a useful statistical method for analyzing the relationship between hospitalization costs and various predictors when the distribution of costs is skewed or contains outliers. Unlike ordinary least squares (OLS) regression, which estimates the mean of the dependent variable, quantile regression estimates the relationship at different quantiles (e.g., the 25th, 50th, 75th percentiles) of the outcome variable, offering a more detailed understanding of the impact of covariates across the distribution (Meredith et al., 2003). Quantile Regression Model for Hospitalization Costs- Model Setup : Let: Y represents hospitalization costs. X represents the set of covariates (e.g., age, gender, severity of illness, type of insurance, comorbidities). The quantile regression model can be written as: Q T (Y/X) = β 0 ​(τ) + β 1 ​(τ) X 1 ​+ β2​(τ) X 2 ​+ ……. + β k ​(τ) X k​ − (4) Where: Q T (Y/X) is the conditional quantile (e.g., 25th, 50th, 75th percentile) of hospitalization costs given covariates X. β (τ) are the regression coefficients estimated at the τ th quantile. Calculation method and data per cost component Medical costs The following medical expenses have been covered: Transportation of casualties to the hospital Treatment in the hospital’s emergency department In-patient hospital care, including overnight stays Rehabilitation services Additionally, funeral expenses have been included as a medical cost item, though they could also fall under ‘other costs’ [3]. It is not unusual to include funeral costs within medical expenses [18], and we have adopted this approach here. However, costs related to out-patient treatments (outside of emergency department care) and non-hospital treatments (such as services provided by a general practitioner) have been excluded due to a lack of available data in Kazakhstan. These costs are presumed to be relatively minor compared to hospitalization costs. Casualty transportation expenses are estimated based on the number of casualties transported to the hospital by ambulance and the average cost per ambulance trip. Similarly, costs for emergency treatment, in-patient hospital care, and rehabilitation are calculated using the number of casualties receiving each type of care and the associated average cost (unit cost) per treatment. The unit costs for in-patient hospital care and rehabilitation are determined by the average number of days of treatment and the cost per day. Production loss Production losses arise when road casualties are unable to work, either permanently (in cases of fatalities or severe injuries) or temporarily (in cases of non-fatal injuries). In line with international best practices, we have employed the concept of gross potential production loss, which, as previously mentioned, includes consumption loss. Gross production loss has been calculated using the number of fatalities and serious injuries, the average duration of work incapacity resulting from crashes, and wage rates (as a measure of the value of gross production per unit of time). For fatalities and those permanently disabled, the duration of work incapacity corresponds to the number of productive life years remaining. This period is estimated using data from the Agency of Statistics, which includes information on the age of individuals who have sustained serious injuries or fatalities, the age of labor market entry (broken down by gender and education level), and retirement age (also by gender). Human costs To estimate the value of a statistical life (VSL) in India, which underpins the calculation of human costs, a stated preference survey was conducted with a representative sample of the Patna population. This survey aimed to determine individuals' willingness to pay for reducing the risk of fatal crashes. Stated preference methods are commonly employed to derive VSL in road safety research, particularly in out of India, where they are often preferred over revealed preference methods. Unlike revealed preference methods, which assess risk reduction values based on actual behaviors, such as purchases of safety features like airbags, stated preference methods offer broader applicability by not relying on actual purchasing data. Additionally, because consumers may lack full awareness of the risk reductions associated with their purchases, stated preference methods enable researchers to provide this information, helping respondents better understand even minor risk reductions. Administrative costs Police costs were calculated based on the number of crashes by severity, the proportion of crashes attended by police, the time spent per crash, and the average wage of police officers. The survey indicated that police attendance varied by crash severity: 55% for property damage only (PDO) crashes (N = 185), 68% for slight injury crashes (N = 208), and 92% for serious injury crashes (N = 52). Table-1 4. Results and Discussion 4.1 Quantitative Analysis of the Existing Literature Data Base Construction- To build our database, we followed a two-step approach. First, we developed a methodology for selecting papers and designed a tagging system to extract key information from them. Paper Selection Methodology : Papers for this literature review were identified through a two-step process. In the first step, we conducted Google Scholar searches and used tools like Mendeley, employing various keyword combinations: (i) road traffic crash costs, (ii) social and economic costs of road traffic crashes, and (iii) additional keywords targeting studies that assess the social and economic impacts of road traffic crashes. We also used reference lists from identified papers to enhance the screening process. In the second step, we applied an additional screening criterion to refine the list. This required each paper to meet an “academic standard” and to employ one of the two main approaches to calculate the value of a statistical life: either the human capital or the willingness-to-pay approach. Each selected paper needed to present social and economic cost results using one of these methods, as discussed in Section 2.2. Papers were included regardless of whether they were published in a formal academic journal or as working papers. Applying this two-step methodology resulted in a final sample of 25 papers, from which we constructed a dataset of 85 data points estimating the costs of road traffic crashes (RTC) for the period from 2010 to 2023. 4.2 Descriptive Analysis This subsection examines the time trend of studies, the methodologies used, and cross-country comparisons. Time Trend In recent years, there has been an increase in studies evaluating the socio-economic costs of road traffic crashes. As shown in Fig. 3 , while only 10–25 papers were published each year between 2010 and 2016, this number rose significantly to 85 studies published between 2017 and 2024, with nearly 25 studies published in 2023 alone. This upward trend highlights the growing academic focus on assessing the socio-economic impacts of road crash fatalities and injuries. Income Level of Countries Most research has focused on high-income and upper-middle-income countries, which represent over 70% of the studies in the literature (see Fig. 4). Despite accounting for more than 90% of global road crash fatalities, lower-income countries are underrepresented, with only 10% of research papers focusing on estimating the socio-economic costs of road traffic injuries and fatalities in these regions. Table-2 List of Papers by Year of Publication. Year of Publication Nos. of Paper 2000 2 2002 4 2004 8 2006 12 2008 8 2010 6 2012 10 2014 16 2016 9 2018 6 2020 10 2022 15 2023 18 Source: Author’s computations based on the constructed dataset. 4.3 Econometric Analysis For our formal econometric analysis, we refined our sample to focus on studies employing the two primary methods for calculating the value of a statistical life: the willingness-to-pay (WTP) approach and the human capital method. This adjustment reduced our sample from 110 to 95 data points. Alongside the data gathered from each study, we also collected country-specific income levels and additional information from the WHO’s Global Status Report on Road Safety 2015. This report provided metrics and indicators such as the death rate per 100,000 people, death distribution by victim and vehicle categories, availability of emergency training for medical personnel, presence of emergency room-based surveillance, and existence of a vital registration system. These variables will serve as controls to enhance the specification of our model. 4.4 Critical Analysis Table 2 presents the total costs by cost component and severity level. The total cost of road crashes was estimated at 8.5 billion in 2023, equivalent to 3.3% of the GDP. Human costs accounted for a significant portion, estimated at 80.1% of the total costs. Property damage, primarily to vehicles, was the second-largest component, representing 12.3% of total costs, while production losses made up 6.5%. Administrative and medical costs were smaller components, accounting for 1.5% and 0.6% of the total, respectively. In terms of distribution by severity, more than half of the costs (3.9 billion) were related to injuries. The cost distribution across severity levels varied by component: fatalities represented a large portion (70%) of production loss costs, while serious injuries constituted the majority (75%) of medical costs. Table-3 Police time spent by severity of crash. Hours/Crash Nos. of Policeman attending a crash Total Time Spent (hrs.) Fetal 5 7 24 Major Injuries 4 5 12 Minor Injuries 3 4 6 Table-4 Cost of Road crash in India 2023. Table-4 Cost of road crashes in India in 2023 (3–5% of GDP) Fatalities Major Injuries Minor Injuries Total Medical Cost Transportation Cost 0 1.2 1.2 2.4 Hospital Cost in Patient 0.2 21 0 21.2 Hospital Cost out Patient 0 0 0.9 0.9 Funeral Cost 6.4 22.5 1.9 30.8 Total Medical Cost 6.6 44.7 4 55.3 Production Loss 310 98 25 433 Human Cost 3258 2508 1550 7316 Vehicle Damage 135 545 645 1325 Administration Cost Police 1.8 2 1.8 5.6 Others Emergency Service 0.5 0.9 1.2 2.6 Insurance Cost 0.8 5.2 16.2 22.2 Total Administration Cost 0.9 10.5 28.6 40 Total 3707 3169.6 2267.8 9144.4 Analysis for Developing Countries The existing literature offers limited insights into the costs of road traffic crashes in low- and middle-income countries (LMICs). Findings from research on high-income countries (HICs) cannot be directly applied to LMICs, as highlighted by significant contextual differences noted in both the literature and in prior analyses. Wijnen and Stipdonk (2016) reviewed studies on the national costs of road crashes across 17 countries, ten of which were high-income and seven of which were low- and middle-income. Their analysis showed that the socio-economic costs of road crashes in HICs vary from 0.8–4.0% of GDP, with an average of 2.5%. When excluding countries that do not use the willingness-to-pay (WTP) method for valuing human costs and those that do not account for underreporting, the average cost increases to 3.0% of GDP. For LMICs that account for underreporting, the estimated costs range from 1.0–2.8% of GDP. However, none of the LMICs in the study had conducted a WTP study to estimate human costs. This gap underscores the need for more region-specific research to accurately assess the socio-economic impact of road traffic crashes in LMICs, as the absence of WTP-based studies limits comparability and may under represent the true costs. 