Road Safety Investment Research Articles

Internalizing External Accident Costs in Safety Investment Evaluation Using Cost–Benefit Analysis

The primary objectives of transportation system development in European countries include improving transport efficiency, enhancing national economic competitiveness, and promoting sustainable mobility. A key aspect of achieving these goals is the evaluation of transport externalities, particularly external accident costs, which is critical for ensuring the economic and social sustainability of road transport development. This study comprehensively evaluates external road accident costs and proposes an innovative approach to internalizing these costs through a cost–benefit analysis (CBA). By integrating these external costs into CBA, policymakers can better understand the societal impact of road safety investments, allowing for more informed decisions regarding measures to reduce road accidents and fatalities. This paper also explores the role of specific safety investments, such as installing vehicle restrain systems (VRS), buffers, and terminals on the national road network, as well as installing speed cameras, in reducing accidents and mitigating external costs. The findings highlight road safety measures’ importance in enhancing economic efficiency and social well-being, providing a solid foundation for the sustainable and efficient development of transportation systems.

Case Study on Rolling Barrier on Pune- Mumbai Highway

Abstract: In India, the transportation industry has grown quickly, especially since the turn of the century. To connect diverse cities and towns, the Indian government is pouring enormous sums of money into the infrastructure and transportation sectors. This paper highlights on the need for cost effective road safety investments using 'rolling barrier' systems which can redirect the deviated automobiles onto the right path and also prevent the overturning of vehicles. Road accidents are a result of the interaction of many factors, including the length of road networks, the number of vehicles, the population's adherence to and enforcement of traffic laws, etc. This study intends to assess the rolling barrier's efficiency and comprehend its crash cushioning properties, as well as how to rectify the vehicles' running direction and the necessary barrier strength. The Rolling Barrier satisfied the ministry of construction and transportations, “Guidelines for Installation and management of road safety facilities”. Curved road portions, ramps, medians, and entrance or departure ramps in parking garages can all be safely protected by the Rolling Barrier.

Estimating the Value of Statistical Life in a Road Safety Context Based on the Contingent Valuation Method

A cost-benefit analysis in a road safety context fundamentally analyzes the advantage of higher safety or lower risk. It can help determine if increasing spending on road safety programs is cost-effective. This study estimates the value of statistical life (VSL)—the amount of money that might be justified to save one person’s life. The VSL is calculated using the willingness to pay (WTP) data obtained through a contingent valuation survey. Three discrete choice models are developed: log-logistic, log-normal, and Weibull. The log-logistic model outperforms the log-normal and Weibull models, comparing Akaike’s Information Criterion (AIC) and Bayesian Information Criterion (BIC) values. We consider the log-logistic model’s mean and median WTP values to estimate the VSL value in the Ethiopian road transport safety context. The VSL estimate in the Ethiopian road transport safety context is 53.52 million ETB (USD 1.07 million). The respondents’ median WTP is ETB 714.44 (USD 14.23). Although the study is in Ethiopia, the findings can be applied to other low- and middle-income countries (LMICs) for the same purpose with modifications. The research findings will aid in a better understanding of the economic efficiencies of increased spending on road safety initiatives. Future research could compare current trends in road safety investment to the amount that should be spent based on the economic justifications from this study.

교통안전 투자의 경제성 분석

This study analyzes the economic efficiency of central governments' road safety investments. The value of a statistical life lost due to road accident was estimated to be about 5.3 billion won, using the contingent valuation method. The amounts of investment in road safety facilities and road safety education in 2021 turned out to be about 2.78 trillion won and 0.13 trillion won, respectively. The resulting effects on the number of death prevented were 3,324 and 156, respectively. And these led to the estimates of social benefits of 17.49 trillion won and 0.82 trillion won, respectively. The benefit-cost ratio turned out to be 6.3 for both types of investment. This result implies that we need a huge increase in the amount of investment for road safety policies, in order to improve our road traffic culture which is below the OECD average level.

A systematic review of road safety investment appraisal models

It is estimated that 1.35 million people die annually due to road traffic crashes and these crashes cost most countries 3–5% of their gross domestic product (GDP). The economic appraisal of roads c...

