Safety assessment of Czech motorways and national roads

The idea of road safety impact assessment (RSIA) is that road safety should play an important role in the decision making during the design/planning stage [32, 50, 91]. The 2008 Directive defines RSIA as “a strategic comparative analysis of the impact of a new road or a substantial modification to the existing network on the safety performance of the road network”, conducted alongside with other impact assessments, such as environmental impact assessment (EIA). By 2011, EU member states implemented the Directive procedures, including RSIA, to their national guidelines. However, independent analysis [79] concluded that in most cases, implementation only resulted in legislative documents, which offer mere Directive translations, rather than detailed guidelines for practical use. This is also probably a reason why RSIA remains the least used of Directive processes [79].

In principle, RSIA process involves definition and comparison of project variants (“do nothing” and one or more alternative scenarios), of which road safety performance needs to be assessed, including impacts on adjacent road network. Therefore, the underlying principle is quantification of safety. For this purpose, rates derived from accidents and injuries have been traditionally used, based on Police accident records and tabulated for several road network categories. This approach has been used in majority of European countries [12, 14, 19, 33, 36, 53, 85, 87]. However, in recent years, general use of accident rates is declining, since they were found to incorrectly assume a linear relationship between accident frequency and the degree of exposure [31, 41, 66]. Accident prediction models (APMs, or safety performance functions, SPFs) are instead preferred, since they are able to model accident frequency non-linearity, while also taking into account multiple explanatory variables (potential risk factors).

Another element of state-of-the-art approach to safety estimation involves accident modification factors (AMFs, also known as crash modification factors, CMFs). These are multiplicative factors, used for calculating the expected number of accidents after implementing safety measure at a specific site, through multiplication with expected accident frequency without treatment [38]. An AMF value higher than 1.0 indicates an expected increase in accidents, while a value lower than 1.0 indicates an expected reduction in accidents after the treatment. Various methodologies may be used in order to obtain AMF values, while before-after methodology with Empirical Bayes adjustment, has been deemed the most suitable [42, 64, 74]. In addition, AMFs were recently put into the centre of evidence-based decision-making [92], as a foundation of efficiency assessments to be applied in all cases, where “lack of reliable knowledge of the effects of countermeasures is a key barrier to the advancement of many critical, life-saving initiatives” [59].

To sum up, road safety impact assessment should be based on state-of-the-art assessment approach, using accident prediction models (APMs) and accident modification factors (AMFs). These elements constitute the foundations of evidence-based road safety management [28, 45, 96]. However, most of existing APMs and AMFs were developed in North America, Australasia or North-Western Europe [95], and it is known that these results may not be easily transferable [40, 59]. This means that should state-of-the-art approach be applied, a number of EU member states, including the Czech Republic, need to develop their own tools. [9]

In Europe, APMs and AMFs are used for example in Denmark, Sweden and Finland [62, 82, 88]. Analysis of their guidelines and manuals showed that:

The Directive states, that “safety ranking and management of the road network in operation” (in short, network safety ranking, NSR) should take into account the number of accidents, that have occurred in previous years per unit of road length in relation to the volume of traffic; and it should result in a priority list of road sections where an improvement of the infrastructure is expected to be highly effective. However, this description is rather short and thus provides, as well as RSIA description in the previous subsection, space for users in EU countries to apply their own approaches.

Several reviews indicated that in most countries, it is common to rely on traditional approaches, based on exceeding a fixed threshold of number of Police-reported accidents, without considering traffic volume and other risk factors [22, 30, 37, 52]. Such definitions have also been applied in the Czech Republic [12, 86]. However, the research has shown that safety performance is influenced not only by accident occurrence, but also by its random variations, known as regression to the mean [42]. Due to this effect, the locations (road sections or intersections) identified as critical in one period, may not be critical in another period. The effect of these random fluctuations around a long-term mean may be corrected for by adding information on safety of similar units. The previously mentioned Empirical Bayes (EB) method, i.e. combination of the information contained in accident counts with the information contained in knowing the safety of similar entities, through using an accident prediction model, is considered a state-of-the-art method of road safety assessment [30, 44, 56]. With the EB approach, in the end the list is produced which enables ranking the locations based on their potential for safety improvement [1]. The list includes not only accident locations, but also potentially hazardous locations, where no accidents have yet occurred.