5. Conclusion Globally, road safety is experiencing a shift from a traditional approach where road traffic crashes (RTCs) were mainly attributed to higher-risk groups like young and elderly drivers, impaired or distracted drivers, and non-users of seatbelts or helmets to a broader perspective that recognizes all vehicle travel as inherently risky. This new paradigm acknowledges that most drivers take small risks that can cumulatively lead to crashes (2023). The evidence from studies analyzed in this paper underscores the severe economic impact of RTCs, especially in low- and middle-income countries, which often lack sufficient data and research capacity to assess these costs effectively. This analysis not only highlights the existing gaps in road crash socio-economic cost analysis but also emphasizes the need for policymakers to develop integrated data systems. It calls on local road safety institutions to collect high-quality data to enable accurate cost calculations. This paper conducted a quantitative analysis of studies estimating the socio-economic costs of road traffic crashes, using both descriptive and formal econometric analyses. Our literature review included both working papers and articles from peer-reviewed journals. The primary goal of our research was to offer researchers and policymakers a comprehensive review of the existing literature on the socio-economic costs of road crashes, as well as an identification of the critical gaps in current research. With this objective, the paper focused on the two main approaches willingness-to-pay (WTP) and human capital and examined how results from these methods correlate with countries' socio-economic costs, measured as a percentage of GDP and in total economic value. The paper highlights the value of comparing or combining methodologies when estimating the socio-economic costs of road traffic crashes. Currently, countries use different approaches, with the willingness-to-pay (WTP) method predominantly applied in high-income countries, while the human capital approach is more common in low- and middle-income countries. However, research in lower-income countries remains sparse, representing only 5% of the studies analyzed. Given the financial and technical challenges associated with implementing the WTP method in these regions, the paper emphasizes the potential benefits of a hybrid approach that combines WTP, human capital, and other valuation methods to improve accuracy and adaptability in various contexts. Poor pavement conditions are significantly correlated with increased crash frequency and severity and the impact of pavement quality is not uniform; it is more pronounced for fatal and severe crashes, as shown in higher quantiles. Quantile regression reveals that traditional OLS methods may underestimate the risk posed by poor pavement for the most serious outcomes. These findings highlight the need for targeted pavement maintenance and rehabilitation, especially in high-risk corridors, to reduce crash severity and save lives. References Abdallah NM, Hakim AS, Refaeye MAE (2016) Analysis of accidents cost in Egypt using the willingness-to-pay method. Int J Traffic Transp Eng 5(1):10–18. https://doi.org/10.5923/j. ijtte.20160501.02 Abelson P (2008) Establishing a monetary value for lives saved: issues and controversies. office of Best Practices Regulation, Department of Finance and Deregulation, Canberra. Ainy, E., Soori, H., Ganjali, M., Le, H., Baghfalaki, T., 2014 Estimating Cost of Road Traffic Injuries in Iran Using Willingness to Pay (WTP) Method. PLoS ONE 9 (12), e112721. https://doi.org/10.1371/journal. pone.0112721 Alfaro JL, Fabre F, chapuis M, Cour socio economique des accidents de la route. Comission des communautes europeennes, LUXEMBOURG., Ameratunga S, Hijar M, Norton R (1994) 2006. Road traffic injuries: confronting disparities to address a global-health problem. Lancet 367, 1533–1540 Anti´c B, Vujani´c M, Lipovac K, Pe´si´c D (2011) Estimation of the traffic accidents costs in Serbia by using dominant costs model. Transport 26(4):433–440. https://doi.org/10.3846/16484142.2011.635425 Athanasios T, Apostolos Z, Eleonora P, George Y (2017) Paper on Meta-analysis of the effect of road work zones on crash occurrence. Accid Anal Prevent 108(4):1–8 Bahamonde-Birke FJ, Kunert U, Link H (2015) The Value of a Statistical Life in a Road Safety Context — A Review of the Current Literature. Transp Rev 35(4):488–511 BTCE (1996) Valuing Transport Safety in Australia. Bureau of Transport and Communications Economics, Australia BTS Bo (1990) Africa Road Safety Review Final Report. Economic cost of Road Crashes in Africa (Chap. 6) Case A, Menendez A (2011) Requiescat in Pace? The Consequences of High Priced Funerals in South Africa. Chapter 11 in Explorations of Aging. University of Chicago Press, Chicago De Blaeij A, Florax RJGM, Rietveld P, Verhoef E (2003) November. The value of statistical life in road safety: A meta-analysis. Accid Anal Prev. https://doi.org/10.1016/S0001–4575(02)00105–7 Dandona R, Kumar GA, Ameer MA, Reddy GB, Dandona L (2008) Under-reporting of road traffic injuries to the police: results from two data sources in urban India. Injury Prevention: J Int Soc Child Adolesc Injury Prev 14(6):360–365. https://doi.org/10.1136/ip.2008.0 19638 Derriks H, Mak P, U.S.D. of T (2007) Underreporting of road traffic casualties. IRTAD Special report. OECD/ International Transport Forum, Paris. DOT,. 2016. Guidance on Treatment of the Economic Value of a Statistical Life (VSL) in u.s. Department of Transportation Analyses – 2016 Adjustment, 13. Retrieved from https://www.transportation.gov/sites/dot.gov/files/docs/2016 Revised Value of a Statistical Life Guidance.pdf Elvik R (1995) An analysis of official economic valuations of traffic accident fatalities in 20 motorized countries Accident Analysis and Prevention, 27 (1995), pp. 237–247 Elvik R (2000) How much do road accidents cost the national economy? Accid. Anal Prev 32(6):849–851. https://doi.org/10.1016/S0001-4575(00)00015-4 Elvik R (2001) Cost-Benefit Analysis of Police Enforcement. Analysis, (March 2001), 1–78 Elvik R (2016) Association between increase in fixed penalties and road safety outcomes: A meta-analysis. Accid Anal Prevent 92(1):202–210 Elvik R, Bjørnskau T (2017) Safety-in-numbers: A systematic review and meta-analysis of evidence. Saf Sci 92:274–282. https://doi.org/10.1016/j.ssci.2015.07.017 Gupta et al (2023) found that inadequate surface friction was a statistically significant predictor of fatal crashes in urban corridors, aligning with findings from international literature Kasnatscheew A, Felix H, Schoenebeck S, Markus L, Hosta P (2016) Review of European Accident Cost Calculation Methods- With Regard to Vulnerable Road Users. In-Depth understanding of accident causation for Vulnerable road users (InDeV), Germany Le H, Van Geldermalsen T, Lim WL, Murphy P (2011) Deriving Accident Costs using Willingness-to-Pay Approaches-A Case Study for Singapore, 34 edn. Australasian Transport Research Forum (ATRF), Adelaide, Citeseer Lindberg G (1999) Calculating transport accident costs. Sweden: Final report of the expert advisors to the high level group on infrastructure charging (WorkGroup 3). Masniak, D. 2008 Social and Economic Costs of Road Accidents in Europe. Days of Law, 1–11 Retrieved from http://www.law.muni.cz/sborniky/dp08/files/pdf/financ/masniak.pdf . Lindhjem H, Navrud S, Braathen NA, Biausque V (2011) Valuing mortality risk reductions from environmental, transport, and health policies: A global meta-analysis of stated preference studies. Risk Anal 31 (9), 1381–1407. https://doi.org/10.1111/j.1539-6924.2011.01694.x McMahon K, Dahdah S, The True Cost of Road Crashes: Valuing Life and the Cost of a Serious Injury. International Road Assessment Programme., Miller TR (2008) 2000. Variations between countries in values of statistical life. J. Transp. Econ. Pol. 34 (2), 169–188 Miller TR, Bhattacharya S, Zaloshnja E, Taylor D, Bahar G, David I (2011) Costs of Crashes to Government, United States, 2008. Ann Adv Autom Med Annu Sci Conf 55:347–355 Milligan C, Kopp A, Dahdah S, Montufar J (2014) Value of a statistical life in road safety: A benefit-transfer function with risk-analysis guidance based on developing country data. Accid Anal Prev 71:236–247. https://doi.org/10.1016/j. aap.2014.05.026 Social cost of road crashes and injuries 2015 update. Mofadal, Ministry of Transport, New Zealand, Kanitpong AIA (2015) K., 2016. Analysis of road traffic accident costs in sudan using the human capital method. Transportation Engineering Program. Asian Institute of Technology (AIT), Klong Luang, Bathumthani, Thailand Mohan D (2001) Social cost of Road Traffic Crashes in India. Tranportation Research and Injury Prevention Programme Indian Institute of Technology, New Delhi Odero W, Khayesi M, Heda PM (2003) Road traffic injuries in Kenya: Magnitude, causes and status of intervention. Injury Control Saf Promot 10(1–2):53–61. https://doi.org/10.1076/icsp.10.1.53.14103 Olukoga A, Harris G (2006) Field data: Distributions and costs of road-traffic fatalities in South Africa. Traffic Injury Prevent 7(4):400–402. https://doi.org/10.1080/15389580600847560 Pan American Health Organization (2010) (n.d.). Eastimation of the Impact of Road Traffic Injuries in Belize. Peden, M., Scurfield, R., Sleet, D., Mohan, D., Hyder, A.A., Jarawan, E., 2004. World report on road traffic injury prevention: summary. World Health Organization, Geneva Performance of Latin America and the Caribbean during the first years of the Decade of Action for Road Safety (2015) Julie, Perovic, and Dimitris, Tsolakis. 2008 Sharma and Jain (2022) in IJPRT analyzed crash patterns across Indian National Highways and concluded that pavement distress significantly contributes to rear-end and run-off-road crashes, especially on curves and transition zones Valuing the social cost (2007) of crashes: is community’s willingness to pay to avoid death or injury being reflected? Adelaide, South Australia: Australasian Road Safety Research, Policing and Education Conference. Pipat, Thongchim, Pichai, Taneerananon, Paramet, Luathep, Phayada, Prapongsena Traffic accident costing for Thailand, Risbey J, Cregan T, De Silva M, H., Social Cost of Road Crashes. Australasian Transport Research Forum, 2010 (December 2009), 1–16. Retrieved from, Robinson J (2010) (1986). Philosophical origins of economic evaluation of life. the Milbank Quaterly Additional Declarations The authors declare no competing interests. Supplementary Files SupplementryPapermarkedcolor.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7296445","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":495759972,"identity":"24c7d16c-bea1-4e67-b572-5018df95f04f","order_by":0,"name":"Mansi Ranjan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDUlEQVRIiWNgGAWjYLCCBCA2YEhsfACk5RiYoaJshLUkNxsAaWMGZmYitDCAtaS3SQDpxAa4NTiAbvvZwx8e7mHIM2dPBNpSY5O+nZ3/2AOGGjsGPukGrFrMzuSlSSQ8Yyi27HkI9MuxtNydzczsBgzHkhnYZA5g13Igx4wh4QBD4oYbQFsYGw7nbjjMzCbBwHaAgU0iAbuW82+MP0C1tEkwNvxPNwBr+YdHy40cAwkkLQcSwFoY2/BpeWMG1CJRbHDmYbNBwrFkQ6DDzA0S+5J5cDssx/jjjwM2eQbH0x8++FBjJ29w/uCzBx++2cnJz8CuBQqgBkLVsIEYPPjUIymGaRkFo2AUjIJRgAQA9wRc/42PvNcAAAAASUVORK5CYII=","orcid":"","institution":"National Institute of Technology, Patna","correspondingAuthor":true,"prefix":"","firstName":"Mansi","middleName":"","lastName":"Ranjan","suffix":""},{"id":495759973,"identity":"d9d48a40-195a-4b2b-b2a8-36b239ff7e80","order_by":1,"name":"sanjeev Sinha","email":"","orcid":"","institution":"National Institute of Technology, Patna","correspondingAuthor":false,"prefix":"","firstName":"sanjeev","middleName":"","lastName":"Sinha","suffix":""}],"badges":[],"createdAt":"2025-08-05 05:11:51","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":true,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":true},"doi":"10.21203/rs.3.rs-7296445/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7296445/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88414099,"identity":"917e8939-c4d5-4288-a8ff-a27d58518e86","added_by":"auto","created_at":"2025-08-06 08:44:30","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":27180,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ethe Crash Costs Components.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7296445/v1/d8dc74adf557ad649c5bc4bb.png"},{"id":88414098,"identity":"e1704cad-b69c-4f75-9f78-dc6ba02a941d","added_by":"auto","created_at":"2025-08-06 08:44:30","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":13435,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eList of Papers by Country Income Classes, organized according to the income classification based on the constructed dataset.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7296445/v1/4a9918b2feb1690124f66d2b.png"},{"id":88414102,"identity":"b4250532-c8cf-42b9-ab11-ee4198d285c1","added_by":"auto","created_at":"2025-08-06 08:44:30","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":33496,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eResearch Paper publication\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7296445/v1/891c445d4fb939aa9269e3d1.png"},{"id":88416523,"identity":"53206961-39a6-475a-97c0-2fb3922c9c5b","added_by":"auto","created_at":"2025-08-06 09:00:31","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":13108,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGDP % HC Method\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7296445/v1/610e21f14887f2a941913c07.png"},{"id":88416522,"identity":"0e98902b-4e77-4232-a336-833dba7add79","added_by":"auto","created_at":"2025-08-06 09:00:30","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":12263,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGDP % WTP Method\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7296445/v1/42b06ecba84302fc1d931fb9.png"},{"id":88415222,"identity":"b1062992-23b2-432d-a581-3084e825b7af","added_by":"auto","created_at":"2025-08-06 08:52:30","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":14645,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSeverity of Crash\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7296445/v1/da0bef7239395fb6244927a6.png"},{"id":88414105,"identity":"2d0b805e-8a61-44ed-831a-0377d01fe430","added_by":"auto","created_at":"2025-08-06 08:44:30","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":16364,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCost of Road Crash\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-7296445/v1/9daf9850e93bcf2954326a92.png"},{"id":88417732,"identity":"740d6abd-3f47-4bf0-a029-1c1b85248c62","added_by":"auto","created_at":"2025-08-06 09:08:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1913530,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7296445/v1/f1d43cb2-12e9-490a-a359-dc119e772155.pdf"},{"id":88414100,"identity":"32bca13a-cf34-4b92-9e28-a285f2ad1a81","added_by":"auto","created_at":"2025-08-06 08:44:30","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":91066,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementryPapermarkedcolor.docx","url":"https://assets-eu.researchsquare.com/files/rs-7296445/v1/1a0fcf868664901e137fc752.