Next Article in Journal
Development of a “Smart Dry Port” Indicator and Ranking Calculation for Spanish Dry Ports
Previous Article in Journal
Identifying Barriers and Expectations in MaaS: Users’ and Stakeholders’ Perspective
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Systematic Review

A Systematic Review of the Application of Road Safety Valuation Methods in Assessing the Economic Impact of Road Traffic Injuries

Department of Civil Engineering, School of Engineering, College of Engineering and Physical Sciences, The University of Birmingham, Birmingham B15 2TT, UK
Author to whom correspondence should be addressed.
Future Transp. 2023, 3(4), 1253-1271;
Submission received: 7 August 2023 / Revised: 15 September 2023 / Accepted: 1 November 2023 / Published: 7 November 2023


Road traffic injuries (RTIs) are increasingly claiming lives, particularly of those living in low- and middle-income countries (LMICs). To evaluate the economic consequences of RTIs, their financial impact on Gross Domestic Product (GDP) has been investigated by several studies using road safety valuation methods. This in turn has been used to quantify the resources required for investment on appropriate countermeasures to reduce the severity and frequency of RTIs. To investigate the frequency of use of road safety valuation methods in assessing the economic impact of road injuries, a robust systematic review was carried out with the aid of EPPI-reviewer software. The analysis of the evidence gathered showed that 55% of the included studies used the willingness-to-pay (WTP) method, 29% used human capital (HC), 11% used restitution cost and 5% used other methods. In high-income countries (HICs), the predominant method used was WTP, while HC was more common for middle-income countries. In addition, it was found that 49% of the studies in this field were conducted on HICs, whilst 4% focused on low-income countries (LICs). This indicates that there is a gap in the use of road safety valuation methods for LICs in the literature and therefore a need for further research.

1. Introduction

Road traffic injuries (RTIs) continue to be a burden on society and cause negative effects on a country’s economy. RTIs can be fatal, serious or slight injuries and are defined as a traffic crash involving a moving vehicle and at least one casualty [1]. As reported by the most recent global status report on road safety, road traffic fatalities reached 1.35 million in the year of 2016 and have continued to rise in line with population growth [2]. It is noteworthy that for those aged 5–29 years, RTIs are the leading cause of death [2]. They particularly affect vulnerable road users, such as pedestrians, motorcyclists and cyclists, along with those in low- and middle-income countries (LMICs) as transport becomes increasingly motorised. The death rate in LMICs is nearly double that of high-income countries (HICs) [3]. Several studies [4,5,6,7,8,9] noted that RTIs, especially fatalities, cause a big financial burden on an economy. It is very unlikely that RTIs can be eliminated entirely, since people need to travel, which has a risk in itself; however, their occurrence and severity may be minimised [10] by investing more resources in road safety and choosing the safest means of travel [11]. HICs have successfully invested more resources into a safe transport system approach, which has contributed to a reduction in the number of mortalities [4]. The value of resources to invest could be determined by assessing the economic impact of road injuries on the country’s Gross Domestic Product (GDP).
Assessment of the economic burden of RTIs may be evaluated using road safety valuation methods, which estimate the different cost components in monetary terms [12,13]. These methods can only be effectively used when the required data are available, which could be a challenge in most LMICs [14,15]. As a result, it is important to investigate and understand the factors that affect the use of these valuation methods and their data requirements. Although COST 313 guidelines present a preferred valuation method [16], there is no clear standard to verify the most appropriate method for the economic analysis of RTIs [10]. When road safety valuation methods are integrated with traditional road investment appraisal models, they may alter the priority of projects based on cost effectiveness and risk reduction [17]. Therefore, there is a need to identify and examine the impact of these valuation methods and their frequency of use for specific country income groupings. This paper will aim to first examine the reason why certain methods are more commonly used in different economic groupings, and second, to identify gaps in the literature, particularly for low-income countries (LICs) where there appears to be very limited evidence (or data) about their use. To achieve this, a systematic literature review, which also seeks to establish the optimal valuation method for LICs, was carried out and is presented hereinafter.

1.1. Review Question

The initial step in conducting a systematic literature review is setting a well-defined review question. The main review question is as follows:
“What evidence supports the application of road safety valuation methods in assessing the economic impact of RTIs worldwide?”
This question sought to clarify the following questions:
  • What road safety valuation methods exist?
  • Have these methods been applied in LMICs and/or HICs?

1.2. Definition of Terms

RTIs are caused by road traffic crashes that occur on a communal road involving a casualty and any moving automobile, such as a car or motorcycle. RTIs involve fatalities, serious injuries or minor injuries. A casualty is any person that is injured or killed because of a road traffic crash. In this review, RTI severity levels were classified according to the Common Accident Data Set (CADaS) [1].
Income groupings were classified in four categories by the World Bank using the Atlas method. These categories include low-income, lower- and upper-middle-income and high-income countries [18].
Road safety valuation methods are used to assess the different cost components with their relative cost elements and items in monetary terms [12]. According to Anh et al. [19], Hills and Jones-Lee [20] and Jacobs [21], the different valuation methods include human capital (HC) or gross output, net output, life insurance, court-award, implicit public sector valuation and WTP or value of risk change. More recently, based on the COST 313 guidelines, the three main methods used for costing RTIs are restitution costs, HC and WTP [16].

2. Systematic Review Methods

A systematic review is a research method aiming to answer specific questions by gathering all accessible evidence that matches eligibility criteria which have been pre-specified in a review protocol. In this review, a well-structured protocol showing a step-by-step and clear procedure of the systematic review was developed in order to minimise bias, hence providing more reliable results. This process involves identifying all appropriate studies systematically in relation to eligibility criteria and carefully assessing their validity through different means, such as risk of bias assessment. The final cohort included studies which were subjected to a synthesis, where the findings were presented and compared systematically [22]. This review was not registered.
This systematic review was written using the guidelines provided by the Cochrane Handbook [22] and the Preferred Reporting Items for Systematic Reviews and Meta-analyses Protocols (PRISMA-P) 2020 checklist (refer to Supplementary Information, SI 1). The research question was formulated in consideration of the PICOS (Population, Intervention, Comparison, Outcome and Study design) [22].

2.1. PICOS Inclusion and Exclusion Criteria

This review considered studies which assessed the economic impact of road injuries using a valuation method, such as WTP, HC or restitution cost, amongst others. The included studies were categorised according to the different income groupings as per the World Bank [18]. All ages, sex and ethnicities of the population were considered.
Studies that were published outside the range of 2001–2022 were excluded, along with journals that were published in languages other than English. Duplicate studies, those with no full-text files available and studies that did not apply a road safety valuation method for assessing the economic impact of RTIs were excluded.

2.2. Search Strategy

Keywords from the research questions were identified based on the main objective of the systematic review. These keywords were used to prepare a search strategy, keeping in mind the inclusion criteria. Synonyms or alternative words/phrases to the keywords were later identified and used as search terms. Having diverse search terms helped minimise the risk of omitting important literature. Based on the research question and inclusion criteria, the alternative phrases for the corresponding keywords and search terms with Boolean operators [23] can be found in SI 2.

2.3. Databases

After identifying the keywords, bibliographic databases were searched systematically for relevant literature. The main bibliographic databases included Engineering village, Web of Science, Scopus, EBSCOhost, ProQuest, Taylor & Francis, PubMed, Ovid and Cochrane. The main search engines were Google Scholar, Google and FindIt@bham, which is a searchable library catalogue for the University of Birmingham. A detailed list of these databases can be found in SI 3. Studies from Google Scholar were manually searched to find literature that was relevant for this review. The application of Boolean operators, wildcards and proximity operators such as “OR” and “AND” helped broaden or narrow the search [23].

2.4. Other Sources

Additional literature was manually searched using the websites of international organisations such as the World Health Organisation (WHO), the World Bank, the European Transport Safety Council (ETSC), the National Highway Traffic Safety Administration (NHTSA) and the Institute for Road Safety Research, the Netherlands (SWOV), amongst others. A detailed list of these sources can be found in SI 3.

