On the Efficient Use of Road Safety Inspections on Rural Roads
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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] 1 Winkelbauer, M. / TRA2018, Vienna, Austria, April 16-19, 2018 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. 2 Winkelbauer, M. / TRA2018, Vienna, Austria, April 16-19, 2018 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) ∗ UPS = number of accidents with personal injuries L = road length in km t = period of time observed = (2) ∗ 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 3 Winkelbauer, M. / TRA2018, Vienna, Austria, April 16-19, 2018 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.