Nevertheless, to be able to apply the EB method on a primary road network, it is necessary to develop a set of accident prediction models for all types of network elements (road sections, intersections, interchanges, etc.). For example, US Highway Safety Manual (HSM) introduced a number of US-specific accident prediction models. It was found that these models are well transferable between the US states [21, 76, 94]; however, transferability outside of the US was not always successful, as indicated by studies in Canada or Italy [65, 71, 97]. In the USA, also interchange safety analysis tools are available [20, 80]. But like with HSM transferability, calibration of these tools may be uncertain [25, 81]. At the same time, several European countries have developed their own accident prediction models. However, they differ in their level of complexity: sometimes they are rather accident rates (i.e., implying linear relationship between traffic volume AADT and accidents) and they often consider either sections or intersections only [47, 62, 82]. In terms of data requirements, motorways can be even more challenging due to complexity of interchanges. This is probably why motorway safety studies usually omit interchanges or consider them part of sections [24, 27, 55, 73, 98].

Based on data from several European countries, a recent PRACT project has developed the base model to be calibrated to local conditions based on historical data [49]; limitation is that the models do not consider intersections, and the calibration tool is not yet available.

Following the international research, EB method with accident prediction model was recently applied in a part of Czech secondary road network [6]. However, its application in more complex primary road network has not yet been attempted.

The literature review, focusing on international state-of-the-art practice of road safety impact assessment (RSIA) and network safety ranking (NSR), concluded that both procedures should ideally be based on accident prediction models (in addition, RSIA also requires accident modification factors). The process of developing accident prediction models (APMs) has been known from several international studies and applications; however, these usually did not cover complete road networks. In the example of motorways, the models need to consider all the elements, including road sections, various types of intersections, interchanges, etc. How this was done in case of Czech primary roads (motorways and national roads), is described in the present paper.

Since the idea of network safety ranking is to screen the entire road network, the sample should in theory comprise the whole population. However, the studied elements should be described by predictors, of which values are constant through the studied period. On the other hand, for the purpose of road safety impact assessment, accident prediction models may be based on a representative sample only.

Based on these requirements, following decision guided the definition of the studied samples:

Given the variety of Czech primary road network types, it was decided to distinguish motorways and national roads, as well as intersections (interchanges on motorways) and sections. Since RSIA also assesses impacts on adjacent road network, in addition to motorways and national roads, secondary roads were considered as well. At the same time, it was found that some categories were relatively rare: for example, 6-lane or 2 + 1 sections are not as frequent, as would be required for developing a reliable accident prediction model. The same held for secondary roads, sample size of which was limited by unavailability of AADT data.

The final 11 categories were the following:

In Table 1, the models are labelled as M01, M02, …, M11.

Regarding road sections, it is necessary to realize the consequences of various section lengths. In case of using homogeneity principle, i.e. keeping constant values of explanatory variables within a section, one may obtain too short sections [67]. In addition, for network ranking, it should be kept in mind that the follow-up on-site visits require manageable lengths (say, in units of kilometres). Therefore, the goal was to define logical section, while also trying to minimize differences within the sections.

On motorways, average length of sections between interchanges was 5 km. These sections were thus taken as motorway sections. Subsequently, each interchange was split into conflict points (merging, diverging, crossing, etc.) and sections between them (ramps). On national roads, sections were defined in following steps:

Note that data contained both divided and undivided roads. On divided sections, sections in two directions were defined independently. Undivided sections comprised both directions.

