Proceedings of 7th Transport Research Arena TRA 2018, April 16-19, 2018, Vienna, Austria On the efficient use of Road Safety Inspections on rural roads

Martin Winkelbauera,*, Sandra Schmiedb, Bernd Strnadb, Peter Trimmelb

aAustrian Road Safety Board (KFV), Schleiergasse 18, 1100 Vienna, Austria bKFV Sicherheit-Service GmbH, Schleiergasse 18, 1100 Vienna, Austria

Abstract

Road safety on the trans-European road network (TERN) is continuously assessed and improved under Directive 2008/96/EC of the European Parliament and of the Council on Road Infrastructure Safety Management by means of Road Safety Inspections (RSI), an effective intervention conducted by specifically trained and certified experts, who systematically scan existing roads for potential risks. For Austrian rural roads, a network 40 times the length of the Austrian part of the TERN, there is no such inspection obligation. Yet about 50 % of all road accidents in which people are injured occur on rural roads. Nonetheless, subjecting the complete rural road network to RSIs is neither necessary nor practicable. The objective of this research was therefore to develop and test theoretical methods for detecting and prioritizing sections of the road that would benefit most from such inspections. The findings (high-risk sections, mostly suitable for low-cost-measures) of one method were verified by an RSI and a comparison to low-risk sections.

Keywords: Accident Prevention; Traffic Survey; Accident Rate; Impact Study; High-Risk Sites; Risk Assessment

* Corresponding author. Tel.: +43-5-77077-1214; fax: +43-5-77077-1186. E-mail address: [email protected]

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1. Introduction, Background and Objectives

A Road Safety Inspection (RSI) is a systematic, periodic assessment of roads with regard to their safety properties and features and their current state (Nadler et al, 2014). Conducting a successful RSI requires well-educated and experienced staff (i.e. certified auditors), a defined procedure and, of course, a follow-up on the improvements proposed by the auditors. Measures typically proposed as a result of an RSI are predominantly low-cost improvements or cheap maintenance work. RSIs are mandatory on the trans-European road network (TERN) as laid down in Directive 2008/96/EC of the European Parliament and of the Council on Road Infrastructure Safety Management, the so-called Infrastructure Directive.

Applying RSIs to the rural road network (national and regional roads) is a stated objective in the Austrian Road Safety Programme 2011-2020 (bmvit, 2016). The national and regional road network in Austria is about 40 times longer than the Austrian part of the TERN. The cost of an RSI for one km is roughly 1,000 euros. Conducting RSIs on all roads would therefore exceed any budget provisions and also place an additional burden on the capacities of qualified staff. Furthermore, the need for improvement measures varies greatly. Considering these facts, a prioritization procedure for the carrying out of RSIs on the regional and national network is required.

Central to the research project described in this article was the hypothesis that significantly more potential improvements could be identified in an RSI on high-priority sections of road than on their low-priority counterparts. Accordingly, the project’s main objective was to develop a procedure that produces a reliable ranking of high-risk sections of road with strong potential for RSIs and to validate this hypothesis through practical tests in pilot areas. Conducting RSI is daily commercial business to some of the authors of this paper, they provided much of their expertise and experience in this research and during preparation of this paper.

2. Methodology

The project was carried out in three major phases (Fig. 1):

Fig. 1 Project phases

2.1. Assessment Parameters

The following data was used in the project: • Revised accident data 2012-2014 (accidents with injuries to persons, data corrections mostly encompass improvements in localization), including geodata (kilometrage and coordinates) • Infrastructure data from the nationwide transport graph (gip.gv.at) • Traffic volume data if available • Modified casualty cost rates (fatalities rated as severe injuries) • Aerial / satellite images (basemap.at)

The prioritization tool should cover all inter-urban roads (= all sections of the rural road network with the exception of those within residential areas). Because RSIs have proved to be especially effective when treating sites prone to single vehicle and run-off-road accidents (RVS 02.02.21, FSV – Österreichische Forschungsgesellschaft Straße- Schiene-Verkehr, 2014), the focus was placed on these particular types of accident. Additionally, all accidents in which people suffered injuries (“injury accidents”) were investigated.