2.5. Study Selection Process

A total of 3275 studies obtained from different databases were exported to the EPPI-reviewer software [24]. EPPI-reviewer is an online tool which assists in conducting the systematic review process by eliminating duplicate references, managing study records and analysing selected studies [22]. From these, 820 were duplicates, hence 2455 studies were subjected to the screening process. A total of 1908 studies were excluded as they were irrelevant following scrutiny of their titles and abstracts. The remaining 547 studies were assessed for eligibility, with 445 studies excluded as the full-text files could not be retrieved or they were irrelevant to this study following scrutiny of their full-text files. A total of 102 studies were considered for final review. A flow diagram summarising the study selection process, which involved four processes, namely, identification, screening, eligibility and included processes, is shown in Figure 1 [25].

3. Results and Discussion

3.1. Valuation Methods

The three main road safety valuation methods used worldwide for assessing the economic burden of RTIs include restitution cost, human capital (HC) and willingness-to-pay (WTP) [12,16,26,27,28]. The main valuation methods used worldwide are elaborated below and shown in the flow chart in Figure 2.

3.1.1. The Restitution Cost Method

This method, also known as cost of restitution, involves direct costs incurred to restore the casualties, their family, relatives and friends to a position in which they would be had they not been involved in a road traffic crash causing an injury [12]. These direct costs can be valued using current market or representative prices. Examples of such costs include property damage (repairing damaged vehicles, road facilities or personal property), medical costs (treatment for RTI victims or visiting the victim at the hospital), administrative costs (police, fire department, insurance and legal costs) and lost output or loss of productivity [12,29,30]. An example of the direct costs used for the restitution cost method is shown below.
Direct   costs = medical   costs + property   damage + administration   costs + others   ( visiting   victims in   hospitals + friction   costs + environmental   costs + funeral   costs + vehicle   unavailability )

3.1.2. The HC Method

This method, also known as ex post or gross output method, aims to calculate statistically the economic value of a person to society and therefore the loss of that value in the case of an injury or fatality [12,15,19]. The HC method is preferred for measuring lost output and a country’s wealth maximisation [20]. Additionally, it is considered less complex compared to other methods, more consistent and easier to use due to its reliance on existing data [31]. However, it is not recommended for valuing human cost, which involves a value for the loss of quality of life, along with pain, grief and suffering [15,19,21,32].
The variables considered to compute lost output include GDP/GNP per capita, age of the victim, economic growth rate, discount rate, average retirement age, unemployment rate and total income per capita [12]. The lost output per fatality is computed as follows [19]:
Lost   output = t = 0 n 1 Y 1 + r t + 1 ( 1 + d ) t + 1
where Y represents the average GDP per capita per year;
  • d represents the discount rate;
  • r represents the growth rate of the economy;
  • t represents the average number of years of lost production per fatality [19].

3.1.3. The WTP Method

This method, also known as an ex ante, measures how much an individual is willing to pay to reduce the likelihood and impact of a road injury. Additionally, the willingness-to-accept (WTA) method estimates how much a person is willing to accept for an increase in the risk of an injury occurring or premature death [12,19,21]. The WTP method most commonly uses survey questionnaires to obtain information from individuals about their trade-off for safety [20]. These questionnaires may, however, be too complicated and are based on hypothetical risk instead of real choice situations, which are considered difficult for the majority of the population in LMICs to comprehend [20,21]. Nevertheless, it is the preferred method for measuring human cost and the social well-being of individuals, since its values incorporate an estimate for pain, grief and suffering on society, while also producing a higher value of statistical life (VSL) than the HC method [15,28,33].
There are two main approaches under the WTP method, revealed preference (RP) and stated preference (SP). The SP method involves the use of survey questionnaires designed to estimate VSL, whereas the RP method is based on the actual purchasing behaviour of individuals, such as buying cars with different safety features [12,26,34]. This makes the RP method more challenging to implement since consumers may not be aware of the risk of their purchasing behaviour [34,35]. The contingent valuation (CV) [17,36,37] and the stated choice (SC) [38,39] methods are considered to be approaches under SP. The SC method of WTP was recommended by several studies because it relies on more real market situations [27,40,41]. Some studies recognise hedonic pricing [11,35] as a technique under the WTP method. Hedonic pricing involves considering various marketable factors that affect the pricing of goods and services; for example, the value of a safety device includes both its internal characteristics and external factors [42]. The standard gamble method is different but related to WTP, since it also involves the use of contingent valuation surveys to collect relevant data [43,44]. The standard gamble method estimates the risk an individual is willing to accept to avoid early death [42].

3.1.4. Other Methods

Estimating the VSL and Value of Statistical Injury (VSI) has commonly been conducted using the WTP method or iRAP methodology. The WTP method is, however, costly and more complex compared to the iRAP methodology [33]. Thus, iRAP has been preferred for estimating VSL, since it is already established on readily available data collected from countries which used either WTP or HC methods for their injury cost computations [45]. However, it was noted by Bhalla et al. [46] that the iRAP methodology overestimates the value of non-fatal injuries. Under this methodology, the VSL equates to 70 times the GDP per capita for the corresponding country, while the VSI equates to 25% of the VSL [33]. The VSL is the monetary value an individual is willing to pay to reduce the likelihood of an injury or early death occurring in the event of executing a certain activity [39], as expressed below:
VSL = WTP   for   change   in   fatality   risk   change   in   the   fatality   risk
Connelly and Richard (2016) [7] suggest the use of court-awarded compensation as an alternative method to the SP and RP methods to measure the value of lost quality of life. This method relies on compensations awarded by the court to the surviving dependants of the victim to estimate the VSL [19,20].
The life insurance method considers the value that individuals place on their lives, which is also the cost of preventing the occurrence of a road crash [20]. It ignores the value of life, only valuing the earning capacity of an individual, whilst focusing on third party compensation. It is also common that the rich with no dependants may attach a higher value on their lives in order to survive [21].

3.2. Road Safety Valuation Methods Observed in Included Studies

A summary of the characteristics of the studies included in this review for in-depth analysis are shown in Table 1.
From the results, it was observed that the main road safety valuation methods applied were WTP, HC and restitution cost, as shown in Figure 3. These methods could be subdivided further, especially WTP, where 48 studies applied the stated preference/contingent valuation (SP/CV) approach, 1 study applied the revealed preference (RP) approach, 3 studies applied the stated choice (SC) approach and 11 studies did not specify what category of WTP method they had applied. In total, 63 studies applied WTP, 34 studies applied HC, 13 studies applied restitution cost and 6 studies applied other methods for road safety valuation. In reference to the other methods that were applied, two studies used the Personal Injury Recovery Cost (PIRC) approach, one study applied System dynamics and three studies did not specify what method was applied. Of the included studies, 11 applied more than one method in a single publication; for example, the application of WTP, HC and/or restitution cost. Although the WTP and HC methods are often applied as alternatives, they could be considered complimentary since they consider different cost components. For example, WTP for human cost and HC for lost output [127].
WTP was used most frequently for high-income countries, while for low- and middle-income countries, HC was more commonly used. This aligns with recommendations made by some researchers [15,21] indicating that HC is the most suitable method for low- and middle-income countries. Some studies did not explicitly state the use of the restitution cost method. However, according to our definition discussed earlier, direct costs are usually computed using this method. Therefore, we considered studies that included direct costs to have used the restitution cost method. For example, Mashreky et al. [50], Zuraik et al. [54], Urua et al. [72], Kruse [98] and Papadakaki et al. [101].
Several researchers argue that the WTP method was the preferred method because it factors human cost of pain, grief and suffering, while generally producing a higher VSL than the HC method [12,15,33].

3.3. Data Gathering

The accuracy of any model depends on the quality and quantity of the data used. It is worth noting that road safety valuation studies collected data in several ways, with survey questionnaires, interviews and existing data dominating. Survey questionnaires were the most frequently used data collection tool for WTP studies, with over 70% of data collected using them for our included studies. Some studies, however, obtained information from government databases, without carrying out their own surveys [4,123], while WTP/RP studies used existing data from consumer purchasing behaviour to value road safety [11].
Interviews were mostly carried out to obtain direct costs incurred by casualties and their care takers, without forgetting crash-related costs. Data from such interviews were mainly used to compute restitution costs, along with questionnaires. It should be noted that only 13 studies considered restitution cost as a method, compared to 63 for WTP, so there is more uncertainty in the data. Data from interviews were also used to compute lost output due to road crashes. Nevertheless, existing data were predominantly collected to compute lost output, such as employment details, age, life expectancy and country’s discount rate for use in the HC method. The different data sources used by the included studies for road safety valuations are shown in Figure 4.