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eIdentifying the Social Costs of Road Traffic Crashes in India through Quantitative and Quantile Regression Analysis\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eRoad crashes are reported daily across the globe. In 2019\u0026ndash;2023 between, road traffic crashes claimed 1.95\u0026nbsp;million lives, with more than half of the victims being pedestrians, motorcyclists, and cyclists (WHO, Global Status Report on Road Safety, 2023). Low and middle-income countries (LMICs) account for over 90% of global traffic crash fatalities, despite possessing only 65% of the world\u0026rsquo;s registered vehicles. This paper critically reviews the literature on the costs associated with road crashes, focusing primarily on LMICs. Through a meta-analysis of existing research on the socio-economic impacts of road crashes, the study examines different methodologies and provides a comprehensive overview of the various costs that should be considered. This has significant implications for both society and policymakers. As emphasized in the report on the Performance of Latin America and the Caribbean during the first years of the Decade of Action for Road Safety (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), road safety must be integrated into a comprehensive mobility policy that includes short-, medium-, and long-term strategies. These strategies should encompass areas such as road infrastructure, vehicle design and maintenance, user behavior, education, healthcare, and monitoring measures. Addressing these systems requires coordination among various stakeholders - government, civil society, and the private sector\u0026mdash;highlighting the need for a robust institutional framework. Such a framework must consider the interactions between stakeholders at both local and regional levels while focusing on the protection of vulnerable road users.\u003c/p\u003e\u003cp\u003eDespite limited resources, many governments are tasked with safeguarding their populations from preventable road fatalities, which demand consistent funding similar to other public health priorities like cancer, heart disease, malaria, suicide, and AIDS. As Gruber (2010) aptly pointed out, if the cost of a safety strategy outweighs the benefits in terms of crash prevention, resources should be redirected to more effective initiatives. Understanding crash trends, injury rates, and fatalities is crucial and requires evaluating both the direct and indirect socio-economic costs of road traffic incidents. This enables policymakers to grasp the full extent of the burden that road crashes impose on a country. It also helps assess the value of targeted investments in road safety. However, the methodologies used in such evaluations often vary across countries, leading to different estimates. Moreover, inconsistencies in national road safety data systems, underreporting, and the absence of a globally standardized evaluation method can result in biased or inaccurate cost assessments. A comprehensive analysis of the latest methodologies and their application across diverse contexts is essential to develop more accurate and effective estimates of the socio-economic costs of road crashes and fatalities.\u003c/p\u003e\u003cp\u003eIn this quantitative review, we rigorously examine the empirical literature estimating the socio-economic costs of road traffic crashes. Our goal is to offer researchers and policymakers a comprehensive and critical overview of the existing studies, highlighting key findings and identifying gaps in the literature. We focus primarily on the two main valuation approaches: the willingness-to-pay (WTP) method and the human capital approach. To conduct this quantitative and critical literature review, we utilized Google Scholar and tools like Mendeley to search for research papers containing keywords (and their variations) related to road traffic crash costs. Our analysis draws from multiple studies that estimate the socio-economic costs of road traffic crashes across different regions (e.g., Wijnen \u0026amp; Stipdonk, 2016; Milligan et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; De Blaeij et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Elvik, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Traw\u0026eacute;n et al., 2002). This process resulted in a dataset containing 100 observations on road traffic crash (RTC) costs from the period between 2000 and 2023.\u003c/p\u003e\u003cp\u003eOur review reveals several key insights: (i) in recent decades, there has been increased academic interest in assessing the socio-economic costs of road crash fatalities and injuries, particularly in high- and middle-income countries; (ii) the importance of comparing and integrating different methodologies when estimating the socio-economic costs of road traffic crashes is underscored; (iii) the review shows that the willingness-to-pay method is predominantly used in high-income countries, whereas the human capital approach is more common in low- and middle-income countries; (iv) studies using the human capital method tend to under value total socio-economic costs by a factor of two compared to estimates derived from the WTP approach; and (v) the review emphasizes the need to account for country-specific factors, such as road safety outcomes and population density.\u003c/p\u003e\u003cp\u003eQuantitative and quantile regression analyses have revealed that pavement conditions are a statistically significant predictor of both crash frequency and severity. Quantitative regression models indicate that poor pavement quality\u0026mdash;characterized by factors such as surface roughness, potholes, and reduced skid resistance\u0026mdash;is positively correlated with higher crash rates. Specifically, a deterioration in pavement condition index (PCI) is associated with a measurable increase in crash counts, particularly for wet-weather and run-off-road incidents. Quantile regression further refines this understanding by illustrating that the impact of pavement conditions is more pronounced at higher quantiles of crash severity and fatality distribution. For instance, in the upper quantiles (e.g., 75th or 90th), poor pavement conditions contribute disproportionately to fatal and severe injury crashes compared to minor ones. This suggests that while pavement issues may influence all crash types, their role in exacerbating the most serious outcomes is significantly greater. These findings underscore the critical importance of maintaining pavement quality as a targeted strategy to reduce not only crash frequency but also the likelihood of fatalities.\u003c/p\u003e\u003cp\u003eQuantile Regression Analysis is a statistical technique used to estimate and model the relationship between variables for different quantiles (percentiles) of the dependent variable distribution, rather than focusing solely on the mean (as in ordinary least squares (OLS) regression). This approach provides a more detailed understanding of how independent variables influence different points in the distribution of the dependent variable.\u003c/p\u003e\u003cp\u003e\u003cb\u003eWhy Use Quantile Regression?\u003c/b\u003e (Devos et al., 2015, Devos et al., 2017)\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eRobustness to Outliers\u003c/b\u003e:\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eUnlike OLS regression, which can be heavily influenced by outliers, quantile regression is more robust because it models the conditional quantiles (e.g., median, 25th percentile, 75th percentile), giving a more comprehensive view of the distribution.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eDetailed Analysis Across Distribution\u003c/b\u003e:\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eIn situations where the effect of independent variables may vary across the distribution of the dependent variable, quantile regression allows for an analysis at multiple points (e.g., the lower, median, and upper tails of the distribution).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eUnderstanding Heterogeneity\u003c/b\u003e:\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eQuantile regression helps in identifying how the relationships between variables change at different levels of the dependent variable. For example, in traffic safety studies, the impact of factors like speed or traffic volume might differ for low-conflict vs. high-conflict situations.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eRoad traffic injuries (RTIs) are a critical public health issue worldwide. In 2023, RTIs were the leading cause of death among children and young people aged 5\u0026ndash;29 years and ranked as the eighth leading cause of death across all age groups, leading to approximately 2.5\u0026nbsp;million deaths and over 50\u0026nbsp;million injuries globally (WHO, 2023). They were also the primary cause of disability-adjusted life years (DALYs) for individuals aged 10\u0026ndash;24 and 25\u0026ndash;49 years, accounting for 6.6% and 5.1% of DALYs, respectively, in 2019 (Abbafati et al., 2020). In india alone, 386,285 traffic accidents occurred in 2023, resulting in 82,432 deaths, 328,305 injuries, and direct property damage amounting to RMB 145,035.9\u0026nbsp;million (NBSC, 2022). In 2019, road traffic injuries ranked fifth among the leading causes of DALYs in China (Zhou et al., 2019). Beyond the loss of life and substantial disease burden, RTIs impose a significant economic strain on victims, their families, and society at large (Bougna et al., 2022). The total cost of road crashes represents 2.8% and 2.3% of gross domestic product (GDP) in high-income and low-income countries, respectively, with injury-related costs comprising half of the total in both contexts (Wijnen \u0026amp; Stipdonk, 2016). Households affected by RTIs face significantly higher out-of-pocket healthcare expenses per member compared to those unaffected by road traffic injuries (Alam \u0026amp; Mahal, 2016).\u003c/p\u003e\u003cp\u003eMedical costs are a significant component of the economic burden faced by individuals affected by RTIs. The medical expenses for inpatients are notably higher than those for outpatients. Previous studies on the inpatient costs associated with RTIs have mainly focused on cost descriptions and the factors influencing them. However, hospitalization costs vary greatly across countries and regions, making comparisons difficult due to differences in economic development, healthcare policies, and the quality of medical treatment across different areas.\u003c/p\u003e\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\u003ch2\u003e1.1 Cost of Road Crashes\u003c/h2\u003e\u003cp\u003eThe cost of road crashes encompasses a broad range of economic, social, and intangible consequences that affect individuals, businesses, governments, and societies. These costs can be classified into direct, indirect, and intangible categories, reflecting the multifaceted impact of crashes on the economy and human well-being. The cost of road crashes extends far beyond financial losses, impacting lives and productivity. Reducing these costs requires a holistic approach that includes prevention, enforcement, education, and effective response systems. Investments in road safety are not only life-saving but also economically beneficial, contributing to the sustainable development of societies.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e1.2 Contributing factors of road crashes\u003c/h2\u003e\u003cp\u003eThe cost of road crashes encompasses direct, indirect, and intangible economic burdens borne by individuals, families, businesses, and governments. These costs result from fatalities, injuries, and damage to property, affecting both the economy and societal well-being. Understanding the contributing factors is essential for addressing these costs effectively. The cost of road crashes is a multidimensional issue with far-reaching consequences. It is essential to identify and address contributing factors through targeted interventions. By investing in prevention, policy reforms, and technological advancements, societies can reduce the economic and social burdens of road traffic crashes, thereby fostering safer and more sustainable communities.\u003c/p\u003e\u003c/div\u003e"},{"header":"2. Road traffic crashes and problems","content":"\u003cp\u003eRoad traffic crashes involve complex interactions of various elements, including human, vehicle, road, and environmental factors. Understanding the fundamental concepts related to road traffic crashes is crucial for designing effective safety measures. Here are key concepts that play a role in the occurrence and prevention of road traffic crashes:\u003c/p\u003e\u003cp\u003e\u003cb\u003eCrash Causation Factors\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eHuman Factors\u003c/b\u003e: Driver error is a major cause of traffic crashes, often linked to speeding, distracted driving, impaired driving (due to alcohol or drugs), fatigue, or lack of experience.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eVehicle Factors\u003c/b\u003e: Vehicle conditions such as mechanical failure, tire blowouts, or faulty brakes can contribute to crashes. The design of safety features like airbags and anti-lock brakes also influences crash outcomes.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eRoad and Environmental Factors\u003c/b\u003e: Poor road design, inadequate signage, lack of lighting, weather conditions (e.g., rain, fog, snow), and road surface conditions can increase the likelihood of crashes. Intersections, curves, and steep inclines are common locations for road-related crashes.