Characteristics of Road Traffic Accident Cases Reported in a Tertiary Military Hospital

Introduction: Road traffic accidents (RTA) are the eighth leading cause of death globally, and the leading cause of death in Bangladesh. The road safety situation in Bangladesh has been deteriorating with increasing number of road accident deaths, largely as direct consequences of rapid growth in population, motorization, urbanization and lack of investment in road safety.
 Objectives: To describe the injury characteristics of road traffic accidents cases that reported at Combined Military Hospital (CMH), Dhaka.
 Materials and Methods: This descriptive cross-sectional hospital based study was conducted on all the RTA cases reported to medical inspection room CMH, Dhaka from 01 March to 30 June 2010. A total of 126 RTA victims irrespective of age and sex were included, data were collected by face to face interview using a pre-tested questionnaire.
 Results: A total of 126 road traffic accident victims both sexes were studied. The majority of the study subjects were men 103 (81.7%) and most of them were in 31-40 years group (27%), Muslims (98.41%) were the majority. In our study regarding educational qualification of RTA victims most of them were SSC qualified (44’4%) and primary level (15.1%). Military services holder (45.2%) was the majority of road traffic crash victims. Bus/Minibus (32.5%) was responsible for the majority of road traffic crashes and motor vehicle passengers (42.9%) were main victims. Laceration & cut injury were (38.1%) and multiple injury (18.3%) were the most common injury. Main roads (49.2%) were the commonest site of the accidents. Regarding place of occurrence main roads 62 (49.2%) was the most common site and noon 41 (32.5%) was the most common time of occurrence of accidents.
 Conclusion: Road traffic crashes constitute a major public health problem in our setting and contribute significantly to unacceptably high morbidity and mortality. Urgent preventive measures targeting at reducing the occurrence of road traffic crashes is necessary to reduce the morbidity and mortality resulting from these injuries. Early recognition and prompt treatment of road traffic injuries is essential for optimal patient outcome.
 JAFMC Bangladesh. Vol 15, No 2 (December) 2019: 213-217

Road Safety Assessment Considering the Expected Fatal Accident Density

Network-wide road safety assessment throughout the whole network is one of the four road infrastructure safety management procedures regulated by Directive 2019/1936/EC of the European Parliament and of the Council of 23 October 2019 Аmending Directive 2008/96/EC on Road Infrastructure Safety Management and one of the methods for determining the direction of investment in road safety. So far, the implementation of the procedure has been lightly regulated and adapted using various road safety indicators. The paper describes the evaluation of road accident data that is one of the criteria for conducting a network-wide road safety assessment. Taking into consideration that networkwide road safety assessment is a proactive road safety activity, the paper proposes to conduct road safety assessment considering the expected fatal accident density. Such assessment makes it possible to assess the severity of accidents, and the use of the predicted road accident data on calculating the introduced road accident rate contributing to the prevention of accidents. The paper describes both the empirical Bayes method for predicting road accidents and the application of one of the road safety indicators – the expected fatal accident density – to determine five road safety categories across the road network. The paper demonstrates the application of the proposals submitted to Lithuanian highways using road accident and traffic data for the period 2014–2018.

Road safety as a factor in the socio-economic development of the country

In the conditions of modern society, the prospective development of the country’s economy cannot be considered without a well-established transport system. The discrepancy between the level of development of the transport network and the level of demand for road transport leads to inefficient and huge costs. The main costs are associated with the elimination of the consequences of road accidents. The article considers road safety as a particularly significant socio-economic problem of the country. The relationship between economic development and the level of road safety was studied using the example of the Republic of Tatarstan (a subject of the Russian Federation). As main indicators we selected population, level of motorization, length of transport networks, gross regional product, accident rates and damage as a result of deadly accidents. Studies have shown that there is an interdependence of road safety and the economy of the republic. Moreover, it is important to assess the direct and indirect socio-economic costs of the consequences of road accidents. This allows us to measure the burden that a traffic accident causes to society, and emphasizes the return on investment in road safety.