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Accident costs monetarize both casualties and their injury levels as well as vehicle damage. In Austria, the respective values are periodically published by the Ministry of Transportation, Innovation and Technology. Currently, a fatality is about 3 M€, a severe injury 380 k€ and a slight injury about 30k€. It is common use to treat severe and fatal injuries equally at the value of severe injuries. The impact of a single fatal injury – due to its monetary value – would be so strong that it would largely cover the effects of non-fatal injuries. Further, it has to be considered that the actual outcome of a collision – severe or fatal –to a certain extent can be influenced by random external factors (e.g. vulnerability due to age and gender of the victims) and not just by road characteristics.

Several existing prioritization methods that are in use both nationally and internationally (e.g. ESN, EuroRAP, iRAP, ASFINAG-NSM) were compared. Several selection methods and potential key prioritization parameters were considered. According to the experience of the authors and relevant guidelines (Nadler et al, 2014), the relevant parameters hardly correlate. Hence, the choice of parameter(s) strongly influences the result. The typical solution for tasks like this is to calculate several parameters and include them all in a wholistic assessment. In this case, (1) accident density (accidents per kilometre) and (2) accident cost density (accident costs per kilometre) were chosen for prioritising road sections. Reasons for this decision will be explained in the following.

= (1) 𝑈𝑈𝑈𝑈𝑈𝑈

𝐴𝐴𝐴𝐴 𝐿𝐿∗𝑡𝑡 UPS = number of accidents with personal injuries L = road length in km t = period of time observed

= (2) 𝐾𝐾

𝐴𝐴𝐴𝐴𝐴𝐴 𝐿𝐿∗𝑡𝑡 K = accident costs L = road length in km t = period of time observed

The advantage of using accident density is its focus on sites with a high concentration of accidents, while the use of accident cost density places more emphasis on crash severity. Both parameters are strongly affected by section length, i.e. the shorter sections are, the more likely are high scores due to single (or a few) severe events.

Several other parameters that are based on traffic volumes (e.g. accident rate, accident cost rate) would also have been suitable – considering that the preventive potential of measures depends among other things on the number of recipients – but had to be rejected. Data on traffic volumes was not available for the whole network.

2.2. Method Design

Until recently, most data on crash location in Austria was based on road names and kilometrage (or house numbers). As of 2012, a new digital crash reporting system is used police forces. Among other improvements (less underreporting, accident cause and tentative responsible party), crash locations are now geo-reference, either by capturing coordinates right at the crash location or indicating the location on an interactive map. Locations of earlier (2011 and before) crashes were occasionally converted to coordinates, but this information is far from being complete and accurate enough to be used for this research.

After comprehensive discussion of potential methodologies among the experts taking part in this research, two approaches for calculating high-risk locations suitable for RSI were selected for further investigation: • an area-based approach • a section-based approach

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Both approaches were based on the same accident data, but calculated high-risk locations by different methods. The findings were then compared and the approaches evaluated. As explained above, two accident populations were of interest for the project: all accidents and single vehicle and run-off-road accidents. It was decided to examine both in parallel and compare the results.

2.2.1. Area-based approach

The area-based approach used GIS software and coordinate data to project accidents onto a map. The Austrian territory was divided into squares of equal size; each square was analysed individually. The approach resulted in maps depicting the accident density (accidents per total road length) and accident cost density (sum of accident costs per total road length) of each square. Accident density was chosen as the primary parameter for the area- based approach. After investigating various sizes of these squares, 10 by 10 km was chosen; mainly because the total length of roads contained therein could be inspected within one day by RSI. Grid size and map design were also meant to be easily understandable. A static country-wide grid (as opposed to a floating grid based on accident locations) was chosen to allow for future monitoring requirements.