3.4. Application of Road Safety Valuation Methods Worldwide

Initially, countries were categorised into four income groupings, LICs, lower-middle-income countries, upper-middle-income countries and HICs, according to the World Bank [18]. Numerous studies categorise economies into developing and developed, which in many cases may not clearly represent LICs. To understand the situation in all countries, it is important to categorise them into their appropriate income groupings, as it is very likely that there may be significant differences between varying economies such as LICs and upper-middle-income countries. Consequently, factors affecting growth (for example risk aversion) in LICs could differ from those affecting growth in upper-middle-income countries and HICs, as shown in a study carried out in Chile [40].
From the results of this study, it may be pointed out that although road safety valuations have been applied worldwide, more have been conducted in HICs than any other economy. Nearly half (49%) of the included studies are from HICs, which accounted for 50 studies, with 19 studies (19%) from upper-middle-income countries, 29 studies from lower-middle-income countries (28%) and 4 studies (4%) from LICs, as shown in Figure 5. It is noteworthy that some countries have moved into different income groupings from when the studies were originally carried out. Subsequently, the countries’ current income groupings were considered at the time of this review.
Figure 6 shows the number of countries by income groups versus the number of countries represented by the studies included in this review. It seems that LICs are under-represented, which may be due to an insufficient availability of data required for comprehensive analysis [14,32,45]. Several LMICs may rely on the rule-of-thumb method for economic evaluation of road crashes [27,33,45]. In many HICs, governments have published road crash data which have been used in projects such as SafetyCube to assess the impacts on stakeholders [28].
Overall, HICs were the most represented, with 27% of these countries (22 countries) having at least one study, followed by 24% of upper-middle-income countries (13 countries), then 20% of lower-middle-income countries (11 countries) and finally 11% (3 countries) of LICs. This indicates that there is a gap in LICs as limited research has been conducted in this subject area.
The application of the different road safety valuation methods for road injury costing by income group is represented by Figure 7. From this, it may be observed that the WTP method was most widely used for HICs and least used in LICs. This method was also used in middle-income countries, although the results show that the HC method was more frequently used in lower- than upper-middle-income countries.
In addition to the WTP method, the HC method was also used for LICs, though insufficient literature was available. Overall, more studies were carried out in HICs than all the other income groupings. There were additional methods other than the WTP, HC and restitution cost that were used by some studies, as shown in Figure 7. It is very difficult to draw conclusions based on these findings since the studies are few and hence less representative of their respective country income groupings.
It was very difficult to conclude what method was most used in LICs, as only four studies were included in this review. Of the four studies, one study used the HC method, two studies used the WTP method and one did not specify what method was used, since it did not compute the total injury cost due to scarcity of relevant data [14]. However, a number of researchers [15,19,21,33] suggested that the HC method is more applicable for low- and middle-income countries, since it follows a less lengthy and less complex procedure as compared to the WTP method. Additionally, the iRAP methodology was recommended to be used to cover the unavailability of relevant data, as it is established on existing data from countries which used either WTP or HC methods for road safety valuation. Therefore, it is likely that most LICs use this method to estimate VSL, since several researchers [14,32,45] mention the challenge of unavailability of required data for road safety valuation. The rate of underreporting and misclassification of injuries, especially by the police, in LMICs is very high, affecting the road safety valuation studies [10,32,56]. It was noted that most of the countries did not update their injury cost annually, yet Chin et al. [106] mention that it is important to update the injury cost annually, since the cost figure changes with the number of injuries, which vary with time.

4. Recommendation

The lack of publications for LICs in this area, combined with the alarming road safety records, highlight the research gaps, while leaving scope for further investigation. Valuation of the economic impact of RTIs in LICs would support the justification of road safety investments, which in turn could promote economic growth and help to save lives. More broadly, research is needed to cover this gap and allow for more reliable road safety valuations. This may focus on the appropriateness of the methods in relation to their data requirements, availability and management.

5. Conclusions

The aim of this review was to investigate the existing evidence supporting the use of road safety valuation methods for assessing the economic impact of road injuries in LMICs and/or HICs by means of a systematic review. This is the first systematic review that has been carried out to clarify the current state of knowledge with regard to the desirability of road safety valuation methods and their data collection, especially for low-income countries. The following conclusions may be made based on the results of this review:
The most frequently used road safety valuation method was WTP (55%), followed by HC (29%) and thirdly, restitution cost (11%).
The primary data source used for WTP studies was questionnaires, while for HC, existing data were predominantly employed. Studies that applied the restitution cost method used a mix of questionnaires, interviews and existing data.
In HICs, the most frequently used method for assessing the economic impact of RTIs was WTP, while HC was slightly more common in lower-middle-income countries. It was difficult to establish what road safety valuation method was predominantly used in LICs due to insufficient data retrieved for this review.
In terms of method usage as characterised by income groupings, HICs had the highest representation in the literature at 49%, followed by lower-middle-income countries (28%), upper-middle-income countries (19%) and LICs (4%).

Supplementary Materials

The following supporting information can be downloaded at:, SI 1: PRISMA 2020 Checklist; SI 2: Alternative phrases to the keywords used in this review; SI 3: Databases searched in this review.

Author Contributions

Methodology, C.N.; supervision, H.E. All authors have read and agreed to the published version of the manuscript.


This research was supported by the Commonwealth Scholarships Commission (CSC).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.


I would like to acknowledge the Commonwealth Scholarships Commission (CSC), UK, for sponsoring my PhD at the University of Birmingham.

Conflicts of Interest

The authors declare no conflict of interest.