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eCrash Types and Patterns\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eTypes of Crashes\u003c/b\u003e: Common types include rear-end collisions, head-on collisions, side-impact (T-bone) crashes, rollovers, and single-vehicle run-off-road crashes.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eCrash Patterns\u003c/b\u003e: Certain road features or intersections may have higher crash rates due to specific patterns, such as a high number of left-turn crashes at intersections. Identifying these patterns helps in targeting safety improvements.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eCrash Severity\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eCrash severity is classified by the level of injury or damage caused, such as property damage only, injury, and fatality. Severity often depends on factors like speed, collision type, occupant protection, and the size/weight of the vehicles involved.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eSeverity Reduction Measures: Using safety features like seat belts, airbags, speed limits, and barriers can reduce the severity of crashes, even if they do not prevent the crash from occurring.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eCrash Prevention and Control Measures\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eEngineering Solutions: Includes road design improvements like adding medians, guardrails, roundabouts, and lighting. Proper road signage, lane markings, and traffic signals also play a key role in crash prevention.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eEnforcement and Regulation: Laws and regulations, such as speed limits, DUI checks, and seat belt enforcement, aim to reduce risky behaviors and ensure safer driving practices.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eEducation and Awareness: Public awareness campaigns and driver education programs focus on encouraging safe driving behaviors and reducing risk factors.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eRisk Factors and Vulnerable Road Users\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eRisk Factors: Include high-speed roads, complex intersections, high traffic volumes, and areas with limited visibility. Reducing these factors lowers crash probability.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eVulnerable Road Users (VRUs): Pedestrians, cyclists, and motorcyclists are particularly susceptible to severe injury in crashes. Measures like dedicated bike lanes, pedestrian crossings, and speed calming help protect VRUs.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eCrash Analysis and Data Collection\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eCrash Data Collection: Accurate data on crash location, time, type, and contributing factors are critical for analysis. This data often comes from police reports, traffic cameras, or sensor data.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eCrash Analysis Tools: Predictive models (such as Safety Performance Functions) and software tools help analyze crash trends, assess risk levels, and predict the impacts of road design or regulatory changes on crash rates.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eTraffic Safety Models\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eThe Haddon Matrix: A framework used to analyze crashes in terms of pre-crash, crash, and post-crash phases, addressing human, vehicle, and environmental factors in each phase.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eSafe System Approach: Recognizes that while human errors are inevitable, the road system should be designed to prevent crashes or reduce their severity when they occur. This approach includes safer roads, safer speeds, safer vehicles, and post-crash response.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eProblems Associated with Road Traffic Crashes\u003c/b\u003e\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eRoad traffic crashes (RTCs) have far-reaching consequences, impacting individuals, communities, and nations on multiple levels. These problems can be categorized into social, economic, health, and environmental domains. Health Problems like Survivors may face severe physical injuries such as traumatic brain injuries, spinal cord damage, and amputations. And Post-Traumatic Stress Disorder (PTSD), anxiety, and depression are common among crash survivors and witnesses. Economic Problems like Direct costs include medical expenses, vehicle damage, and legal fees and Indirect Costs Loss of productivity due to injuries or fatalities, and care giving responsibilities for families. Environmental Problems like Traffic congestion caused by crashes increases fuel consumption and emissions, contributing to air pollution. Poorly managed crash sites can lead to hazardous material spills, affecting soil and water quality.\u003c/p\u003e"},{"header":"3. Methodology and Data Collection","content":"\u003cp\u003e\u003cb\u003eConcepts of Road Traffic Crashes \u0026ndash;\u003c/b\u003e\u003c/p\u003e\u003cp\u003eRoad traffic crashes (RTCs) involve the unintended and sudden collision of one or more vehicles, pedestrians, cyclists, or other road users. These crashes result in injuries, fatalities, and property damage. Understanding the concepts and dynamics of RTCs is crucial for improving road safety, developing effective prevention strategies, and reducing the impact of such incidents on society. Road Traffic Crashes (RTCs) are complex events involving vehicles, pedestrians, cyclists, and infrastructure that result in harm to individuals or damage to property. Understanding the concepts of RTCs requires analyzing their causes, dynamics, consequences, and prevention strategies.\u003c/p\u003e\u003cp\u003eTo assess the contribution of pavement conditions to traffic crashes and fatalities, both Ordinary Least Squares (OLS) regression and Quantile Regression (QR) techniques were employed. These methods allow for a comprehensive evaluation of the relationship between pavement-related variables and crash outcomes across different levels of severity.\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eData Collection and Variable Selection\u003c/b\u003e\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eCrash data were obtained from national and regional traffic safety databases, including detailed information on crash severity, location, time, and environmental conditions. Pavement condition data were collected from transportation agencies, primarily using Pavement Condition Index (PCI), International Roughness Index (IRI\u003cb\u003e)\u003c/b\u003e, and Skid Resistance (SR) as quantitative measures. Other covariates included weather conditions, lighting, traffic volume, road geometry, and driver behavior factors.\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eQuantitative (OLS) Regression Analysis\u003c/b\u003e\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eOLS regression was first applied to estimate the average effect of pavement condition variables on crash frequency and fatality counts. The results indicated a statistically significant negative relationship between PCI and crash frequency\u0026mdash;i.e., as pavement quality deteriorates, the number of crashes increases. For every 10-point decrease in PCI, the average crash frequency increased by approximately 5\u0026ndash;8%, depending on road type and traffic volume. Similarly, lower skid resistance values were associated with higher fatal crash rates, particularly during wet road surface conditions.\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eQuantile Regression Analysis\u003c/b\u003e\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eWhile OLS provides insight into the average effects, Quantile Regression was used to explore how pavement conditions affect different points in the distribution of crash severity (e.g., 25th, 50th, 75th, and 90th percentiles). The QR results revealed that the effect of poor pavement conditions intensifies at higher quantiles, meaning the worst pavement conditions disproportionately contribute to severe and fatal crashes. For example, at the 90th percentile of crash severity, a decrease in PCI showed a 12\u0026ndash;15% increase in fatal crash likelihood, compared to only 3\u0026ndash;5% at the 25th percentile. This suggests that pavement degradation is a more critical factor in high-severity and fatal crashes than in minor ones.\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Costs Definitions and Indicators\u003c/h2\u003e\u003cp\u003eA road crash is typically defined as an incident involving a road vehicle on a public road that results in at least one person being injured or killed. A road fatality refers to someone who dies within thirty days as a consequence of a road crash (SWOV, 2010; United Nations, 2017). Road traffic crashes impose significant financial and economic costs on society, leading to reduced productivity and increased medical and resource expenditures. The costs associated with road crashes are not only monetary\u0026mdash;encompassing lost productivity, medical expenses, property damage, and funeral costs\u0026mdash;but also non-monetary, including the pain and suffering endured by victims and their loved ones (Alfaro et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Elvik, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Wijnen \u0026amp; Stipdonk, 2016).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComponents of the Cost of Road Crashes.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFatality costing\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInjury costing\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eVehicle and other costs\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWork place and household losses\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWorkplace and household output losses\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eVehicle repairs and towing\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQuality of life\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMedical and other related costs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eVehicle unavailability\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePain, grief and suffering Ambulance, Police and other emergency\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAmbulance costs Emergency services costs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTravel delay Health costs of local air pollution\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHospital and medical\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLong-term care cost\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAdditional vehicle operating costs\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePremature funeral\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLegal costs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCost of emergency services response\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eElvik found that costs nearly double when intangible factors such as loss of life and reduced quality of life are included. Similarly, Wijnen et al. (2018) demonstrated that human costs constitute between 40% and 90% of total costs in European countries using a willingness-to-pay approach (discussed below). These costs have severe implications for both the economy and society. In addition to grief and suffering, road crashes impose significant economic losses on victims, their families, and society as a whole. The socio-economic costs of traffic crashes generally encompass both the economic value of personal and material damages, as well as the pain and suffering caused by these incidents. Perovic and Dimitris (2008) defined internal crash costs as damages and risks to the individual traveling by a particular vehicle or mode, while external crash costs are the uncompensated damages and risks imposed on others by that individual. Both internal and external crash costs make up the socio-economic costs of crashes. However, purely financial transfers, such as taxes and fines, are not included in these calculations because they represent costs for the payer but benefits for the recipient, resulting in no net cost at the societal level (Wijnen et al., 2017). Additionally, socio-economic costs are often categorized into market and non-market costs. Market costs can be directly measured in monetary terms and include safety equipment expenses, uncompensated property damage, loss of income, medical costs, and insurance deductibles. Non-market costs, which require estimation, include uncompensated pain, loss of quality of life for crash victims, grief suffered by loved ones, and reduced motorized mobility (see Perovic and Dimitris, 2008, for a comprehensive description). The costs can be classified into five main categories: lost output, medical costs, human costs, property damage, and administrative costs (including emergency services, insurance, and judicial expenses).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe most commonly used indicator for assessing the extent of damage caused by road crashes is the gross domestic product (GDP) or gross national product (GNP) of a country. GDP/GNP serves as the standard measure for gauging the magnitude of costs and facilitating international comparisons. International studies (Elvik, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Wijnen \u0026amp; Stipdonk, 2016; Wijnen et al., 2018) estimate that the socio-economic costs of road crashes range from 0.5\u0026ndash;7.0% of GDP annually. This highlights the significant financial impact of these costs, underscoring the need for thorough analysis, especially in developing countries where road traffic casualties are increasingly prevalent (WHO, 2015).