The quality of available regional data differed and sometimes necessitated data corrections. For example, road data in one region had not been fully added to the nationwide transport graph. This led to some grid cells containing incomplete road data, resulting in some “blank cells” and a certain inaccuracy in these regions. Cases of accident data with incorrect area classifications are another such example. Coordinate data and satellite images were used to correct cases where accidents had obviously been wrongly assigned to either a residential or non-residential area. The following steps were used to create priority maps:

• Cells containing roads were identified. • The total road length per cell was calculated through GIS analysis. • Accident data was implemented into GIS using accident coordinate data. • The number of accidents per cell was calculated. • The accident cost density (sum of accident costs per total road length) and accident density (accidents per total road length) parameters were calculated for each cell. • Maps were generated for each accident population and for each density parameter.

2.2.2. Section-based approach

The section-based approach used kilometrage data to calculate sections with high accident density and with high accident cost density (with a focus on the latter). Rankings were calculated separately at national and regional level to account for structural differences between the regions and because the highest-risk sections of road in each region would be of interest for the authorities when deciding where best to carry out RSIs or implement other suitable road safety measures. There were several steps to the calculation of the ranked list:

• A section length of 3 kilometers was chosen as a target length for sections because an RSI would inspect not just the accident site but a longer road section. The kilometrage (x) of every accident was used to calculate a section with an upper (x+1.5 km) and a lower bound (x-1.5 km, i.e. to a minimum of km 0.001), sometimes resulting in the creation of sections shorter than 3 km. • Accident density and accident cost density were calculated for each 3-km section. • All inter-urban accidents within the top 5% in accident density, the top 5% in accident cost density or both were selected for further investigation, while the rest were excluded as selectable cases. • Overlapping inter-urban sections were aggregated. • Several parameters were calculated for each resulting section: the maximum accident density and accident cost density, the number of inter-urban accidents and the number of urban accidents. • Sections with only a few urban accidents were investigated in detail using one of the following two methods: comparing kilometrage with concurrent road/residential boundary lists or comparing geo- location with satellite/aerial images. Sections with accidents proving to be wrongly classified inter-urban accidents were corrected, while sections with actual urban accidents were rejected.

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• Prioritizing lists were created on a national and on a regional level, each following two parallel assessment schemes: including all accident types and including only single vehicle and run-off-road accidents. Sections found in both main units (identical or overlapping) were highlighted. The list was sorted by accident cost density in descending order. • Further details about each section were added to the priority lists of specific regions chosen as potential pilot areas.

3. Validation

The calculated results of both methodological approaches were evaluated and compared. One approach – the section-based approach – was chosen for further validation through a practical trial.

3.1. Theoretical evaluation of the methods

The results of both methods were analysed and compared for a conclusive decision on whether both were equally suitable or whether one method was clearly better suited and should be given preference over the other.

3.1.1. Validation of the area-based method

The area-based method was used to create colour-graded priority maps of Austria. The maps depicted the accident density of all cells containing relevant roads. The method was intended to give a clear indication which cells held the highest potential for an RSI. The size of the grid proved to have a major influence on the results. Using too small a cell size resulted in too many areas which appeared to be relevant, making a reasonable prioritization impossible. Using a large cell size led to cells containing road sections with high densities as well as many irrelevant road sections. Inspecting all roads within the cell would therefore be inefficient. The cell size of 10 by 10 km was initially assumed to be suitable for the objective.

Several graduation scales were tested prior to the decision to depict all densities while highlighting and further differentiating between the top 10% of the cells using a separate graduation scale (see Fig. 2). The decisive factor in this regard was analysability. As with the cell size, the graduation scale greatly affected the result in terms of proposed sites.