  1. European Commission. CaDaS—Common Accident Data Set, Reference Guide; Version 3.8; Directorate-General for Mobility and Transport: Brussels, Belgium, 2021. [Google Scholar]
  2. World Health Organization. Global Status Report on Road Safety. Available online: (accessed on 16 December 2021).
  3. Chandran, A.; Hyder, A.A.; Peek-Asa, C. The global burden of unintentional injuries and an agenda for progress. Epidemiol. Rev. 2010, 32, 110–120. [Google Scholar] [CrossRef] [PubMed]
  4. Campbell, M.; Stone, D.H.; Kleinberg, K.F.; McLean, R. Down but not out: Incidence and estimated costs to society of road casualties in Strathclyde, Scotland. Public Health 2014, 128, 350–353. [Google Scholar] [CrossRef] [PubMed]
  5. Niroomand, N.; Jenkins, G.P. A comparison of stated preference methods for the valuation of improvement in road safety. Econ. Anal. Policy 2018, 59, 138–149. [Google Scholar] [CrossRef]
  6. De Brabander, B.; Vereeck, L. Valuing the Prevention of Road Accidents in Belgium. Transp. Rev. 2007, 27, 715–732. [Google Scholar] [CrossRef]
  7. Connelly, L.B.; Supangan, R. The economic costs of road traffic crashes: Australia, states and territories. Accid. Anal. Prev. 2006, 38, 1087–1093. [Google Scholar] [CrossRef]
  8. Chantith, C.; Permpoonwiwat Chompoonuh, K.; Hamaide, B. Measure of productivity loss due to road traffic accidents in Thailand. IATSS Res. 2020, 45, 131–136. [Google Scholar] [CrossRef]
  9. Drosu, A.; Cofaru, C. Estimating the Costs Caused by Road Traffic Accidents in Romania; Springer: Cham, Switzerland, 2017; pp. 889–898. [Google Scholar]
  10. Donário, A.A.; Santos, R.B.D.; Elvik, R. The Economic and Social Cost of Road Accidents: The Portuguese Case; EDIUAL-Universidade Autónoma de Lisboa: Lisbon, Portugal, 2012. [Google Scholar]
  11. Andersson, H. The value of safety as revealed in the Swedish car market: An application of the hedonic pricing approach. J. Risk Uncertain. 2005, 30, 211–239. [Google Scholar] [CrossRef]
  12. Kasnatscheew, A.; Heinl, F.; Schoenebeck, S.; Lerner, M.; Hosta, P. Review of European Accident Cost Calculation Methods—With Regard to Vulnerable Road Users; SWOV: The Hague, The Netherlands, 2016. [Google Scholar]
  13. Wismans, J.; Thynell, M.; Lindberg, G. Economics of Road Safety—What Does It Imply under the 2030 Agenda for Sustainable Development. United Nations Centre for Regional Development. 2017. Available online: (accessed on 1 August 2023).
  14. Mowafi, H.; Rice, B.; Nambaziira, R.; Nirere, G.; Wongoda, R.; James, M.; Group, G.W.; Bisanzo, M.; Post, L. Household economic impact of road traffic injury versus routine emergencies in a low-income country. Injury 2021, 52, 2657–2664. [Google Scholar] [CrossRef]
  15. Silcock, R.; TRL. Guidelines for Estimating the Cost of Road Crashes in Developing Countries; Project R7780; Department for International Development: London, UK, 2003. [Google Scholar]
  16. Alfaro, J.L.; Chapuis, M.; Fabre, F. COST 313: Socioeconomic Cost of Road Accidents; Commission of the European Communities: Brussels, Belgium; Luxembourg, 1994. [Google Scholar]
  17. Dionne, G.; Lanoie, P. Public choice about the value of a statistical life for cost-benefit analyses—The case of road safety. J. Transp. Econ. Policy 2004, 38, 247–274. [Google Scholar]
  18. World Bank. Country Classification: World Bank Country and Lending Groups. Available online: (accessed on 2 April 2023).
  19. Anh, T.T.; Dao, N.X.; Anh, T.T. The cost of road traffic accident in Vietnam. In Proceedings of the Eastern Asia Society for Transportation Studies, Tokyo, Japan, 28 November 1994; Volume 5, pp. 1923–1933. [Google Scholar]
  20. Hills, P.; Jones-Lee, M.W. The role of safety in highway investment appraisal for developing countries. Accid. Anal. Prev. 1983, 15, 355–369. [Google Scholar] [CrossRef]
  21. Jacobs, G.D. Costing Road Accidents in Developing Countries; Overseas Road Note 10; Transport Research Laboratory: Crowthorne, Berkshire, UK, 1995. [Google Scholar]
  22. Higgins, J.P.; Thomas, J.; Chandler, J.; Cumpston, M.; Li, T.; Page, M.J.; Welch, V.A. Cochrane Handbook for Systematic Reviews of Interventions; John Wiley & Sons: Hoboken, NJ, USA, 2019. [Google Scholar]
  23. Lefebvre, C.; Sambunjak, D.; Cumpston, M.; Watts, C. Module 3: Searching for Studies. Cochrane Interactive Learning: Conducting an Intervention Review. Available online: (accessed on 24 June 2021).
  24. Thomas, J.; Brunton, J.; Graziosi, S. EPPI-Reviewer: Software for Research Synthesis, version 4.0. EPPI-Centre Software. Social Science Research Unit, UCL Institute of Education, University of London: London, UK, 2010.
  25. Sambunjak, D.; Cumpston, M.; Watts, C. Module 4: Selecting Studies and Collecting Data. Cochrane Interactive Learning: Conducting an Intervention Review. Available online: (accessed on 24 June 2017).
  26. Wijnen, W. Socio-economic costs of road crashes in middle-income countries: Applying a hybrid approach to Kazakhstan. IATSS Res. 2021, 45, 293–302. [Google Scholar] [CrossRef]
  27. World Bank. Socio-Economic Costs and Human Impacts of Road Accidents in Azerbaijan 2021. Available online: (accessed on 2 September 2021).
  28. Schoeters, A.; Wijnen, W.; Carnis, L.; Weijermars, W.; Elvik, R.; Daniels, S.; Johannsen, H. Costs related to serious road injuries: A European perspective. Eur. Transp. Res. Rev. 2020, 12, 58. [Google Scholar] [CrossRef]
  29. Wijnen, W.; Weijermars, W.; Schoeters, A.; van den Berghe, W.; Bauer, R.; Carnis, L.; Elvik, R.; Martensen, H. An analysis of official road crash cost estimates in European countries. Saf. Sci. 2019, 113, 318–327. [Google Scholar] [CrossRef]
  30. Elvik, M.R.; Ortenwall, M.P.; Mackay, M.M.; Pelckmans, M.J.; Monclus, M.J.; Tecl, M.J.; Buylaert, M.W.; Sølund, M.J.; Holló, M.P.; Wodzin, M.E. Social and Economic Consequences of Road Traffic Injury in Europe; European Transport Safety Council: Brussels, Belgium, 2007. [Google Scholar]
  31. BTCE. Valuing Transport Safety in Australia; Working Paper 26; BITRE: Canberra, Australia, 1996.
  32. Banstola, A.; Kigozi, J.; Barton, P.; Mytton, J. Economic Burden of Road Traffic Injuries in Nepal. Int. J. Environ. Res. Public Health 2020, 17, 4571. [Google Scholar] [CrossRef] [PubMed]
  33. McMahon, K.; Dahdah, S. The True Cost of Road Crashes: Valuing Life and the Cost of a Serious Injury; iRAP: New York, NY, USA, 2008. [Google Scholar]
  34. De Blaeij, A.; Florax, R.J.G.M.; Rietveld, P.; Verhoef, E. The value of statistical life in road safety: A meta-analysis. Accid. Anal. Prev. 2003, 35, 973–986. [Google Scholar] [CrossRef] [PubMed]
  35. Bhattacharya, S.; Alberini, A.; Cropper, M.L. The value of mortality risk reductions in Delhi, India. J. Risk Uncertain. 2007, 34, 21–47. [Google Scholar] [CrossRef]
  36. Ainy, E.; Soori, H.; Ganjali, M.; Basirat, B.; Haddadi, M. Cost estimation of road traffic injuries among iranian motorcyclists using the willingness to pay method. Arch. Trauma Res. 2016, 5, e23198. [Google Scholar] [CrossRef]
  37. Haddak, M.M.; Lefèvre, M.; Havet, N. Willingness-to-pay for road safety improvement. Transp. Res. Part A Policy Pract. 2016, 87, 1–10. [Google Scholar] [CrossRef]