A comparative analysis of road safety across the provinces of Iran from 2005 to 2015

During the most recent decade, Iran has invested in road safety improvement programs and has experienced a reduction in road fatalities, with rates of improvement differing across provinces. Knowing which road safety investments are performing well across provinces is essential for wisely investing in future road safety improvements. This firstly requires identification of successful provinces. Accordingly, the aim of this study is to evaluate the relative performance of Iranian provinces in improving their safety, while accounting for a variety of factors that influence road safety outcomes. To achieve this aim, traffic fatality rates recorded in each province during 2005–2015 were analyzed. The analysis framework involved first estimating a panel data model to control for random variations as well as exogenous factors—in effect providing a safety performance function for provinces. Then, the “expected provincial safety performance” was compared to actual provincial safety performance, to identify “outlying” provinces with respect to safety performance. The results reveal that the fatality rates are positively associated with exposure as captured by per capita fuel consumption, and percentage of undivided two-lane, two-way rural roads, and negatively related to urban to rural population ratio, population density, and hospital beds per 100,000 residents. Moreover, the number of speed cameras per 100 km rural roads is associated with reduced fatality rates. A comparative assessment of provinces over the 11 year period revealed significant differences among the provinces in terms of safety performance in each year as well as safety improvement rates over time.

A decision support toolkit to inform road safety investment decisions

Road safety practitioners are tasked with maintaining safety on their network, primarily by identifying collision hotspots to which resources should be allocated and by ensuring that road safety schemes are operating effectively. Both these tasks often require the analysis of road safety count data (e.g. regarding collisions or casualties); however, these data are frequently bedevilled by confounding statistical factors such as regression to the mean (RTM) and trend. Failing to account for these factors can lead to misallocation of resources as well as sites at risk of high counts not receiving treatment. To overcome this, methods are proposed in this paper which clean data for RTM and trend to allow for more accurate scheme evaluation, and a proactive approach for hotspot prediction. Unfortunately, these techniques require the development of complex, statistical algorithms and so can be inaccessible to some practitioners. To overcome this, user-friendly software applications have been developed and are described here, which implement the aforementioned techniques, with an additional application to investigate the potential collision contributory factors at sites.

Road Safety Management Model

Traffic accidents are a serious problem in every society. Traffic safety is a science that analyzes road accidents with material damage, lightly injured, severely injured and killed persons. This paper proposes that road safety investment can be optimised by the development of a road safety management model. Road safety strategies typically include a basket of engineering, enforcement and education/training measures but there does not appear to be any management model which permits the optimisation of road safety investment. The proposed model utilises linear programming to predict changes in road safety resulting from safety interventions. It is mainly based on research in the areas of engineering and enforcement since there is little published research on the correlation between education and accident reduction. The model output provides the accident reduction and associated costs resulting from feasible road safety strategies. This should benefit policy makers when allocating resources. This example will be good experience for Traffic safety management system in the Republic of Macedonia.

Road safety: serious injuries remain a major unsolved problem.

To investigate temporal trends in the incidence, mortality, disability-adjusted life-years (DALYs), and costs of health loss caused by serious road traffic injury. A retrospective review of data from the population-based Victorian State Trauma Registry and the National Coronial Information System on road traffic-related deaths (pre- and in-hospital) and major trauma (Injury Severity Score > 12) during 2007-2015.Main outcomes and measures: Temporal trends in the incidence of road traffic-related major trauma, mortality, DALYs, and costs of health loss, by road user type. There were 8066 hospitalised road traffic major trauma cases and 2588 road traffic fatalities in Victoria over the 9-year study period. There was no change in the incidence of hospitalised major trauma for motor vehicle occupants (incidence rate ratio [IRR] per year, 1.00; 95% CI, 0.99-1.01; P = 0.70), motorcyclists (IRR, 0.99; 95% CI, 0.97-1.01; P = 0.45) or pedestrians (IRR, 1.00; 95% CI, 0.97-1.02; P = 0.73), but the incidence for pedal cyclists increased 8% per year (IRR, 1.08; 95% CI; 1.05-1.10; P < 0.001). While DALYs declined for motor vehicle occupants (by 13% between 2007 and 2015), motorcyclists (32%), and pedestrians (5%), there was a 56% increase in DALYs for pedal cyclists. The estimated costs of health loss associated with road traffic injuries exceeded $14 billion during 2007-2015, although the cost per patient declined for all road user groups. As serious injury rates have not declined, current road safety targets will be difficult to meet. Greater attention to preventing serious injury is needed, as is further investment in road safety, particularly for pedal cyclists.