Fig. 2 Accident density in Austria (all accident types)

Advantages The main advantage of the area-based method lies in its independence from existing designation structures and changes therein (e.g. street designation or kilometrage point shifts). Its constant spatial frame (static grid) allows comparative analyses of the same spatial units over time. A benefit of the area-based method was that it produced presentable visualisations for stakeholder

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communication as part of the analysis. Disadvantages A disadvantage of the static grid is that it cannot take account of structural differences (rural, suburban, urban) and spatial anomalies. Once the positioning and size of the grid are defined, cross-cell accident hot spots and other spatial dependencies might remain undetected due to the focus on specific grid cells. The RSI focuses on streets. Road designations and kilometrage data – if available – must be extracted from the raw data, not from the results. Cells may contain irrelevant (safe) road sections, effectively concealing individual significance. Limits The method is limited by the current degree of data accuracy, especially with regard to coordinate position and location classification (urban vs. inter-urban location). Inaccurate data necessitates manual data verification and corrections. The chosen grid layout defines and restricts viable fields of application. Cases without coordinate data (missing data, historical data) and cases of missing road data cannot be analysed.

The significance of results depends on the cell size and the chosen density threshold. Despite testing different sizes and percentage values, no distinct suitable values for an effective prioritisation were found. The validity of the method could have been improved by adding a step to the process: by using a gradual cell analysis with different cell sizes changing for improvement determining the RSI potential per cell by adding an additional analysis of each cell could have improved its effectivity. However, this would have exceeded the scope of the project.

3.1.2. Validation of the section-based method

The section-based method is used to produce lists that rank the road sections with the greatest calculated potential for RSIs (see Fig. 3). It is especially suited for analysing verified data on roads with complete road designations.

Significant road sections (2012 - 2014) Lower Austria (sorted by ACD max) Top 5% Top 5% surrounding inter-urban accident location AD* ACD** nation province section accidents Notes population max max additional