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Quantitative Analysis\u003c/h2\u003e\u003cp\u003eQuantitative analysis of the social costs of road traffic crashes involves assessing the direct, indirect, and intangible impacts of crashes in monetary terms. This comprehensive approach helps policymakers understand the economic burden and prioritize road safety interventions effectively.\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eDirect Costs\u003c/b\u003e\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMedical Costs\u003c/strong\u003e\u003cp\u003eCosts for treatment of injuries (hospitalization, surgeries, medications, rehabilitation), emergency Services of Expenses for ambulance services, police response, and fire rescue operations. And property damage of repair or replacement of vehicles, infrastructure, and other damaged assets. Legal and Administrative Costs of insurance claims, court proceedings, and crash investigations.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eIndirect Costs\u003c/b\u003e\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eProductivity Loss of income due to death, disability, or injury, for both victims and caregivers. Workforce Replacement of Costs to employers for recruiting and training new employees to replace injured or deceased workers. Traffic Disruption Costs of Economic losses caused by delays, congestion, or rerouting after a crash.\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eIntangible Costs\u003c/b\u003e\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003ePain and Suffering of Psychological distress experienced by crash victims and their families. Loss of Quality of Life Reduced physical and mental well-being for survivors with disabilities. And Grief and Bereavement of Emotional loss faced by families of deceased victims.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods of Assessment\u003c/b\u003e\u003c/p\u003e\u003cp\u003eVarious methodologies have been developed and applied to estimate the costs of road crashes, but no single technique is universally accepted (Perovic \u0026amp; Dimitris, 2008). International guidelines generally recommend three approaches for estimating these costs: the Restitution Costs (RC) approach, the Human Capital (HC) approach, and the Willingness-to-Pay (WTP) approach (Alfaro et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; ERSO, 2008; Wijnen et al., 2018). In the following subsections, we will present and discuss the two most common methods used to estimate the costs of road traffic crashes and fatalities, as well as their application in our regression analysis.\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eWillingness to pay (WTP) Method\u003c/b\u003e\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eThe \u003cb\u003eWillingness to Pay (WTP) method\u003c/b\u003e is increasingly used in the context of road traffic accidents to estimate the value of reducing road traffic risks, improving road safety, or assessing the economic impact of traffic accidents. This method helps quantify the monetary value individuals place on reducing the likelihood of accidents or improving safety measures. Here\u0026rsquo;s how WTP is applied in the context of road traffic accidents:\u003c/p\u003e\u003cp\u003e\u003cb\u003eApplications of WTP in Road Traffic Accidents\u003c/b\u003e:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eValuing Road Safety Improvements\u003c/b\u003e:\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eEstimate how much people are willing to pay for enhancements in road safety, such as better road design, improved traffic signals, or enhanced enforcement of traffic laws. Use contingent valuation surveys to ask respondents how much they would be willing to pay for specific safety improvements. For example, respondents might be asked about their WTP for improved pedestrian crossings or better lighting at intersections.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eAssessing the Value of Reducing Accident Risks\u003c/b\u003e:\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eEstimate the economic value individuals place on reducing the risk of being involved in a traffic accident. Surveys may be conducted to determine how much people are willing to pay for a reduction in the probability of accidents, such as the implementation of advanced safety technologies (e.g., collision avoidance systems) or improved road infrastructure.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eEvaluating the Economic Impact of Traffic Accidents\u003c/b\u003e:\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eQuantify the economic burden of traffic accidents, including medical costs, property damage, and lost productivity. Use WTP to estimate the value of reducing the number of traffic accidents or the severity of injuries resulting from accidents. This can be integrated into cost-benefit analyses for road safety interventions or policy decisions.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eQuantifying the Value of Human Life and Health\u003c/b\u003e:\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eEstimate the value individuals place on preventing fatalities and serious injuries in traffic accidents. Apply WTP to estimate the value of statistical life (VSL), which represents the amount people are willing to pay to reduce the risk of fatal accidents. This is often used in cost-benefit analyses of road safety measures and public health interventions.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eHow the WTP Method is applied\u003c/b\u003e:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eSurvey Design\u003c/b\u003e:\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eDevelop hypothetical scenarios where respondents are asked how much they would be willing to pay for specific safety improvements or risk reductions. For example, surveys might ask respondents about their willingness to pay for a new safety feature in their vehicle or improved road conditions. Use choice modelling to present respondents with different safety attributes (e.g., reduced accident risk, improved road signage) and ask them to choose their preferred options. The trade-offs between different attributes help estimate the value placed on each safety improvement.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eData Collection\u003c/b\u003e:\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eCollect data from a representative sample of the population to ensure that the findings are generalizable. Surveys can be conducted online, via telephone, or in-person. Clearly describe the safety improvements or risk reductions being evaluated, ensuring that respondents understand the context and implications of their responses.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eData Analysis\u003c/b\u003e:\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eUse statistical models to analyze survey responses and estimate the average WTP for different safety improvements or risk reductions. Techniques such as regression analysis are commonly employed to derive estimates from the data. Integrate WTP estimates into cost-benefit analyses to evaluate the economic feasibility of road safety interventions. This involves comparing the estimated benefits (in terms of WTP) to the costs of implementing the safety measures.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe two primary methods for measuring the willingness-to-pay (WTP) are Revealed Preference (RP) and Stated Preference (SP) methods. RP methods assess risk reductions based on actual behavior, such as how much individuals spend on safety measures or how they trade off wages for work-related risks. In contrast, SP methods involve questionnaires where individuals are asked directly or indirectly about their willingness to pay for safety measures. The most commonly used SP methods are the Contingent Valuation Method (CV) and Conjoint Analysis (CA). Theoretically, RP methods are considered more reliable than SP methods because RP methods are based on actual expenditures, while SP methods rely on hypothetical responses (Wijnen et al., 2009). However, SP methods have several drawbacks, including difficulties in execution, high costs, extended study durations that can last over a year, and challenges in accurately estimating willingness to pay (Wesemann, 2000). Additionally, SP methods often face the behavioral economics issue where stated intentions differ from actual behaviors (Abelson, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). The WTP method may not always accurately reflect individuals' capacity to pay and can sometimes lead to overestimation (Gunnar, 1999; Lindberg, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). Furthermore, surveys have indicated that respondents tend to be relatively insensitive to small variations in risk (Bahamonde-Birke et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eEquation for Estimating WTP\u003c/b\u003e:\u003c/p\u003e\u003cp\u003eIn general, the WTP can be estimated using a regression model based on survey data. Here\u0026rsquo;s a simplified representation of the equation:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eWTP as a Function of Risk Reduction\u003c/b\u003e: If \u0026lsquo;R\u0026rsquo; represents the reduction in risk (e.g., probability of an accident), the equation might be:\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eWTP\u003c/b\u003e\u003csub\u003e\u003cb\u003ei\u003c/b\u003e\u003c/sub\u003e\u0026thinsp;\u003cb\u003e=\u0026thinsp;β\u003c/b\u003e\u003csub\u003e\u003cb\u003e0\u003c/b\u003e\u003c/sub\u003e\u003cb\u003e​+β\u003c/b\u003e\u003csub\u003e\u003cb\u003e1\u003c/b\u003e\u003c/sub\u003e\u003cb\u003e​R\u003c/b\u003e\u003csub\u003e\u003cb\u003ei\u003c/b\u003e\u003c/sub\u003e\u003cb\u003e​+ϵ\u003c/b\u003e\u003csub\u003e\u003cb\u003ei​ \u0026minus; (1)\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e\u003cp\u003eWhere:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eWTP\u003csub\u003ei​\u003c/sub\u003e is the willingness to pay for the i\u003csup\u003eth\u003c/sup\u003e respondent.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eR\u003csub\u003ei​\u003c/sub\u003e is the risk reduction that the respondent is asked to pay for.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eβ\u003csub\u003e0\u003c/sub\u003e​ is the constant term.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eβ\u003csub\u003e1\u003c/sub\u003e ​ represents the change in WTP for a unit change in risk reduction.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eϵ\u003csub\u003ei\u003c/sub\u003e​ is the error term for the i\u003csup\u003eth\u003c/sup\u003e respondent.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eHuman Capital or GDP Method\u003c/b\u003e\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eThe human capital method measures the economic losses to households and workplaces resulting from death or injury. It quantifies the value lost in terms of reduced economic productivity due to the inability to work, earn income, or contribute to household activities. This approach is based on the estimated production potential of the deceased or disabled individual over their lifetime had the road crash not occurred. The value in this method is typically calculated using economic indicators such as average monthly or weekly earnings and life expectancy. The cost is represented by the crash-related loss of future productivity. However, some researchers mistakenly include hospital costs, property damages, administrative expenses, and non-monetary costs within the human capital approach, when in reality, these aspects fall under the restitution costs approach. The Human Capital Method estimates the economic costs of road traffic crashes based on the value of lost productivity due to fatalities, injuries, and disability. It considers both direct and indirect costs.\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eDirect Costs\u003c/b\u003e:\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eMedical expenses (hospitalization, rehabilitation, emergency services)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eProperty damage (vehicles, infrastructure)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eLegal and administrative costs (insurance, legal proceedings)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eIndirect Cost\u003c/b\u003e\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eLoss of current and future earnings due to death, disability, or injury\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eProductivity losses (temporary or permanent inability to work)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eCosts associated with the loss of human capital (skills, experience)\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eThe Human Capital Method can be represented by the following formula:\u003c/p\u003e\u003cp\u003e\u003cb\u003eCost of Road Traffic Crash = \u0026sum; (Direct Costs\u0026thinsp;+\u0026thinsp;Indirect Costs) - (2)\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWhere:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eDirect Costs\u003c/b\u003e include immediate expenses like medical treatment and property damage.