  38. González, R.M.; Román, C.; Amador, F.J.; Rizzi, L.I.; de Dios Ortúzar, J.; Espino, R.; Martín, J.C.; Cherchi, E. Estimating the value of risk reductions for car drivers when pedestrians are involved: A case study in Spain. Transportation 2018, 45, 499–521. [Google Scholar] [CrossRef]
  39. Hensher, D.A.; Rose, J.M.; de Dios Ortúzar, J.; Rizzi, L.I. Estimating the willingness to pay and value of risk reduction for car occupants in the road environment. Transp. Res. Part A Policy Pract. 2009, 43, 692–707. [Google Scholar] [CrossRef]
  40. Rizzi, L.I.; de Dios Ortúzar, J. Estimating the willingness-to-pay for road safety improvements. Transp. Rev. 2006, 26, 471–485. [Google Scholar] [CrossRef]
  41. Svensson, M. The value of a statistical life in Sweden: Estimates from two studies using the ‘‘Certainty Approach’’ calibration. Accid. Anal. Prev. 2009, 41, 430–437. [Google Scholar] [CrossRef] [PubMed]
  42. But, B. Improving Our Understanding of the Cost of Injuries on the Road; TRL Academy: Berks, UK, 2021. [Google Scholar]
  43. Koyama, S.; Takeuchi, K. Economic valuation of road injuries in Japan by standard gamble. Environ. Econ. Policy Stud. 2004, 6, 119–146. [Google Scholar] [CrossRef]
  44. Sánchez-Martínez, F.-I.; Martínez-Pérez, J.-E.; Abellán-Perpiñán, J.-M.; Pinto-Prades, J.-L. The value of statistical life in the context of road safety: New evidence on the contingent valuation/standard gamble chained approach. J. Risk Uncertain. 2021, 63, 203–228. [Google Scholar] [CrossRef]
  45. Milligan, C.; Kopp, A.; Dahdah, S.; Montufar, J. Value of a statistical life in road safety: A benefit-transfer function with risk-analysis guidance based on developing country data. Accid. Anal. Prev. 2014, 71, 236–247. [Google Scholar] [CrossRef]
  46. Bhalla, K.; Diez-Roux, E.; Taddia, A.P.; De la Peña Mendoza, S.M.; Pereyra, A. The Costs of Road Injuries in Latin America 2013; Inter-American Development Bank: Washington, DC, USA, 2013. [Google Scholar]
  47. Mekonnen, A.A.; Beza, A.D.; Sipos, T. Estimating the Value of Statistical Life in a Road Safety Context Based on the Contingent Valuation Method. J. Adv. Transp. 2022, 2022, 3047794. [Google Scholar] [CrossRef]
  48. Mofadal, A.I.A.; Kanitpong, K.; Jiwattanakulpaisarn, P. Analysis of pedestrian accident costs in sudan using the willingness-to-pay method. Accid. Anal. Prev. 2015, 78, 201–211. [Google Scholar] [CrossRef]
  49. Mofadal, A.I.A.; Kanitpong, K. Analysis of road traffic accident costs in Sudan using the human capital method. Open J. Civ. Eng. 2016, 6, 203–216. [Google Scholar] [CrossRef]
  50. Mashreky, S.R.; Rahman, A.; Khan, T.F.; Faruque, M.; Svanstrom, L.; Rahman, F. Hospital burden of road traffic injury: Major concern in primary and secondary level hospitals in Bangladesh. Public Health 2010, 124, 185–189. [Google Scholar] [CrossRef]
  51. Mahmud, M. On the contingent valuation of mortality risk reduction in developing countries. Appl. Econ. 2009, 41, 171–181. [Google Scholar] [CrossRef]
  52. Baidoo, J.; Ketu, J. Cost-benefit analysis of road accidents in the forestry sector. Int. J. Crashworthiness 2022, 27, 1423–1427. [Google Scholar] [CrossRef]
  53. Kudebong, M.; Wurapa, F.; Nonvignon, J.; Norman, I.; Awoonor-Williams, J.K.; Aikins, M. Economic burden of motorcycle accidents in Northern Ghana. Ghana Med. J. 2011, 45, 135–142. [Google Scholar] [PubMed]
  54. Zuraik, C.; Sampalis, J.; Brierre, A. The Economic and Social Burden of Traumatic Injuries: Evidence from a Trauma Hospital in Port-au-Prince, Haiti. World J. Surg. 2018, 42, 1639–1646. [Google Scholar] [CrossRef] [PubMed]
  55. Balakrishnan, S.; Karuppanagounder, K. Estimating the cost of two-wheeler road accident injuries in India using the willingness to pay method. Aust. J. Civ. Eng. 2020, 18, 65–72. [Google Scholar] [CrossRef]
  56. Bora, B.; Landage, V.; Dalai, B. Socio-economic costing of road traffic accidents: Evidence from Nagpur city, Maharashtra, India. Curr. Sci. 2018, 11, 1275–1283. [Google Scholar] [CrossRef]
  57. Reddy, G.M.M.; Negandhi, H.; Singh, D.; Singh, A.J. Extent and determinants of cost of road traffic injuries in an Indian city. Indian J. Med. Sci. 2009, 63, 549–556. [Google Scholar] [CrossRef]
  58. Sugiyanto, G. The cost of traffic accident and equivalent accident number in developing countries (case study in Indonesia). ARPN J. Eng. Appl. Sci. 2017, 12, 389–397. [Google Scholar]
  59. Sugiyanto, G.; Santi, M.Y. Road Traffic Accident Cost using Human Capital Method (Case study in Purbalingga, Central Java, Indonesia). J. Teknol. 2017, 79, 107–116. [Google Scholar] [CrossRef]
  60. Widyastuti, H.; Mulley, C. Evaluation of casualty cost of motorcyclist’s slight injury in Indonesia. J. East. Asia Soc. Transp. Stud. 2005, 6, 3497–3507. [Google Scholar]
  61. Ahadi, M.R.; Razi-Ardakani, H. Estimating the cost of road traffic accidents in Iran using human capital method. Int. J. Transp. Eng. 2015, 2, 163–178. [Google Scholar]
  62. Ainy, E.; Soori, H.; Ganjali, M.; Le, H.; Baghfalaki, T. Estimating cost of road traffic injuries in iran using willingness to pay (WTP) method. PLoS ONE 2014, 9, e112721. [Google Scholar] [CrossRef] [PubMed]
  63. Ainy, E.; Soori, H.; Ganjali, M.; Baghfalaki, T. Eliciting road traffic injuries cost among Iranian drivers’ public vehicles using willingness to pay method. Int. J. Crit. Illn. Inj. Sci. 2015, 5, 108–113. [Google Scholar] [CrossRef] [PubMed]
  64. Ainy, E.; Soori, H.; Ganjali, M.; Bahadorimonfared, A. Using bayesian model to estimate the cost of traffic injuries in Iran in 2013. Int. J. Crit. Illn. Inj. Sci. 2017, 7, 166–171. [Google Scholar] [CrossRef] [PubMed]
  65. Hejazi, R.; Shamsudin, M.N.; Radam, A.; Rahim, K.A.; Ibrahim, Z.Z.; Yazdani, S. Estimation of traffic accident costs: A prompted model. Int. J. Inj. Control Saf. Promot. 2013, 20, 152–157. [Google Scholar] [CrossRef]
  66. Jarahi, L.; Karbakhsh, M.; Rashidian, A. Parental willingness to pay for child safety seats in Mashad, Iran. BMC Public Health 2011, 11, 281. [Google Scholar] [CrossRef]
  67. Kavosi, Z.; Jafari, A.; Hatam, N.; Enaami, M. The economic burden of traumatic brain injury due to fatal traffic accidents in shahid rajaei trauma hospital, shiraz, iran. Arch. Trauma Res. 2015, 4, e22594. [Google Scholar] [CrossRef]
  68. Rezaei, S.; Arab, M.; Karami Matin, B.; Akbari Sari, A. Extent, consequences and economic burden of road traffic crashes in Iran. J. Inj. Violence Res. 2014, 6, 57–63. [Google Scholar] [CrossRef]
  69. Mon, E.E.; Jomnonkwao, S.; Khampirat, B.; Satiennam, W.; Ratanavaraha, V. Willingness to pay for mortality risk reduction for traffic accidents in Myanmar. Accid. Anal. Prev. 2018, 118, 18–28. [Google Scholar] [CrossRef]
  70. Mon, E.E.; Jomnonkwao, S.; Khampirat, B.; Satiennam, T.; Ratanavaraha, V. Estimating the willingness to pay and the value of fatality risk reduction for car drivers in Myanmar. Case Stud. Transp. Policy 2019, 7, 301–309. [Google Scholar] [CrossRef]
  71. Sapkota, D.; Bista, B.; Adhikari, S.R. Economic Costs Associated with Motorbike Accidents in Kathmandu, Nepal. Front. Public Health 2016, 4, 273. [Google Scholar] [CrossRef]