STR from to length AD ACD AD ACD from to length all L85 13,834 18,25 4,416 0,56 444.710 7 ACD ACD 13,819 - Ende - k.A. südlich von Haag all B37 1,4 8,3 6,9 2,33 420.061 31 AD ACD AD ACD - - k.A. nördlich v. Krems (ehem. Asfinag-Strecke) (*) nicht in Liste Ortsbereiche NÖ all B7 36,18 44,1 7,92 1,89 393.315 28 ACD AD ACD 31,451 46,459 7,088 nördlich von Schrick in Verlängerung der A5 (und zuk. A5) (*) zukünftige A5 s/ror acc. B7 34,9 39,3 4,4 1,22 393.315 7 ACD ACD 31,451 46,459 10,608 nördlich von Schrick in Verlängerung der A5 (und zuk. A5) (*) zukünftige A5 s/ror acc. B21 55,7 63,85 8,15 1,44 367.004 22 AD ACD AD ACD 54,754 65,96 3,056 Motorradstrecke (Kalte Kuchl) all B21 58,875 63,2 4,325 1,44 367.004 16 ACD 54,754 65,96 6,881 Motorradstrecke (Kalte Kuchl) all B9 28,1 32,3 4,2 1,11 365.839 10 ACD 27,37 37,884 6,314 bei Petronell-Carnuntum all B4 3,1 6,1 3 0,67 363.508 6 ACD 0 34,132 31,132 Horner Straße zw. Stockerau und Großweikersdorf (*) nicht in Liste Ortsbereiche NÖ s/ror acc. B4 3,1 6,1 3 0,67 363.508 4 ACD ACD 0 34,132 31,132 Horner Straße zw. Stockerau und Großweikersdorf (*) nicht in Liste Ortsbereiche NÖ s/ror acc. B37 3,531 8,3 4,769 2,11 362.882 8 ACD ACD - - k.A. nördlich v. Krems (ehem. Asfinag-Strecke) (*) nicht in Liste Ortsbereiche NÖ all B121 7,6 12,55 4,95 1,56 349.689 21 AD ACD 0 13,323 8,373 Amstetten - Kematen all B18 46,55 50,78 4,23 1,22 330.011 13 ACD 45,46 52,102 2,412 Hainfeld all B19 27,9 32,35 4,45 1,78 326.397 18 AD ACD 14,8 34,03 14,78 Zw. Gaisruck und S5 all B21 6,1 10,412 4,312 1,67 325.814 19 AD ACD 0 16,051 11,739 zw. Piesting und A2 all B9 34,35 37,35 3 0,89 325.275 8 ACD 27,37 37,884 7,514 bei Bad Deutsch-Altenburg all L138 9,5 12,5 3 1,00 319.881 10 ACD 7,6 17,496 6,896 nördlich von Pernitz all B5 8,626 12,33 3,704 0,67 314.562 6 ACD 3,604 14,181 6,873 Östlich v. Waidhofen/Thaya all L148 10,8 13,8 3 1,22 311.499 10 ACD 10,45 17,444 3,994 Motorradstrecke (6 UPS Motorrad, Rosalia) s/ror acc. L148 11,5 15,72 4,22 1,22 281.648 9 ACD AD ACD 10,45 17,444 2,774 Motorradstrecke (6 UPS Motorrad, Rosalia) s/ror acc. B6 10,35 13,55 3,2 0,89 273.923 5 ACD 7,51 14,639 3,929 nördlich Korneuburg s/ror acc. B25 39,3 44,27 4,97 0,89 270.935 7 ACD ACD 39,045 - Ende - k.A. Erlauftalstraße (7 von 10: Motorrad) all B29 8,68 12,15 3,47 1,56 258.939 14 AD 8,697 12,5 0,333 Gegend von Mank, nordöstlich von Kilb (*) bis 8,697 OG Bischofstetten s/ror acc. B29 9 12,15 3,15 1,56 258.939 9 AD AD 8,697 12,5 0,653 Gegend von Mank, nordöstlich von Kilb all B20 20,36 23,94 3,58 1,67 238.053 17 AD 20,2 28,361 4,581 Mariazeller Straße (Traisental) s/ror acc. L133 3,54 6,8 3,26 1,00 235.108 8 ACD AD ACD 0,334 8,347 4,753 Motorradstrecke (bei Kleinzell, geht südlich zu Kalter Kuchl) s/ror acc. L135 7,825 12,05 4,225 1,11 232.702 9 ACD AD ACD 6,898 - Ende - k.A. Motorradstrecke (westlich von Reichenau/Rax) all L150 10,2 14,5 4,3 1,67 226.100 16 AD 9,805 - Ende - k.A. bei Moosbrunn/Gramatneusiedl all L112 1,675 4,7 3,025 1,53 221.363 14 AD 1,495 7,131 2,611 bei Langenrohr (Tulln) s/ror acc. B15 29 32 3 0,67 218.418 5 ACD 28,8 - Ende - k.A. Motorradstrecke (Leithagebirge) all B121 1,05 6,9 5,85 2,33 205.139 26 AD AD 0 13,323 7,473 Amstetten - Kematen s/ror acc. L137 20,75 23,75 3 0,78 194.544 7 ACD ACD 17,09 24,109 4,019 südlich Neunkirchen (2 Motorradunfälle) s/ror acc. B46 21,94 25,851 3,911 0,56 193.378 5 ACD ACD 21,838 30,318 4,569 bei Staatz (südl. Laa/Thaya) s/ror acc. B21 37,12 41,3 4,18 0,56 181.425 4 ACD 31,875 47,139 11,084 Motorradstrecke (Rohrer Sattel) s/ror acc. B71 13,677 16,7 3,023 0,56 175.449 5 ACD 7,625 18,001 7,353 Motorradstrecke (zw. Lunz und Mariazell) s/ror acc. L11 10,2 13,324 3,124 1,44 173.583 10 AD AD 9,566 13,358 0,668 Gänserndorfer Straße bei Markgrafneusiedl s/ror acc. B36 62,7 65,7 3 1,22 166.441 8 AD 62,875 67,379 1,504 nördlich v. Zwettl (*) bis 62,875 OG Zwettl all B19 20,33 25,37 5,04 2,11 155.611 20 AD AD 14,8 34,03 14,19 Tullner Straße bei Langenrohr all B9 5,1 8,1 3 1,67 154.519 17 AD 0 11,353 8,353 Bereich Flughafen Schwechat all B1 25,066 29,15 4,084 1,67 153.980 19 AD 23,474 29,693 2,135 Riederberg (nördlich Gablitz)