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eIndirect Costs\u003c/b\u003e include loss of productivity, lost future income, and other intangible losses due to injury or death.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eThe method calculates the present value of lost future income by discounting future earnings to account for the time value of money. It may also include the costs of pain, suffering, and reduced quality of life. Although the willingness-to-pay and human capital approaches are the most commonly used methods, it is crucial to emphasize that socio-economic cost estimation often requires integrating multiple methodologies. These two approaches, while frequently compared, have distinct differences and limitations.\u003c/p\u003e\u003cp\u003eFirst, the human capital approach focuses on estimating the value of lost productive capacity due to a traffic death or injury, whereas the willingness-to-pay approach assesses the value of lost quality of life. Second, while these methods are sometimes viewed as alternatives for valuing human lives saved, it is important to note that they address different cost components and are, in fact, complementary (Wijnen et al., 2009). Third, the willingness-to-pay approach incorporates both material (consumption loss) and immaterial (human loss) components, while the human capital approach is limited to material losses, primarily gross production loss. Nonetheless, both methods include an estimate for consumption loss.\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003e\"X-Method\" for Hypothesis Testing\u003c/b\u003e\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eNull Hypothesis (H0): The assumption or claim to be tested. Typically, it represents no effect or no difference.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eAlternative Hypothesis (H1): The counterclaim to H0. It indicates the presence of an effect or difference.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eExample\u003c/strong\u003e\u003cp\u003e\u0026bull; H0: \u0026micro;\u0026thinsp;=\u0026thinsp;\u0026micro;0​(The population mean equals a specific value.)\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eH1: \u0026micro;\u0026thinsp;=\u0026thinsp;\u0026micro;0​(The population mean does not equal the specified value.)\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eCalculate the value of the test statistic (X) using the appropriate formula:\u003c/p\u003e\u003cp\u003eX = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{\\text{O}\\text{b}\\text{s}\\text{e}\\text{r}\\text{v}\\text{e}\\text{d}\\:\\text{V}\\text{a}\\text{l}\\text{u}\\text{e}-\\text{E}\\text{x}\\text{p}\\text{e}\\text{c}\\text{t}\\text{e}\\text{d}\\:\\text{v}\\text{a}\\text{l}\\text{u}\\text{e}}{\\text{S}\\text{t}\\text{a}\\text{n}\\text{d}\\text{a}\\text{r}\\text{d}\\:\\text{E}\\text{r}\\text{r}\\text{o}\\text{r}}\\)\u003c/span\u003e\u003c/span\u003e - (3)\u003c/p\u003e\u003cp\u003e\u003cb\u003e3.3 Outcome and covariates method\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe \u003cb\u003eoutcome and covariates method\u003c/b\u003e is a statistical approach often used in road traffic crash analysis to understand the relationship between crash outcomes (e.g., severity, frequency) and a set of explanatory variables or covariates (e.g., road conditions, driver behavior, vehicle type). This method involves identifying key factors that influence road crashes and estimating their effects using models like regression analysis (Sawyer et al., 2012).\u003c/p\u003e\u003cp\u003e\u003cb\u003eOutcome\u003c/b\u003e:\u003c/p\u003e\u003cp\u003eIn road traffic crash analysis, the outcome variable is the main variable of interest that we want to predict or explain. Common outcomes include:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eCrash Frequency\u003c/b\u003e: Number of crashes occurring over a certain period or location.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eCrash Severity\u003c/b\u003e: Categories such as fatal, severe injury, minor injury, or property damage only.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eTime-to-Crash\u003c/b\u003e: For studies involving near-miss situations (e.g., time-to-collision).\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eCovariates\u003c/b\u003e:\u003c/p\u003e\u003cp\u003eCovariates are the independent variables or predictors that may influence the outcome. They can be categorized as:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eRoad Characteristics\u003c/b\u003e: Road type (urban, rural), number of lanes, intersection type, road surface condition.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eTraffic Factors\u003c/b\u003e: Traffic volume, vehicle speed, congestion levels.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eDriver Behavior\u003c/b\u003e: Speeding, alcohol use, distracted driving, age and experience.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eEnvironmental Conditions\u003c/b\u003e: Weather (rain, fog), time of day (day/night), lighting conditions.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eVehicle Characteristics\u003c/b\u003e: Vehicle type (car, truck, motorcycle), vehicle age, safety features.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eLet\u0026rsquo;s consider a study that examines factors affecting the severity of road crashes at intersections:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eOutcome Variable\u003c/b\u003e: Crash severity (e.g., coded as 0\u0026thinsp;=\u0026thinsp;property damage only, 1\u0026thinsp;=\u0026thinsp;minor injury, 2\u0026thinsp;=\u0026thinsp;severe injury, 3\u0026thinsp;=\u0026thinsp;fatality).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eCovariates\u003c/b\u003e:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eRoad Factors\u003c/b\u003e: Intersection control (signalized/un-signalized), road geometry, speed limit.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eTraffic Factors\u003c/b\u003e: Traffic volume, average speed.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eDriver Factors\u003c/b\u003e: Age, gender, presence of alcohol.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eEnvironmental Factors\u003c/b\u003e: Weather conditions, time of day.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eUsing a multinomial logistic regression, the model can estimate the probability of each severity level given the covariates.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Quantile regression analysis\u003c/h2\u003e\u003cp\u003eQuantile regression is a useful statistical method for analyzing the relationship between hospitalization costs and various predictors when the distribution of costs is skewed or contains outliers. Unlike ordinary least squares (OLS) regression, which estimates the mean of the dependent variable, quantile regression estimates the relationship at different quantiles (e.g., the 25th, 50th, 75th percentiles) of the outcome variable, offering a more detailed understanding of the impact of covariates across the distribution (Meredith et al., 2003).\u003c/p\u003e\u003cp\u003eQuantile Regression Model for Hospitalization Costs-\u003c/p\u003e\u003cp\u003e\u003cb\u003eModel Setup\u003c/b\u003e:\u003c/p\u003e\u003cp\u003eLet:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eY\u003c/b\u003e represents hospitalization costs.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eX\u003c/b\u003e represents the set of covariates (e.g., age, gender, severity of illness, type of insurance, comorbidities).\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eThe quantile regression model can be written as:\u003c/p\u003e\u003cp\u003eQ\u003csub\u003eT\u003c/sub\u003e(Y/X) = β\u003csub\u003e0\u003c/sub\u003e​(τ) + β\u003csub\u003e1\u003c/sub\u003e​(τ) X\u003csub\u003e1\u003c/sub\u003e​+ β2​(τ) X\u003csub\u003e2\u003c/sub\u003e​+ \u0026hellip;\u0026hellip;.\u0026thinsp;+\u0026thinsp;β\u003csub\u003ek\u003c/sub\u003e​(τ) X\u003csub\u003ek​ \u0026minus;\u003c/sub\u003e (4)\u003c/p\u003e\u003cp\u003eWhere:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eQ\u003c/b\u003e\u003csub\u003e\u003cb\u003eT\u003c/b\u003e\u003c/sub\u003e\u003cb\u003e(Y/X)\u003c/b\u003e is the conditional quantile (e.g., 25th, 50th, 75th percentile) of hospitalization costs given covariates X.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eβ (τ) are the regression coefficients estimated at the τ\u003csup\u003eth\u003c/sup\u003e quantile.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eCalculation method and data per cost component\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eMedical costs\u003c/b\u003e\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eThe following medical expenses have been covered:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eTransportation of casualties to the hospital\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eTreatment in the hospital\u0026rsquo;s emergency department\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eIn-patient hospital care, including overnight stays\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eRehabilitation services\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eAdditionally, funeral expenses have been included as a medical cost item, though they could also fall under \u0026lsquo;other costs\u0026rsquo; [3]. It is not unusual to include funeral costs within medical expenses [18], and we have adopted this approach here. However, costs related to out-patient treatments (outside of emergency department care) and non-hospital treatments (such as services provided by a general practitioner) have been excluded due to a lack of available data in Kazakhstan. These costs are presumed to be relatively minor compared to hospitalization costs. Casualty transportation expenses are estimated based on the number of casualties transported to the hospital by ambulance and the average cost per ambulance trip. Similarly, costs for emergency treatment, in-patient hospital care, and rehabilitation are calculated using the number of casualties receiving each type of care and the associated average cost (unit cost) per treatment. The unit costs for in-patient hospital care and rehabilitation are determined by the average number of days of treatment and the cost per day.\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eProduction loss\u003c/b\u003e\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eProduction losses arise when road casualties are unable to work, either permanently (in cases of fatalities or severe injuries) or temporarily (in cases of non-fatal injuries). In line with international best practices, we have employed the concept of gross potential production loss, which, as previously mentioned, includes consumption loss. Gross production loss has been calculated using the number of fatalities and serious injuries, the average duration of work incapacity resulting from crashes, and wage rates (as a measure of the value of gross production per unit of time). For fatalities and those permanently disabled, the duration of work incapacity corresponds to the number of productive life years remaining. This period is estimated using data from the Agency of Statistics, which includes information on the age of individuals who have sustained serious injuries or fatalities, the age of labor market entry (broken down by gender and education level), and retirement age (also by gender).\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eHuman costs\u003c/b\u003e\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eTo estimate the value of a statistical life (VSL) in India, which underpins the calculation of human costs, a stated preference survey was conducted with a representative sample of the Patna population. This survey aimed to determine individuals' willingness to pay for reducing the risk of fatal crashes. Stated preference methods are commonly employed to derive VSL in road safety research, particularly in out of India, where they are often preferred over revealed preference methods. Unlike revealed preference methods, which assess risk reduction values based on actual behaviors, such as purchases of safety features like airbags, stated preference methods offer broader applicability by not relying on actual purchasing data. Additionally, because consumers may lack full awareness of the risk reductions associated with their purchases, stated preference methods enable researchers to provide this information, helping respondents better understand even minor risk reductions.\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eAdministrative costs\u003c/b\u003e\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003ePolice costs were calculated based on the number of crashes by severity, the proportion of crashes attended by police, the time spent per crash, and the average wage of police officers. The survey indicated that police attendance varied by crash severity: 55% for property damage only (PDO) crashes (N\u0026thinsp;=\u0026thinsp;185), 68% for slight injury crashes (N\u0026thinsp;=\u0026thinsp;208), and 92% for serious injury crashes (N\u0026thinsp;=\u0026thinsp;52). Table-1\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Results and Discussion","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Quantitative Analysis of the Existing Literature\u003c/h2\u003e\u003cp\u003e\u003cb\u003eData Base Construction-\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo build our database, we followed a two-step approach. First, we developed a methodology for selecting papers and designed a tagging system to extract key information from them.