  72. Urua, U.; Osungbade, K.; Obembe, T.; Adeniji, F. A cost analysis of road traffic injuries in a tertiary hospital in south-west Nigeria. Int. J. Inj. Control Saf. Promot. 2017, 24, 510–518. [Google Scholar] [CrossRef]
  73. De Leon, M.R.M.; Cal, P.C.; Sigua, R.G. Estimation of socio-economic cost of road accidents in Metro Manila. J. East. Asia Soc. Transp. Stud. 2005, 6, 3183–3198. [Google Scholar]
  74. Pham, K.H.; Le Thi, Q.X.; Petrie, D.J.; Adams, J.; Doran, C.M. Households’ Willingness to Pay for a Motorcycle Helmet in Hanoi, Vietnam. Appl. Health Econ. Health Policy 2008, 6, 137–144. [Google Scholar] [CrossRef]
  75. Pérez-Núñez, R.; Hijar-Medina, M.; Heredia-Pi, I.; Jones, S.; Silveira-Rodrigues, E.M. Economic impact of fatal and nonfatal road traffic injuries in Belize in 2007. Rev. Panam. Salud Publica 2010, 28, 326–336. [Google Scholar] [CrossRef] [PubMed]
  76. Liu, W.-G.; Zhao, S.-C. The value of a statistical life in road safety: Findings from a contingent valuation study in Dalian, China. In Proceedings of the 1st International Conference on Transportation Information and Safety: Multimodal Approach to Sustained Transportation System Development—Information, Technology, Implementation, ICTIS, Wuhan, China, 30 June 2011; pp. 1061–1067. [Google Scholar]
  77. Zheng, Q.; Liu, P.; Yang, Z.; Xu, C.; Ke, Z. Estimation of Value of Statistical Life Using Willingness-to-Pay Method: A Focus on Hangzhou, China. In Proceedings of the CICTP 2019: Transportation in China-Connecting the World, Nanjing, China, 6–8 July 2019; pp. 3810–3822. [Google Scholar]
  78. Tan, H.; Zhao, F.; Hao, H.; Liu, Z. Cost analysis of road traffic crashes in China. Int. J. Inj. Control Saf. Promot. 2020, 27, 385–391. [Google Scholar] [CrossRef] [PubMed]
  79. Ghadi, M.; Török, Á.; Tánczos, K. Study of the Economic Cost of Road Accidents in Jordan. Period. Polytech. Transp. Eng. 2018, 46, 129–134. [Google Scholar] [CrossRef]
  80. Jadaan, K.; Braizat, E.; Al Matawah, J.; Abdallah, A.; Sharan, I. Modeling cost of road crashes based on human capital approach. In Proceedings of the APCIM and 7th ICTTE, Part F148260, Beijing, China, 21–23 December 2018; pp. 99–105. [Google Scholar]
  81. Mohd Fauzi, M.Y.; Nor Ghani, M.N.; Radin Umar, R.S.; Hariza, H.A. The Value of Life and Accident Costing: A Willingness-to-Pay Study Amongst Motorcyclists in Malaysia. Appl. Health Econ. Health Policy 2004, 3, 5–8. [Google Scholar] [CrossRef]
  82. Nor, N.G.M.; Yusoff, M.F.M. Value of life of Malaysian motorists: Estimates from a nationwide survey. J. East. Asia Soc. Transp. Stud. 2003, 4, 275–284. [Google Scholar]
  83. Yusof, M.F.M.; Nor, N.G.M.; Mohamad, N.A. Malaysian value of statistical life for fatal injury in road accident: A conjoint analysis approach. J. Soc. Transp. Traffic Stud. 2013, 2, 30–40. [Google Scholar]
  84. Yusoff, M.F.M.; Mohamad, N.A.; Nor, N.G.M. Malaysian Value of Fatal and Non Fatal Injury due to Road Accident: The Willingness to Pay Using Conjoint Analysis Study. In Proceedings of the Eastern Asia Society for Transportation Studies Vol. 8 (The 9th International Conference of Eastern Asia Society for Transportation Studies), Jeju, Republic of Korea, 20–23 June 2011; p. 33. [Google Scholar]
  85. Pérez-Núñez, R.; Ávila-Burgos, L.; Híjar-Medina, M.; Pelcastre-Villafuerte, B.; Celis, A.; Salinas-Rodríguez, A. Economic impact of fatal and non-fatal road traffic injuries in Guadalajara Metropolitan Area and Jalisco, Mexico. Inj. Prev. 2011, 17, 297–303. [Google Scholar] [CrossRef]
  86. Antić, B.; Vujanić, M.; Lipovac, K.; Pešić, D. Estimation of the traffic accidents costs in Serbia by using dominant costs model. Transport 2011, 26, 433–440. [Google Scholar] [CrossRef]
  87. Olukoga, A.; Harris, G. Field data: Distributions and costs of road-traffic fatalities in South Africa. Traffic Inj. Prev. 2006, 7, 400–402. [Google Scholar] [CrossRef] [PubMed]
  88. Chaturabong, P.; Kanitpong, K.; Jiwattanakulpaisarn, P. Analysis of Costs of Motorcycle Accidents in Thailand by Willingness-to-Pay Method. Transp. Res. Rec. 2011, 2239, 56–63. [Google Scholar] [CrossRef]
  89. Puttawong, C.; Chaturabong, P. Willingness-To-Pay for Estimation the Risk Pedestrian Group Accident Cost. Civ. Eng. J. Tehran 2020, 6, 1064–1073. [Google Scholar] [CrossRef]
  90. Bambach, M.R.; Mitchell, R.J. Estimating the human recovery costs of seriously injured road crash casualties. Accid. Anal. Prev. 2015, 85, 177–185. [Google Scholar] [CrossRef]
  91. Hensher, D.A.; Rose, J.M.; de Dios Ortúzar, J.; Rizzi, L.I. Estimating the Value of Risk Reduction for Pedestrians in the Road Environment: An Exploratory Analysis. J. Choice Model. 2011, 4, 70–94. [Google Scholar] [CrossRef]
  92. Mitchell, R.J.; Bambach, M.R. Personal injury recovery cost of pedestrian–vehicle collisions in New South Wales, Australia. Traffic Inj. Prev. 2016, 17, 508–514. [Google Scholar] [CrossRef]
  93. Hojman, P.; de Dios Ortuzar, J.; Rizzi, L.I. On the joint valuation of averting fatal and severe injuries in highway accidents. J. Saf. Res. 2005, 36, 377–386. [Google Scholar] [CrossRef]
  94. Rizzi, L.I.; de Dios Ortúzar, J. Stated preference in the valuation of interurban road safety. Accid. Anal. Prev. 2003, 35, 9–22. [Google Scholar] [CrossRef]
  95. Iragüen, P.; de Dios Ortúzar, J. Willingness-to-pay for reducing fatal accident risk in urban areas: An Internet-based Web page stated preference survey. Accid. Anal. Prev. 2004, 36, 513–524. [Google Scholar] [CrossRef]
  96. Niroomand, N.; Jenkins, G.P. Estimating the value of life, injury, and travel time saved using a stated preference framework. Accid. Anal. Prev. 2016, 91, 216–225. [Google Scholar] [CrossRef] [PubMed]
  97. Niroomand, N.; Jenkins, G.P. Estimating the value of life and injury for pedestrians using a stated preference framework. J. Saf. Res. 2017, 62, 81–87. [Google Scholar] [CrossRef] [PubMed]
  98. Kruse, M. Costs of traffic injuries. Inj. Prev. 2015, 21, e4–e9. [Google Scholar] [CrossRef] [PubMed]
  99. Haddak, M.M. Estimating the Willingness-to-pay for Road Safety Improvement. Transp. Res. Procedia 2016, 14, 293–302. [Google Scholar] [CrossRef]
  100. Haddak, M.M. Road Users Characteristics And Their Willingness-to-Pay For Road Safety Improvement. Collect. Open Conf. Res. Transp. 2018, 2018, 622. [Google Scholar]
  101. Papadakaki, M.; Stamouli, M.-A.; Ferraro Ottavia, E.; Orsi, C.; Otte, D.; Tzamalouka, G.; von der Geest, M.; Lajunen, T.; Özkan, T.; Morandi, A.; et al. Hospitalization costs and estimates of direct and indirect economic losses due to injury sustained in road traffic crashes: Results from a one-year cohort study in three European countries (The REHABILAID project). Trauma 2017, 19, 264–276. [Google Scholar] [CrossRef]
  102. van der Vlegel, M.; Haagsma, J.A.; de Munter, L.; de Jongh, M.A.C.; Polinder, S. Health care and productivity costs of non-fatal traffic injuries: A comparison of road user types. Int. J. Environ. Res. Public Health 2020, 17, 2217. [Google Scholar] [CrossRef]
  103. Flügel, S.; Veisten, K.; Rizzi, L.I.; de Dios Ortúzar, J.; Elvik, R. A comparison of bus passengers’ and car drivers’ valuation of casualty risk reductions in their routes. Accid. Anal. Prev. 2019, 122, 63–75. [Google Scholar] [CrossRef]