Preventing traffic accidents with in-vehicle decision support systems - The impact of accident hotspot warnings on driver behaviour

Despite continuous investment in road and vehicle safety, as well as improvements in technology standards, the total amount of road traffic accidents has been increasing over the last decades. Consequently, identifying ways of effectively reducing the frequency and severity of traffic accidents is of utmost importance. In light of the depicted challenge, latest studies provide promising evidence that in-vehicle decision support systems (DSSs) can have significant positive effects on driving behaviour and collision avoidance. Going beyond existing research, we developed a comprehensive in-vehicle DSS, which provides accident hotspot warnings to drivers based on location analytics applied to a national historical accident dataset, composed of over 266,000 accidents. As such, we depict the design and field evaluation of an in-vehicle DSS, bridging the gap between real world location analytics and in-vehicle warnings. The system was tested in a country-wide field test of 57 professional drivers, with over 170,000km driven during a four-week period, where vehicle data were gathered via a connected car prototype system. Ultimately, we demonstrate that in-vehicle warnings of accident hotspots have a significant improvement on driver behaviour over time. In addition, we provide first evidence that an individual's personality plays a key role in the effectiveness of in-vehicle DSSs. However, in contrast to existing lab experiments with very promising results, we were unable to find an immediate effect on driver behaviour. Hence, we see a strong need for further field experiments with high resolution car data to confirm that in-vehicle DSSs can deliver in diverse field situations.

Road safety and the urban emergency medical service (uEMS): Strategy station location

Abstract This paper provides a methodology on how to contribute to road safety by improving the vehicle response of urban emergency medical services (uEMS) using road safety investments. The methodology embodies two steps. The first step includes a demand assessment through a model that calculates the frequency of urban emergency events and their priority in a spatial area per different population demographics and urban characteristics. These events are categorized by type, which we separated as road crashes, cardiac arrests and other emergency events that require the dispatch of an ambulance with a medical team. The second step proposes an optimization model developed to maximize the uEMS vehicle coverage, considering road crashes by locating ambulance stations in the urban area, giving priority to high-priority emergencies and strategically double-covering road crashes. The applicability and practical interest of the proposed models are proven by applying them to the real-world case of the metropolitan area of Porto, where data on the emergency service response, land use and population demographics are available. The final conclusions indicate that areas prone to high volumes of traffic and high speed roads lead to higher volumes of road crash emergencies, and stations should be located closer to high speed roads. Moreover, further investigations should entail micromanagement improvements to assist in road crashes.

Road Safety Investments and Interventions in South East Europe

Countries of South-East Europe show relatively low road safety performance among the EU Members. The objective of this paper is the exploration of a number of potential investments and interventions for the improvement of road safety in South-East European regions with regard to relative legislation, policy and institutional capacity. For the identification of the most appropriate road safety investments and interventions, a three step methodology was followed. Firstly, results of the, EU co-funded, ROSEE project concerning several aspects of road safety legislation, policy and institutional capacity in South-East European regions were exploited to identify the particular issues on which the proposed investments and interventions should focus. Then, relevant proposals and recommendations from the international bibliography were taken into account. Finally, original work was carried out by assessing and ranking selected road safety measures, programs, and policies. Specific road safety interventions were assessed and ranked based on three criteria, the estimated safety benefit related to the measure, the implementation cost and the implementation time needed for benefit. The overall results show that road safety legislation, policy and institutional capacity related investments and interventions are generally considered very effective for the improvement of road safety. On the other hand, it seems that most such investment proposals are considered relatively expensive to implement and effective on the long-term. These results were expected and are meaningful given that, based on international literature, the legislation, policy and institutional capacity are strategic aspects of road safety which, if taken into account at national level and in long-term, are considered highly effective. Specifically, the overall results show that investments and interventions considered to provide high safety benefit at low cost, in most partner countries, are the legislation for infrastructure safety management and the legislation for efficient enforcement. However, both types of investments need time to show their effect on the improvement of road safety. The analysis of the assessment results per country revealed important differences among South-East European regions that may be attributed to particular local characteristics and conditions.