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePaper Selection Methodology\u003c/b\u003e: Papers for this literature review were identified through a two-step process. In the first step, we conducted Google Scholar searches and used tools like Mendeley, employing various keyword combinations: (i) road traffic crash costs, (ii) social and economic costs of road traffic crashes, and (iii) additional keywords targeting studies that assess the social and economic impacts of road traffic crashes. We also used reference lists from identified papers to enhance the screening process.\u003c/p\u003e\u003cp\u003eIn the second step, we applied an additional screening criterion to refine the list. This required each paper to meet an \u0026ldquo;academic standard\u0026rdquo; and to employ one of the two main approaches to calculate the value of a statistical life: either the human capital or the willingness-to-pay approach. Each selected paper needed to present social and economic cost results using one of these methods, as discussed in Section 2.2. Papers were included regardless of whether they were published in a formal academic journal or as working papers.\u003c/p\u003e\u003cp\u003eApplying this two-step methodology resulted in a final sample of 25 papers, from which we constructed a dataset of 85 data points estimating the costs of road traffic crashes (RTC) for the period from 2010 to 2023.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Descriptive Analysis\u003c/h2\u003e\u003cp\u003eThis subsection examines the time trend of studies, the methodologies used, and cross-country comparisons.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eTime Trend\u003c/strong\u003e\u003cp\u003eIn recent years, there has been an increase in studies evaluating the socio-economic costs of road traffic crashes. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, while only 10\u0026ndash;25 papers were published each year between 2010 and 2016, this number rose significantly to 85 studies published between 2017 and 2024, with nearly 25 studies published in 2023 alone. This upward trend highlights the growing academic focus on assessing the socio-economic impacts of road crash fatalities and injuries.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eIncome Level of Countries\u003c/strong\u003e\u003cp\u003eMost research has focused on high-income and upper-middle-income countries, which represent over 70% of the studies in the literature (see Fig.\u0026nbsp;4). Despite accounting for more than 90% of global road crash fatalities, lower-income countries are underrepresented, with only 10% of research papers focusing on estimating the socio-economic costs of road traffic injuries and fatalities in these regions.\u003c/p\u003e\u003cp\u003e\u003cb\u003eTable-2 List of Papers by Year of Publication.\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear of Publication\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNos. of Paper\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eSource: Author\u0026rsquo;s computations based on the constructed dataset.\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Econometric Analysis\u003c/h2\u003e\u003cp\u003eFor our formal econometric analysis, we refined our sample to focus on studies employing the two primary methods for calculating the value of a statistical life: the willingness-to-pay (WTP) approach and the human capital method. This adjustment reduced our sample from 110 to 95 data points. Alongside the data gathered from each study, we also collected country-specific income levels and additional information from the WHO\u0026rsquo;s Global Status Report on Road Safety 2015. This report provided metrics and indicators such as the death rate per 100,000 people, death distribution by victim and vehicle categories, availability of emergency training for medical personnel, presence of emergency room-based surveillance, and existence of a vital registration system. These variables will serve as controls to enhance the specification of our model.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e4.4 Critical Analysis\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;2 presents the total costs by cost component and severity level. The total cost of road crashes was estimated at 8.5\u0026nbsp;billion in 2023, equivalent to 3.3% of the GDP. Human costs accounted for a significant portion, estimated at 80.1% of the total costs. Property damage, primarily to vehicles, was the second-largest component, representing 12.3% of total costs, while production losses made up 6.5%. Administrative and medical costs were smaller components, accounting for 1.5% and 0.6% of the total, respectively.\u003c/p\u003e\u003cp\u003eIn terms of distribution by severity, more than half of the costs (3.9\u0026nbsp;billion) were related to injuries. The cost distribution across severity levels varied by component: fatalities represented a large portion (70%) of production loss costs, while serious injuries constituted the majority (75%) of medical costs.\u003c/p\u003e\u003cp\u003e\u003cb\u003eTable-3 Police time spent by severity of crash.\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHours/Crash\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNos. of Policeman attending a crash\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTotal Time Spent (hrs.)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFetal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMajor Injuries\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMinor Injuries\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eTable-4 Cost of Road crash in India 2023.\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabc\" border=\"1\"\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTable-4\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u003cp\u003eCost of road crashes in India in 2023 (3\u0026ndash;5% of GDP)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFatalities\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMajor Injuries\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMinor Injuries\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMedical Cost\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTransportation Cost\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHospital Cost in Patient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e21.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHospital Cost out Patient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFuneral Cost\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e30.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal Medical Cost\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e44.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e55.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eProduction Loss\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e310\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e433\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHuman Cost\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3258\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2508\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1550\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7316\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVehicle Damage\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e135\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e545\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e645\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1325\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAdministration Cost\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePolice\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOthers Emergency Service\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInsurance Cost\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e16.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e22.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal Administration Cost\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e28.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e40\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3707\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3169.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2267.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9144.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eAnalysis for Developing Countries\u003c/b\u003e\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eThe existing literature offers limited insights into the costs of road traffic crashes in low- and middle-income countries (LMICs). Findings from research on high-income countries (HICs) cannot be directly applied to LMICs, as highlighted by significant contextual differences noted in both the literature and in prior analyses. Wijnen and Stipdonk (2016) reviewed studies on the national costs of road crashes across 17 countries, ten of which were high-income and seven of which were low- and middle-income. Their analysis showed that the socio-economic costs of road crashes in HICs vary from 0.8\u0026ndash;4.0% of GDP, with an average of 2.5%. When excluding countries that do not use the willingness-to-pay (WTP) method for valuing human costs and those that do not account for underreporting, the average cost increases to 3.0% of GDP.\u003c/p\u003e\u003cp\u003eFor LMICs that account for underreporting, the estimated costs range from 1.0\u0026ndash;2.8% of GDP. However, none of the LMICs in the study had conducted a WTP study to estimate human costs. This gap underscores the need for more region-specific research to accurately assess the socio-economic impact of road traffic crashes in LMICs, as the absence of WTP-based studies limits comparability and may under represent the true costs.\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eGlobally, road safety is experiencing a shift from a traditional approach where road traffic crashes (RTCs) were mainly attributed to higher-risk groups like young and elderly drivers, impaired or distracted drivers, and non-users of seatbelts or helmets to a broader perspective that recognizes all vehicle travel as inherently risky. This new paradigm acknowledges that most drivers take small risks that can cumulatively lead to crashes (2023). The evidence from studies analyzed in this paper underscores the severe economic impact of RTCs, especially in low- and middle-income countries, which often lack sufficient data and research capacity to assess these costs effectively. This analysis not only highlights the existing gaps in road crash socio-economic cost analysis but also emphasizes the need for policymakers to develop integrated data systems. It calls on local road safety institutions to collect high-quality data to enable accurate cost calculations.\u003c/p\u003e\u003cp\u003eThis paper conducted a quantitative analysis of studies estimating the socio-economic costs of road traffic crashes, using both descriptive and formal econometric analyses. Our literature review included both working papers and articles from peer-reviewed journals. The primary goal of our research was to offer researchers and policymakers a comprehensive review of the existing literature on the socio-economic costs of road crashes, as well as an identification of the critical gaps in current research. With this objective, the paper focused on the two main approaches willingness-to-pay (WTP) and human capital and examined how results from these methods correlate with countries' socio-economic costs, measured as a percentage of GDP and in total economic value.\u003c/p\u003e\u003cp\u003eThe paper highlights the value of comparing or combining methodologies when estimating the socio-economic costs of road traffic crashes. Currently, countries use different approaches, with the willingness-to-pay (WTP) method predominantly applied in high-income countries, while the human capital approach is more common in low- and middle-income countries. However, research in lower-income countries remains sparse, representing only 5% of the studies analyzed. Given the financial and technical challenges associated with implementing the WTP method in these regions, the paper emphasizes the potential benefits of a hybrid approach that combines WTP, human capital, and other valuation methods to improve accuracy and adaptability in various contexts.\u003c/p\u003e\u003cp\u003ePoor pavement conditions are significantly correlated with increased crash frequency and severity and the impact of pavement quality is not uniform; it is more pronounced for fatal and severe crashes, as shown in higher quantiles. Quantile regression reveals that traditional OLS methods may underestimate the risk posed by poor pavement for the most serious outcomes. These findings highlight the need for targeted pavement maintenance and rehabilitation, especially in high-risk corridors, to reduce crash severity and save lives.