  104. Jaździk-Osmólska, A. Willingness to Pay for Road Safety Improvements in Poland. Eur. Res. Stud. 2021, 24, 91–112. [Google Scholar] [CrossRef]
  105. Mohamed, H.A. Estimation of Socio-Economic Cost of Road Accidents in Saudi Arabia: Willingness-To-Pay Approach (WTP). Adv. Manag. Appl. Econ. 2015, 5, 43–61. [Google Scholar]
  106. Chin, H.C.; Haque, M.D.; Jean, Y.H. An estimate of road accident costs in Singapore. In International Conference on Road Safety in Developing Countries: Proceedings; Accident Research Centre (Bangladesh University of Engineering and Technology): Dhaka, Bangladesh, 2006; pp. 28–35. [Google Scholar]
  107. Chin, A.T.H. The social cost of fatal road traffic accidents in Singapore. In Proceedings of the Traffic and Transportation Studies, Guilin, China, 23 July 2002; Volumes 1 and 2, pp. 1148–1155. [Google Scholar]
  108. Chin, H.C. The Cost of Traffic Accidents in Singapore; WIT Transactions on The Built Environment: Southampton, UK, 2003; Volume 64. [Google Scholar]
  109. Le, H.; van Geldermalsen, T.; Lim, W.L.; Murphy, P. Deriving accident costs using willingness-to-pay approaches-A case study for Singapore. In Proceedings of the Australasian Transport Research Forum (ATRF), 34th, Adelaide, Australia, 28–30 September 2011. [Google Scholar]
  110. Andersson, H. Willingness to pay for road safety and estimates of the risk of death: Evidence from a Swedish contingent valuation study. Accid. Anal. Prev. 2007, 39, 853–865. [Google Scholar] [CrossRef] [PubMed]
  111. Andersson, H.; Lindberg, G. Benevolence and the value of road safety. Accid. Anal. Prev. 2009, 41, 286–293. [Google Scholar] [CrossRef] [PubMed]
  112. Andersson, H.; Hammitt, J.; Lindberg, G.; Sundström, K. Willingness to Pay for Car Safety: Sensitivity to Time Framing; Working Papers; Swedish National Road & Transport Research Institute: Linköping, Sweden, 2008. [Google Scholar]
  113. Andersson, H. Consistency in preferences for road safety: An analysis of precautionary and stated behavior. Res. Transp. Econ. 2013, 43, 41–49. [Google Scholar] [CrossRef]
  114. Andersson, H.; Hammitt, J.K.; Lindberg, G.; Sundström, K. Willingness to pay and sensitivity to time framing: A theoretical analysis and an application on car safety. Environ. Resour. Econ. 2013, 56, 437–456. [Google Scholar] [CrossRef]
  115. Andersson Järnberg, L.; Andrén, D.; Hultkrantz, L.; Rutström, E.E.; Vimefall, E. Willingness to Pay for Private and Public Improvements of Vulnerable Road Users’ Safety; GLO Discussion Paper; Global Labor Organization (GLO): Essen, Germany, 2021. [Google Scholar]
  116. Hultkrantz, L.; Lindberg, G.; Andersson, C. The value of improved road safety. J. Risk Uncertain. 2006, 32, 151–170. [Google Scholar] [CrossRef]
  117. Krüger, N.A.; Svensson, M. The impact of real options on willingness to pay for mortality risk reductions. J. Health Econ. 2009, 28, 563–569. [Google Scholar] [CrossRef]
  118. Persson, U.; Norinder, A.; Hjalte, K.; Gralén, K. The value of a statistical life in transport: Findings from a new contingent valuation study in Sweden. J. Risk Uncertain. 2001, 23, 121–134. [Google Scholar] [CrossRef]
  119. Svensson, M. Precautionary behavior and willingness to pay for a mortality risk reduction: Searching for the expected relationship. J. Risk Uncertain. 2009, 39, 65–85. [Google Scholar] [CrossRef]
  120. Svensson, M.; Johansson, M.V. Willingness to pay for private and public road safety in stated preference studies: Why the difference? Accid. Anal. Prev. 2010, 42, 1205–1212. [Google Scholar] [CrossRef]
  121. Jou, R.-C.; Chen, T.-Y. The willingness to pay of parties to traffic accidents for loss of productivity and consolation compensation. Accid. Anal. Prev. 2015, 85, 1–12. [Google Scholar] [CrossRef]
  122. Jou, R.-C.; Chen, T.-Y. Estimating Productivity Loss Cost according to Severity of Vehicle Crash Injury. J. Adv. Transp. 2019, 2019, 7219047. [Google Scholar] [CrossRef]
  123. Morris, A.; Welsh, R.; Barnes, J.; Chambers-Smith, D. An approach to the derivation of the cost of UK vehicle crash injuries. Annu. Proc. Assoc. Adv. Automot. Med. 2006, 50, 285–296. [Google Scholar] [PubMed]
  124. Bishai, D.; Mahoney, P.; DeFrancesco, S.; Guyer, B.; Gielen, A.C. How willing are parents to improve pedestrian safety in their community? J. Epidemiol. Community Health 2003, 57, 951–955. [Google Scholar] [CrossRef] [PubMed]
  125. Blincoe, L.J.; Seay, A.G.; Zaloshnja, E.; Miller, T.R.; Romano, E.O.; Luchter, S.; Spicer, R.S. The Economic Impact of Motor Vehicle Crashes, 2000; US National Highway Traffic Safety Administration: Washington, DC, USA, 2002.
  126. Dicker, R.A.; Lopez, D.S.; Pepper, M.B.; Crane, I.; Max, W. Cost-driven injury prevention: Creating an innovative plan to save lives with limited resources. J. Trauma 2011, 70, 985–990. [Google Scholar] [CrossRef]
  127. Wijnen, W.; Stipdonk, H. Social costs of road crashes: An international analysis. Accid. Anal. Prev. 2016, 94, 97–106. [Google Scholar] [CrossRef]
Figure 1. Screening process using a PRISMA flow diagram.
Figure 1. Screening process using a PRISMA flow diagram.
Futuretransp 03 00069 g001
Figure 2. Flow chart showing road safety valuation methods.
Figure 2. Flow chart showing road safety valuation methods.
Futuretransp 03 00069 g002
Figure 3. A breakdown of road safety valuation methods used for road injury costing.
Figure 3. A breakdown of road safety valuation methods used for road injury costing.
Futuretransp 03 00069 g003
Figure 4. Categories of data sources and frequency of use per road safety valuation method.
Figure 4. Categories of data sources and frequency of use per road safety valuation method.
Futuretransp 03 00069 g004
Figure 5. The breakdown of valuation method usage characterised by income group.
Figure 5. The breakdown of valuation method usage characterised by income group.
Futuretransp 03 00069 g005
Figure 6. Number of countries by income grouping versus the number of countries represented by included studies.
Figure 6. Number of countries by income grouping versus the number of countries represented by included studies.
Futuretransp 03 00069 g006
Figure 7. Valuation method applicability per income grouping.
Figure 7. Valuation method applicability per income grouping.
Futuretransp 03 00069 g007
Table 1. A summary of characteristics of studies included in this review.
Table 1. A summary of characteristics of studies included in this review.
MethodCountryData CollectionNo. of
WTP (CV)EthiopiaQuestionnaires7501 cityMekonnen et al. [47]
WTP (CV)SudanQuestionnaires14002 citiesMofadal et al. [48]
HCSudanExisting data; questionnaires14002 citiesMofadal & Kanitpong [49]
Not specificUgandaInterviews8601 hospitalMowafi et al. [14]
Restitution costBangladeshInterviews and existing data36916 districtsMashreky et al. [50]
WTP (CV)BangladeshQuestionnaires7805 districtsMahmud [51]
HCGhanaExisting dataN/A2 accidentsBaidoo & Ketu [52]
HC and restitution costGhanaExisting data and interviews241 municipalityKudebong et al. [53]
HC and restitution costHaitiExisting dataN/A1 hospitalZuraik et al. [54]