Significant sections 33 (bzw. 39) total length 139,674 km reference network length rd. 13.600 km Fig. 3 Priority list with detailed information for one region

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The section-based method was more successful than the area-based method, although it took some time to produce a useful definition of how a “section” should be defined. The calculation process itself also proved far more complex than the area-based method, due to the need for numerous interim calculations, plausibility checks and ad hoc data enhancement. In the early calculation steps, the use of MS EXCEL proved to be suboptimal because of the huge volume of data. A database software would therefore be recommended for future projects.

Advantages Cases without coordinate data (missing data, historical data) can be analysed if the designation systematics stay the same or changes are documented. Using a spreadsheet application with analysis features allows for easy data analysis through complex multifactorial data analysis and simple features (such as automatic formatting, filters and sorting), which can be used to discern distinctive characteristics. Disadvantages Roads without a designation cannot be analysed because they cannot be aggregated correctly. Changes in designation systematics (e.g. road classification, road designation or kilometrage point shifts) and spatial dependencies across multiple roads compromise calculations. Limits The method is limited by the current degree of data accuracy, especially with regard to kilometrage and location classification (urban vs. inter-urban location). Inaccurate data necessitates manual data verification and corrections.

3.1.3. Comparative evaluation

For comparison purposes, the calculation results for each accident population of both methods were overlaid on maps. While both method design approaches were based on the same basic data, some difference in results was to be expected because of the methodological specifics like target values and bounds. To better visualise the similarities and differences, the results were compared cell-wise using the cell definitions from the area-based method. The map creation (see Fig. 4) followed the following scheme: the area-based method resulted directly in nationwide maps depicting the accident density of each cell, therefore indicating regions with high potential. Each cell with a high accident density (within the top 10 %) was marked in blue in the comparison maps.

The section-based method produced section lists. Maps had to be created from these lists in an additional step. While both kilometrage and coordinate data had been used in the method calculation, the results of the section- based method were visualised based on the coordinates of the underlying accidents instead of performing a localisation based on kilometrage data and a road graph (linear referencing). This allowed a congruent accident depiction despite different calculation methods. Cells containing section-based results were coloured yellow. Cells containing results from both methods were coloured red. Cells with no results from either method were left blank.

Fig. 4 (a) Comparison of results (all accidents); (b) Comparison of results (single vehicle and run-off-road accidents)

The comparison results of the different accident populations differ greatly. While the analysis of the ‘all accidents’ population (see Fig. 4a) mostly contains cells notable due to section-based findings, the ‘single vehicle and run- off-road accidents’ population map (see Fig. 4b) contains a similar number of findings per method and shows a higher share of matching results. The area-based results regarding the ‘all accident’ population contain very few

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priority cells. This points to a heterogeneous distribution of accident density with few very high values. A detailed evaluation of compared cells (spatial distribution of relevant accidents among a cell’s roads) proved that the results of the area-based method could not serve by themselves as a final area selection for RSIs, but only as a basis for further assessment. Observed issues were:

• In some cells, accidents were distributed evenly among the roads. The actual accident density per road often differed from the value of the cell. • The fixed grid layout does not match the actual distribution and borders of accident density zones. Some high-potential zones are split up in two cells and aggregated with low-density zones. Resulting subdivisions of these high-potential zones are thus often undistinguishable because they are mixed up with irrelevant road sections (see Fig. 4 which shows a high accident density road section (depicted in dark red) overlapping a cell boundary. One part of the significant section is detected by the area-based method, while the other is not. The section-based method detects both areas).