Application of a Crash-predictive Risk Assessment Model to Prioritise Road Safety Investment in Australia

Australia experiences many similar strategic road safety challenges as most European countries. These include the objective to strongly reduce fatalities and serious injuries, budgetary constraint requiring prioritisation of road investment, and the growing need for improved integration of road transport to drive efficiency. The current National Road Safety Strategy 2011–2020 aims for a 30% reduction in fatal and serious injuries, a step on a path towards the Safe System (Vision Zero). This aim is made more challenging by the geographically scattered nature of fatal and serious (severe) crashes on the road network, especially on routes with moderate traffic flows or in regional areas. This problem has led to reducing economic returns from conventional road safety initiatives based on treatment of crash-cluster locations or lengths.This paper shows how the Australian National Risk Assessment Model (ANRAM) addresses this challenge and assists state road agencies to meet the aims of the national strategy. ANRAM is used by the agencies to assess the risk of future severe crashes across their road networks. It is then used to prioritise those sections based on severe crash estimates, less susceptible to random variation (the scatter). The model facilitates creation of strategically-aligned infrastructure investment programs to reduce the severe crash risk and to estimate future crash savings. These are used together with program capital costs to carry out economic analysis and to support funding decisions.The paper outlines how ANRAM uses a crash-predictive approach to first estimate mean severe crashes per road section, by crash type. Then, it shows how ANRAM adjusts the estimate using risk algorithms which build on the International Road Assessment Program (iRAP) protocols. These algorithms use detailed road attribute data and research evidence about crash risk potential associated with road design/infrastructure, traffic speeds, and likelihood of vehicle conflicts. Finally, the model uses historical severe crash data in an Empirical Bayes validation technique to provide additional confidence in the estimates.ANRAM has enabled Australian road agencies to scope and prioritise proactive road safety investment options before severe crashes form a historical data clusters. The paper presents several examples of recent programs which reflect jurisdictional priorities, unique local conditions and available resources.The paper concludes with a discussion on how the approach used in ANRAM could have relevance to effective programming of road safety investment for European road agencies. It also suggests additional benefits beyond road safety, e.g. though providing inputs into road data inventories used in transport modelling.

Evaluating Safety Benefits of the Insurance Corporation of British Columbia Road Improvement Program Using a Full Bayes Approach

The objective of this study was to conduct a time-series (before-to-after) evaluation of the safety performance of a sample of locations that have been improved under the Insurance Corporation of British Columbia (ICBC) Road Improvement Program. The program started in 1989 when ICBC established partnerships with local road authorities in British Columbia, Canada, and works cooperatively to make sound investments in road safety improvements. The overall effectiveness of the road improvement program was assessed by determining whether the frequency or severity of collisions at the improvement sites has been reduced after implementation of the improvement, and by quantifying the program costs versus the economic safety benefits to determine the return on ICBC’s road safety investment. Seventy-two urban intersections were included in the evaluation. The methodology adopted for estimating the safety benefits was a before–after study with the full Bayesian method, while the benefit–cost analysis was carried out by using two indicators: net present value and benefit–cost ratio (B/C) with a payback period of 5 years. Overall, the total reductions of severe (fatal plus injury) and nonsevere (property damage only) collision frequency for intersections with new pedestrian signal installations were found equal to −24.54% and −6.21%, respectively; −22.95% and −10.78%, respectively, for intersections with geometric design improvements; and −13.76% and −5.04%, respectively, for intersections with traffic signal upgrades. Finally, an overall B/C ratio of 4.32:1 was achieved.

Quantification of Road Safety Risk at Locations Without Collisions to Justify Road Safety Investments

Since creating its Road Improvement Program (RIP), the Insurance Corporation of British Columbia (ICBC) has invested more than $110 million in road safety improvements in British Columbia, Canada. The RIP partners with provincial road authorities to identify problem locations and implement interventions to improve road safety. The level of ICBC investment in a safety project has been based on the potential for collision reduction associated with the proposed improvements. The goal has been to target collision-prone locations to reduce frequency and severity of collisions and, therefore, auto insurance claim costs to ICBC. Although the projects funded by the RIP were entirely necessary and effective, the reactive nature of the program did not allow for investments at locations that were deemed to be high risk but that did not have a significant history of collisions. In recognition that some attention should be given to these high-risk, low-crash locations, a new program was developed to provide funding to support road improvement projects that were not based on a history of collisions. The new program, referred to as the Proactive Road Safety Program, complemented the reactive program through the provision of funding support for projects that could prevent, rather than reduce, collisions. This paper describes the rationale and methodology developed for the Proactive Road Safety Program to quantify road safety risk and assign an economic value to justify funding for proactive road safety projects.