WTP (SP)IndiaQuestionnaires and interviews4461 countryBalakrishnan & Karuppanagounder [55]
WTP (SP/CV)IndiaQuestionnaires12001 countryBhattacharya et al. [35]
System dynamicsIndiaExisting data, questionnaires and interviewsNot specificN/ABora et al. [56]
HCIndiaExisting data, questionnaires and interviews951 cityReddy et al. [57]
HC (Gross Output)IndonesiaInterviews and existing data701 provinceSugiyanto [58]
HCIndonesiaInterviews and existing data1001 provinceSugiyanto & Santi [59]
WTP (CV) and HC (Gross Output)IndonesiaQuestionnaires501 countryWidyastuti and Mulley [60]
HCIranExisting dataN/A1 countryAhadi & Razi-Ardakani [61]
WTP (CV, SP and RP)IranQuestionnaires8461 countryAiny et al. [62]
WTP (CV, SP and RP)IranQuestionnaires4101 cityAiny et al. [63]
WTP (CV, SP and RP)IranQuestionnaires1431 countryAiny et al. [36]
WTP (CV, SP and RP)IranQuestionnaires846Not specificAiny et al. [64]
HCIranExisting dataN/AN/AHejazi et al. [65]
WTP (CV)IranQuestionnaires5901 cityJarahi et al. [66]
HC and restitution costIranExisting data and interviewsN/A1 hospitalKavosi et al. [67]
HCIranExisting dataN/A2 hospitalsRezaei et al. [68]
WTP (CV)MyanmarQuestionnaires and interviews12227 regionsMon et al. [69]
WTP (CV)MyanmarQuestionnaires3857 regionsMon et al. [70]
HC and restitution costNepalExisting dataN/A1 countryBanstola et al. [32]
HC (Gross Output)NepalExisting data, interviews and questionnaires1001 zoneSapkota et al. [71]
Restitution costNigeriaInterviews2661 tertiary hospitalUrua et al. [72]
HC (Gross Output)PhilippinesExisting dataN/A2 tertiary hospitalsDe Leon et al. [73]
HC (Gross Output)VietnamExisting dataN/A1 countryAnh et al. [19]
WTP (CV)VietnamQuestionnaires4143 districtsPham et al. [74]
WTP, HC and restitution costAzerbaijanQuestionnaires, interviews and existing data2001 countryWorld Bank [27]
HCBelizeExisting dataN/A1 hospitalPérez-Núñez et al. [75]
WTP (CV)ChinaQuestionnaires10501800 peopleLiu & Zhao [76]
WTP (SP)ChinaQuestionnaires10921 cityZheng et al. [77]
Not specificChinaExisting dataN/A1 countryTan et al. [78]
WTP, HC and restitution costColombia,
Mexico and
Existing dataN/A4 countriesBhalla et al. [46]
WTP and HCJordanQuestionnaires, interviews and existing data4111 countryGhadi et al. [79]
HCJordanExisting dataN/A1 countryJadaan et al. [80]
WTP, HC and restitution costKazakhstanQuestionnaires, interviews and existing data13651 districtWijnen [26]
WTP (CV)MalaysiaQuestionnaires3201 countryMohd Fauzi et al. [81]
WTP (CV)MalaysiaQuestionnaires8551 countryNor & Yusoff [82]
WTP (SP)MalaysiaQuestionnaires30001 countryYusof et al. [83]
WTP (SP)MalaysiaQuestionnaires30001 countryYusoff et al. [84]
HC and restitution costMexicoQuestionnaires, interviews and existing data2974 medical facilitiesPérez-Núñez et al. [85]
HCSerbiaExisting dataN/A1 countryAntić et al. [86]
HC (Gross Output)South AfricaExisting dataN/A1 countryOlukoga & Harris [87]
WTP (CV)ThailandQuestionnaires10151 cityChaturabong et al. [88]
WTP (CV)ThailandQuestionnaires12001 countryPuttawong & Chaturabong [89]
HCThailandExisting dataN/A1 countryChantith et al. [8]
Personal Injury Recovery Cost (PIRC)AustraliaExisting dataN/A1 countryBambach & Mitchell [90]
HCAustraliaExisting dataN/A8 statesConnelly & Supangan [7]
WTP (SC)AustraliaQuestionnaires3851 countryHensher et al. [39]
WTP (SC)AustraliaQuestionnaires3121 countryHensher et al. [91]
Personal Injury Recovery Cost (PIRC)AustraliaExisting dataN/A1 countryMitchell & Bambach [92]
WTP and HC (Gross Output)BelgiumExisting dataNot specificNot specificDe Brabander & Vereeck [6]
WTP (SP/SC)ChileQuestionnaires495Not specificHojman, et al. [93]
WTP (SP)ChileQuestionnaires3421 routeRizzi & de Dios Ortúzar [94]
WTP (SP)ChileQuestionnaires3201 countryIragüen & de Dios Ortúzar [95]
WTP (SP)CyprusQuestionnaires and interviews3745 districtsNiroomand & Jenkins [96]
WTP (SP)CyprusQuestionnaires and interviews3745 districtsNiroomand & Jenkins [5]
WTP (SP)CyprusQuestionnaires and interviews3785 districtsNiroomand & Jenkins [97]
Restitution costDenmarkExisting dataN/A1 countryKruse [98]
WTP (SP/CV)FranceQuestionnaires and interviews22262 areasHaddak [99]
WTP (CV)FranceQuestionnaires1941 countryHaddak et al. [37]
WTP (SP/CV)FranceQuestionnaires22261 areaHaddak [100]
Restitution costGreece, Germany and ItalyQuestionnaires and interviews933 countriesPapadakaki et al. [101]
Standard gambleJapanQuestionnaires and interviews6411 countryKoyama & Takeuchi [43]
Restitution costNetherlandsQuestionnaires and interviews10241 countryvan der Vlegel et al. [102]
WTP (SP)NorwayQuestionnaires29631 countryFlügel et al. [103]
WTP (SP)PolandQuestionnaires and interviews10851 countryJaździk-Osmólska [104]
HCPortugalExisting dataN/A1 countryDonário et al. [10]
HCRomaniaExisting dataN/A1 countryDrosu & Cofaru [9]
WTPSaudi ArabiaQuestionnaires1481 cityMohamed [105]
HCSingaporeExisting dataN/A1 countryChin et al. [106]
HCSingaporeExisting dataN/A1 countryChin [107]
HC (Gross Output)SingaporeExisting dataN/A1 countryChin [108]
WTP (SP/CV)SingaporeQuestionnaires and interviews15001 countryLe et al. [109]
WTP (SC)SpainQuestionnaires and interviews4771 cityGonzález et al. [38]
WTP (CV)SpainQuestionnaires and interviews20201 countrySánchez-Martínez et al. [44]
WTP (RP)SwedenExisting dataN/A1 countryAndersson [11]
WTP (CV)SwedenQuestionnaires9771 countryAndersson [110]
WTP (CV)SwedenQuestionnaires19501 cityAndersson & Lindberg [111]
WTP (SP/CV)SwedenQuestionnaires8711 countryAndersson et al. [112]
WTPSwedenQuestionnaires9771 countryAndersson [113]
WTP (SP/CV)SwedenQuestionnaires9201 countryAndersson et al. [114]
WTP (SP/CV)SwedenQuestionnaires10221 countryAndersson Järnberg et al. [115]
WTP (CV)SwedenQuestionnaires8731 cityHultkrantz et al. [116]
WTP (SP/CV)SwedenQuestionnaires9401 cityKrüger & Svensson [117]
WTP (CV)SwedenQuestionnaires6751 cityPersson et al. [118]
WTP (SP/CV)SwedenQuestionnaires11482 citiesSvensson [41]
WTP (SP/CV)SwedenQuestionnaires15001 citySvensson [119]
WTP (SP)SwedenQuestionnaires15001 citySvensson & Johansson [120]
WTP (SP)TaiwanQuestionnaires and existing data40891 countryJou & Chen [121]
WTP (SP/CV)TaiwanQuestionnaires21221 countryJou & Chen [122]
WTPUKAIS and national accident statistics3001 countryMorris et al. [123]
WTPUKExisting dataN/A1 cityCampbell et al. [4]
WTP (CV)USAQuestionnaires7231 cityBishai et al. [124]
HCUSAExisting dataN/A1 countryBlincoe et al. [125]
Not specificUSAExisting dataN/A1 stateDicker et al. [126]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Nankunda, C.; Evdorides, H. A Systematic Review of the Application of Road Safety Valuation Methods in Assessing the Economic Impact of Road Traffic Injuries. Future Transp. 2023, 3, 1253-1271.

AMA Style

Nankunda C, Evdorides H. A Systematic Review of the Application of Road Safety Valuation Methods in Assessing the Economic Impact of Road Traffic Injuries. Future Transportation. 2023; 3(4):1253-1271.

Chicago/Turabian Style

Nankunda, Charity, and Harry Evdorides. 2023. "A Systematic Review of the Application of Road Safety Valuation Methods in Assessing the Economic Impact of Road Traffic Injuries" Future Transportation 3, no. 4: 1253-1271.

Article Metrics

Back to TopTop