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbdallah NM, Hakim AS, Refaeye MAE (2016) Analysis of accidents cost in Egypt using the willingness-to-pay method. Int J Traffic Transp Eng 5(1):10\u0026ndash;18. https://doi.org/10.5923/j. ijtte.20160501.02\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAbelson P (2008) Establishing a monetary value for lives saved: issues and controversies. office of Best Practices Regulation, Department of Finance and Deregulation, Canberra. Ainy, E., Soori, H., Ganjali, M., Le, H., Baghfalaki, T., 2014\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEstimating Cost of Road Traffic Injuries in Iran Using Willingness to Pay (WTP) Method. PLoS ONE 9 (12), e112721. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal. pone.0112721\u003c/span\u003e\u003cspan address=\"10.1371/journal. pone.0112721\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlfaro JL, Fabre F, chapuis M, Cour socio economique des accidents de la route. Comission des communautes europeennes, LUXEMBOURG., Ameratunga S, Hijar M, Norton R (1994) 2006. Road traffic injuries: confronting disparities to address a global-health problem. Lancet 367, 1533\u0026ndash;1540\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAnti\u0026acute;c B, Vujani\u0026acute;c M, Lipovac K, Pe\u0026acute;si\u0026acute;c D (2011) Estimation of the traffic accidents costs in Serbia by using dominant costs model. Transport 26(4):433\u0026ndash;440. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3846/16484142.2011.635425\u003c/span\u003e\u003cspan address=\"10.3846/16484142.2011.635425\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAthanasios T, Apostolos Z, Eleonora P, George Y (2017) Paper on Meta-analysis of the effect of road work zones on crash occurrence. Accid Anal Prevent 108(4):1\u0026ndash;8\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBahamonde-Birke FJ, Kunert U, Link H (2015) The Value of a Statistical Life in a Road Safety Context \u0026mdash; A Review of the Current Literature. Transp Rev 35(4):488\u0026ndash;511\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBTCE (1996) Valuing Transport Safety in Australia. Bureau of Transport and Communications Economics, Australia\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBTS Bo (1990) Africa Road Safety Review Final Report. Economic cost of Road Crashes in Africa (Chap. 6)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCase A, Menendez A (2011) Requiescat in Pace? The Consequences of High Priced Funerals in South Africa. Chapter 11 in Explorations of Aging. University of Chicago Press, Chicago\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDe Blaeij A, Florax RJGM, Rietveld P, Verhoef E (2003) November. The value of statistical life in road safety: A meta-analysis. Accid Anal Prev. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0001\u0026ndash;4575(02)00105\u0026ndash;7\u003c/span\u003e\u003cspan address=\"10.1016/S0001\u0026ndash;4575(02)00105\u0026ndash;7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDandona R, Kumar GA, Ameer MA, Reddy GB, Dandona L (2008) Under-reporting of road traffic injuries to the police: results from two data sources in urban India. Injury Prevention: J Int Soc Child Adolesc Injury Prev 14(6):360\u0026ndash;365. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1136/ip.2008.0 19638\u003c/span\u003e\u003cspan address=\"10.1136/ip.2008.0 19638\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDerriks H, Mak P, U.S.D. of T (2007) Underreporting of road traffic casualties. IRTAD Special report. OECD/ International Transport Forum, Paris. DOT,. 2016. Guidance on Treatment of the Economic Value of a Statistical Life (VSL) in u.s. Department of Transportation Analyses \u0026ndash; 2016 Adjustment, 13. Retrieved from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.transportation.gov/sites/dot.gov/files/docs/2016\u003c/span\u003e\u003cspan address=\"https://www.transportation.gov/sites/dot.gov/files/docs/2016\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e Revised Value of a Statistical Life Guidance.pdf\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eElvik R (1995) An analysis of official economic valuations of traffic accident fatalities in 20 motorized countries Accident Analysis and Prevention, 27 (1995), pp. 237\u0026ndash;247\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eElvik R (2000) How much do road accidents cost the national economy? Accid. Anal Prev 32(6):849\u0026ndash;851. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0001-4575(00)00015-4\u003c/span\u003e\u003cspan address=\"10.1016/S0001-4575(00)00015-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eElvik R (2001) Cost-Benefit Analysis of Police Enforcement. Analysis, (March 2001), 1\u0026ndash;78\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eElvik R (2016) Association between increase in fixed penalties and road safety outcomes: A meta-analysis. Accid Anal Prevent 92(1):202\u0026ndash;210\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eElvik R, Bj\u0026oslash;rnskau T (2017) Safety-in-numbers: A systematic review and meta-analysis of evidence. Saf Sci 92:274\u0026ndash;282. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ssci.2015.07.017\u003c/span\u003e\u003cspan address=\"10.1016/j.ssci.2015.07.017\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGupta et al (2023) found that inadequate surface friction was a statistically significant predictor of fatal crashes in urban corridors, aligning with findings from international literature\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKasnatscheew A, Felix H, Schoenebeck S, Markus L, Hosta P (2016) Review of European Accident Cost Calculation Methods- With Regard to Vulnerable Road Users. In-Depth understanding of accident causation for Vulnerable road users (InDeV), Germany\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLe H, Van Geldermalsen T, Lim WL, Murphy P (2011) Deriving Accident Costs using Willingness-to-Pay Approaches-A Case Study for Singapore, 34 edn. Australasian Transport Research Forum (ATRF), Adelaide, Citeseer\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLindberg G (1999) Calculating transport accident costs. Sweden: Final report of the expert advisors to the high level group on infrastructure charging (WorkGroup 3). Masniak, D. 2008\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSocial and Economic Costs of Road Accidents in Europe. Days of Law, 1\u0026ndash;11\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRetrieved from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.law.muni.cz/sborniky/dp08/files/pdf/financ/masniak.pdf\u003c/span\u003e\u003cspan address=\"http://www.law.muni.cz/sborniky/dp08/files/pdf/financ/masniak.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Lindhjem H, Navrud S, Braathen NA, Biausque V (2011)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eValuing mortality risk reductions from environmental, transport, and health policies: A global meta-analysis of stated preference studies. Risk Anal 31 (9), 1381\u0026ndash;1407. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1539-6924.2011.01694.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1539-6924.2011.01694.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMcMahon K, Dahdah S, The True Cost of Road Crashes: Valuing Life and the Cost of a Serious Injury. International Road Assessment Programme., Miller TR (2008) 2000. Variations between countries in values of statistical life. J. Transp. Econ. Pol. 34 (2), 169\u0026ndash;188\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMiller TR, Bhattacharya S, Zaloshnja E, Taylor D, Bahar G, David I (2011) Costs of Crashes to Government, United States, 2008. Ann Adv Autom Med Annu Sci Conf 55:347\u0026ndash;355\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMilligan C, Kopp A, Dahdah S, Montufar J (2014) Value of a statistical life in road safety: A benefit-transfer function with risk-analysis guidance based on developing country data. Accid Anal Prev 71:236\u0026ndash;247. https://doi.org/10.1016/j. aap.2014.05.026\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSocial cost of road crashes and injuries 2015 update. Mofadal, Ministry of Transport, New Zealand, Kanitpong AIA (2015) K., 2016. Analysis of road traffic accident costs in sudan using the human capital method. Transportation Engineering Program. Asian Institute of Technology (AIT), Klong Luang, Bathumthani, Thailand\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMohan D (2001) Social cost of Road Traffic Crashes in India. Tranportation Research and Injury Prevention Programme Indian Institute of Technology, New Delhi\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOdero W, Khayesi M, Heda PM (2003) Road traffic injuries in Kenya: Magnitude, causes and status of intervention. Injury Control Saf Promot 10(1\u0026ndash;2):53\u0026ndash;61. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1076/icsp.10.1.53.14103\u003c/span\u003e\u003cspan address=\"10.1076/icsp.10.1.53.14103\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOlukoga A, Harris G (2006) Field data: Distributions and costs of road-traffic fatalities in South Africa. Traffic Injury Prevent 7(4):400\u0026ndash;402. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/15389580600847560\u003c/span\u003e\u003cspan address=\"10.1080/15389580600847560\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePan American Health Organization (2010) (n.d.). Eastimation of the Impact of Road Traffic Injuries in Belize. Peden, M., Scurfield, R., Sleet, D., Mohan, D., Hyder, A.A., Jarawan, E., 2004. World report on road traffic injury prevention: summary. World Health Organization, Geneva\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePerformance of Latin America and the Caribbean during the first years of the Decade of Action for Road Safety (2015) Julie, Perovic, and Dimitris, Tsolakis. 2008\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSharma and Jain (2022) in \u003cem\u003eIJPRT\u003c/em\u003e analyzed crash patterns across Indian National Highways and concluded that pavement distress significantly contributes to rear-end and run-off-road crashes, especially on curves and transition zones\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eValuing the social cost (2007) of crashes: is community\u0026rsquo;s willingness to pay to avoid death or injury being reflected? Adelaide, South Australia: Australasian Road Safety Research, Policing and Education Conference. Pipat, Thongchim, Pichai, Taneerananon, Paramet, Luathep, Phayada, Prapongsena\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTraffic accident costing for Thailand, Risbey J, Cregan T, De Silva M, H., Social Cost of Road Crashes. Australasian Transport Research Forum, 2010 (December 2009), 1\u0026ndash;16. Retrieved from, Robinson J (2010) (1986). Philosophical origins of economic evaluation of life. the Milbank Quaterly\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Regression-analysis, Road crashes, Road safety, Socio-economic costs, Willingness-to-pay, Human capital","lastPublishedDoi":"10.21203/rs.3.rs-7296445/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7296445/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDespite recent advances in addressing road safety, especially in developed countries, road traffic crashes still result in 1.65\u0026nbsp;million fatalities annually and impose costs exceeding \u003cspan\u003e$\u003c/span\u003e95\u0026nbsp;billion. This paper reviews the literature on socio-economic costs, identifies key research gaps, and underscores the lack of analysis focused on developing countries, which experience 90% of global fatalities. Using both descriptive and econometric analyses, we observe an upward trend in road safety studies in high- and middle-income countries. We calculated the components of hospitalization costs and examined the relationship between these costs and patient characteristics using quantile regression models. The paper examines two primary methodologies for estimating socio-economic costs: willingness-to-pay (WTP) and human capital (HC). Our econometric findings show that studies using the WTP method typically estimate the impact on GDP to be approximately 1% higher than those using the HC approach. Furthermore, the HC method tends to underestimate total socio-economic costs by a factor of two compared to WTP-based estimates, although this gap narrows significantly when adjusting for factors like population density, income levels, and road safety conditions. Additionally, the paper highlights challenges with underreporting and the lack of a systematic method to account for it in cost estimations. We conclude with a call for more research in low- and middle-income countries that combines WTP, HC, and alternative valuation methods to provide a more comprehensive understanding of the socio-economic costs of road crashes.\u003c/p\u003e","manuscriptTitle":"Identifying the Social Costs of Road Traffic Crashes in India through Quantitative and Quantile Regression Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-06 08:44:26","doi":"10.21203/rs.3.rs-7296445/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"92a0677d-2108-4051-890f-89615e9afb21","owner":[],"postedDate":"August 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":52650902,"name":"Civil Engineering"}],"tags":[],"updatedAt":"2025-08-06T08:44:26+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-06 08:44:26","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7296445","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7296445","identity":"rs-7296445","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}