Value of a statistical life in road safety: A benefit-transfer function with risk-analysis guidance based on developing country data

We model a value of statistical life (VSL) transfer function for application to road-safety engineering in developing countries through an income-disaggregated meta-analysis of scope-sensitive stated preference VSL data. The income-disaggregated meta-analysis treats developing country and high-income country data separately. Previous transfer functions are based on aggregated datasets that are composed largely of data from high-income countries. Recent evidence, particularly with respect to the income elasticity of VSL, suggests that the aggregate approach is deficient because it does not account for a possible change in income elasticity across income levels. Our dataset (a minor update of the OECD database published in 2012) includes 123 scope-sensitive VSL estimates from developing countries and 185 scope-sensitive estimates from high-income countries. The transfer function for developing countries gives VSL=1.3732E−4×(GDP per capita)∧2.478, with VSL and GDP per capita expressed in 2005 international dollars (an international dollar being a notional currency with the same purchasing power as the U.S. dollar). The function can be applied for low- and middle-income countries with GDPs per capita above $1268 (with a data gap for very low-income countries), whereas it is not useful above a GDP per capita of about $20,000. The corresponding function built using high-income country data is VSL=8.2474E+3×(GDP per capita)∧.6932; it is valid for high-income countries but over-estimates VSL for low- and middle-income countries. The research finds two principal significant differences between the transfer functions modeled using developing-country and high-income-country data, supporting the disaggregated approach. The first of these differences relates to between-country VSL income elasticity, which is 2.478 for the developing country function and .693 for the high-income function; the difference is significant at p<0.001. This difference was recently postulated but not analyzed by other researchers. The second difference is that the traffic-risk context affects VSL negatively in developing countries and positively in high-income countries. The research quantifies uncertainty in the transfer function using parameters of the non-absolute distribution of relative transfer errors. The low- and middle-income function is unbiased, with a median relative transfer error of −.05 (95% CI: −.15 to .03), a 25th percentile error of −.22 (95% CI: −.29 to −.19), and a 75th percentile error of .20 (95% CI: .14 to .30). The quantified uncertainty characteristics support evidence-based approaches to sensitivity analysis and probabilistic risk analysis of economic performance measures for road-safety investments.

Development of an Accident Prediction Model for Intersections of Dhaka City, Bangladesh

Road accidents are increasing at an alarming rate. Every year more than 1.17 million people die in road crashes around the world. The majority of these deaths, about 70 percent occur in developing countries .As a developing country, Bangladesh is not out of this situation. The road safety situation in Bangladesh has been deteriorating with rapid growth in population, motorisation, urbanisation and lack of investment in road safety. The combination of rapid urbanization and motorization has made the problem even severe . For our paper at first we collect data to analysis the severity of accident in Bangladesh. We collect the data of accidents & rearrange these with respect to weather ,collision type, day of week etc .For this arrangement we collect the data of accident for the last five years . Our main concern were the intersections of Dhaka city. We then select twenty five intersections and collect data of road width and number of approaches. Then we develop a model of accident prediction with the collected data. The major findings of our project is we found that accidents increase in good weather ,it may be because in good weather drivers may be more relax and less conscious as they think that everything is seen clearly .Another finding is as Dhaka is a populated city, number of accident with pedestrian is much higher than other type (with vehicle ,other objects etc.) of accidents . Again number of accident increase with increase in number of approaches in a intersection and decrease with increase in road width. From this prediction model we can get the approximate number of accident that can happen per year and we can take proper steps and precautions such as speed breakers , road dividers ,proper signs , marking ,speed limit ,proper signal design to avoid such accidents . General Terms Road safety issue.