Fig. 5 Detail from an in-depth evaluation of cells (cell colours: blue = area-based, yellow = section-based, red = both point data: green = accident; dark red = significant road section, depicted by aggregation of relevant accident sites)

3.1.4. Conclusions

The assessment led to the conclusion that while each method proved to have certain advantages, disadvantages, limits and therefore different fields of application, only the results of the section-based method should be subjected to the practical evaluation. The main reason for this was that only the section-based method provided a selection of specific sites suitable for an RSI. It accurately identified relevant, connected road sections with road and kilometrage data and further detailed information. It would also be easily evaluable. In its current state, the area- based method had to be rejected because the results by themselves did not provide usable outcomes: a follow-up analysis would have been necessary to determine suitable sites. It is, however, very likely that deeper research could lead to the development of useful area-based accident analysis methods. Possible approaches for further investigation include phased analysis with changing cell sizes, using a floating grid or a combination thereof.

3.2. Practical validation of the section-based calculation results

Following the conclusions of the theoretical evaluation the results of the section-based method were tested further. It was decided to conduct RSI at sites identified as most likely to benefit from RSI and to include a control site in the site list for further validation.

3.2.1. Methodology

RSIs were carried out on all these road sections using the standardised Austrian procedure defined in the official RSI manual issued by the Federal Ministry for Transport, Innovation and Technology (Nadler et al, 2014). The RSI process included a theoretical evaluation of the site with accident analyses, a site visit and discussions with the relevant stakeholders. The site was evaluated regarding accident risk and possible accident consequences (see Table 1). The combination of possible accident consequences and accident risk results in a colour-coded assessment of safety relevance which indicates the potential effect of measures on safety (no colour = irrelevant, yellow = low relevance, orange = medium relevance, red = high relevance).

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Following the evaluation, possible improvement measures were devised along with realistic implementation timeframes. The implementation and monitoring phase continued after the completion of this project and thus did not form part of this project. Table 1 Ranking the safety relevance (Nadler et al, 2014)

Austrian district authorities perform annual accident evaluations as part of their high-risk site (HRS) management. This process includes determining high-risk sites and developing measures to improve safety. It was to be expected that some sites would be found among both the high-priority sites for RSI and those targeted by HRS management. To take this into account, each section investigated in the practical validation phase was also checked for concurrences with HRS investigation results.

3.2.2. Site selection

The first step was to evaluate whether the detected high-risk sections were suitable for RSIs. For this purpose, six top-ranked road sections were selected for validation through an RSI. These sections were chosen from the national and a regional priority list: the three top-ranking sections in each list that met certain criteria were selected (see below). Three randomly selected low-ranking sections from the national priority list were used as control sites to create a baseline for the evaluation. Accordingly, nine sections of road in Styria and Upper Austria (two of which were also registered as HRS) were each subjected to an RSI. To ensure comparability, several criteria were specified:

• Accident location: inter-urban • Accident types: single vehicle and run-off-road accidents • Average daily traffic ≥ 3,000 vehicles per 24 hours • Section length: 4 km – 8 km • Sections with the highest accident cost density

3.2.3. Results

The control sites provided a baseline for comparison: Over the total control site length of about 18 km, the RSIs only resulted in the recommendation of one measure. In the case of the selected high-priority sites, the RSI produced significantly more recommended measures. About a quarter of these were high safety relevance measures, while around 35% were medium safety relevance measures. The measure density (measures per km) for high-priority sites in comparison to control sites differed by a ratio of 15:1. The sections that also contained HRS

Fig. 6 Crash density and crash cost density by site type

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usually had the highest accident density and accident cost density values and always necessitated measures (see Fig. 5). With regard to total road length and measures per type of site, the results show that about 99% of the total length of all control site segments did not elicit specific measures, while this share was only 78 % for priority sites. Measures were recommended for 22% of the total priority site length (4.6% on HRS sites). With regard to accidents in priority sites, 27% related to HRS, 32% to other priority sites and 41% to sites without measures. Only 6% of the control sites were accident sites.