To develop accident prediction models, three basic datasets were needed (accident data, traffic data, road data), which will be described in the following paragraphs.

Accident prediction model equation consists of two parts: (1) exposure, i.e., AADT (and length, in case of sections), and (2) risk factors. In literature, various forms of considering road section risk exposure may be found, for example:

In a previous Czech study [4], several model function forms were compared in terms of proportion of explained systematic variation, and models with power function of section length proved better. Therefore, it was also chosen for this study. For the models of intersections or conflict points, there is a relative consensus in literature [57, 68, 72, 89]: exposure is usually defined as a product of conflicting flows or a product of major flow and proportion of minor flow. Model function forms adopted for this study, together with their linearized form, which will be used for modelling, were as follows:

in linearized form:

in linearized form:

where:

Using the described data, modelling was conducted with aim of developing accident prediction models for 11 categories (Table 1) and 8 severity levels (FAT, SEV, SLI, PDO, injury accidents, total accidents, plus combinations). The modelling, i.e. calibrating the mentioned function forms, entails estimating regression coefficients β_i and obtain model equations. Explanatory variables (x_i) were added stepwise, while controlling for achieved statistical significance and discarding non-significant variables. The process was performed using generalized linear modelling (GLM) procedure in IBM SPSS, with a negative binomial error structure and logarithmic link function.

However, it was found that modelling for categories of individual severity levels did not result in quality APMs, due to low frequencies of severe and fatal accidents. Therefore, consistently with Srinivasan and Bauer [75], alternative approach was taken:

This means, that APMs for individual severity levels of a specific category have different regression constants β₀, but uniform regression coefficients β₁, β₂, …

During modelling, effect of several explanatory variables was found insignificant, for example:

More information, as well as comparison with other studies, is provided in a previous paper [10]. The list of final model forms is given in Table 4. Specific values of regression parameters for each accident severity level are provided in the Appendix.

In addition to accident prediction models (APMs), also accident modification factors (AMFs) are required for conducting road safety impact assessments. However, as mentioned in the literature review, while AMFs are available internationally (especially in Nordic countries or Northern America), no comparable AMF studies for Czech condition exist. The exceptions were two before-after EB studies:

The results of both studies were therefore relatively comparable to the international findings. While more studies are definitely needed to reach definite conclusion about transferability possibilities, for the current purpose it was assumed that using international AMF values will be acceptable. The approximate values of AMFs for typical countermeasures were collated, based on international sources [26, 31, 46, 48, 58, 84] and used as a recommended list to be used for conducting RSIA.

The APMs and AMFs, described in previous text, present the background for conducting RSIA on Czech primary roads. To be useful for practitioners, two tools were provided by CDV:

The final predictions, summed over a selected route and transformed into accident costs, present the predicted costs of a contemplated project variant (for example, bypass). These may be compared to a do-nothing scenario (i.e., not building a bypass) in order to determine the expected safety change. Further comparison of these benefits to the project costs enables a simple cost-benefit analysis and economic evaluation.

The developed APMs were used to obtain predicted accident frequency (\( \widehat{N} \)) for each section (i). For the purpose of network safety ranking, Empirical Bayes estimate of expected accident frequency (EB) was then calculated, using predicted accident frequency, reported accident frequency (N) and length-dependent overdispersion parameter [43]. Finally potential for safety improvement (PSI) was obtained as a difference between predicted accident frequency and EB estimate [63]:

where:

Values of PSI were used for network screening and ranking. Given smaller sizes of 3-legs, 4-legs and roundabouts samples, they were combined into one group. Thus, descendent ranking enabled identifying hot spots in following five groups:

The ranked list of locations were visualized in an on-line map² and handed over to the road agency, which will use them to prioritize and perform necessary steps to improve safety of Czech primary road network. Illustration is provided in Fig. 2: user may select specific elements using filter, and study their location including visualization of input data and a link to Google Street View map.