3.2.4. Conclusions

The results indicate that the prioritisation tool performs very well. The comparison with control sites shows that the method detects sections that are well suited for conducting RSIs. By realising the recommended interventions, major sections of the road network with high accident densities could be improved. While the prioritisation tool successfully located many HRS sites, its main benefit lies in its ability to locate longer road segments that are not detected by HRS calculations and are therefore not evaluated on a regular basis. It should be noted in this regard that HRS management plays an important role in reducing the incidences of accidents. Yet HRS management has a specific field of application: most HRS are of a short length that includes the corresponding findings of the tool. Hence, the strong effect of the factor ‘length’ on the chosen density parameters must be taken into consideration.

For sections identified as high priority by the prioritisation tool, 60% of the accidents occur on sections with interventions recommended by the inspecting expert. Hence, after implementation of all potential improvements, 94% of the accidents on the baseline sections would still be not addressed, compared to only 40% on the target sections. On the basis of the accidents addressed, the prioritisation tool would therefore increase the efficiency of the RSI by a factor of 2.5.

In terms of kilometres covered and inspected, only 1.2% of the kilometres covered in the baseline section were “successful” kilometres, i.e. the inspection of 98.8% of the kilometres would not detect any necessary measures. In situ inspections of the priority sections would still cover kilometres that did not result in proposed measures, but the hit rate in this case increases dramatically to 22.4%, i.e. goes up by a factor of almost 20.

4. Summary

Of the two prioritisation methods that were designed, one – the area-based approach – had to be rejected during the theoretical evaluation of the calculation results. The section-based approach showed promising results and was therefore tested further in a practical evaluation. The sections evaluated by an RSI indicated that suitable sections had been chosen. The analysis of the results led to the conclusion that the developed section-based prioritisation method provided an effective and efficient way of selecting and prioritising road sections in preparation for RSIs on rural roads.

5. References

Nadler, B., Nadler, F., Strnad, B.: (Austrian Federal Ministry of Transport, Innovation and Technology), 2014. Road Safety Inspections (RSI); Manual for Conducting RSI. Vienna. bmvit (Austrian Federal Ministry of Transport, Innovation and Technology), 2016. Austrian Road Safety Programme 2011-2020, 2nd Edition 2016, p. 106. FGSV - Forschungsgesellschaft für Straßen- und Verkehrswesen e.V., 2003. Empfehlungen für die Sicherheitsanalyse von Straßennetzen – ESN [Recommendations for the Safety Analysis of Road Networks – ESN]. Cologne. FSV - Österreichische Forschungsgesellschaft Straße-Schiene-Verkehr, 2014. RVS 02.02.21 – Verkehrssicherheitsuntersuchung [Road Safety Inspection]. Vienna. KFV Sicherheit-Service GmbH, 2011. Network Safety Management am Asfinag-Netz [Network Safety Management in the ASFINAG Road Network]. Vienna. Macaulay, J., McInerney, R., 2002. Evaluation of the proposed actions emanating from Road Safety Audits. Austroads Inc.. Sydney. Sedlacek, N., Herry, M., Pumberger, A., Schwaighofer, P., Kummer, S., Riebesmeier, B., 2012. Unfallkostenrechnung Straße 2012 [Accident Cost Calcuation]. Forschungsarbeiten des österreichischen Verkehrssicherheitsfonds, Band 016, 2012, 72 p. Winkelbauer, M., 2006. Economic Evaluation of Road Safety Programmes with emphasis on Road Infrastructure Safety Management. High Level Expert Meeting on “Infrastructure Safety”, Vienna. Winkelbauer, M., 2017. Verkehrsunfallprävention durch abschnittsbezogene Geschwindigkeitsüberwachung (Section Control) [Road Accident Prevention through Section Control], Deutscher Präventionstag, Hannover.

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